2022-04-18 12:59:58 [32mINFO[m i.a.w.w.WorkerRun(call):49 - Executing worker wrapper. Airbyte version: 0.35.30-alpha
2022-04-18 12:59:59 [32mINFO[m i.a.w.t.TemporalAttemptExecution(get):105 - Docker volume job log path: /tmp/workspace/16/1/logs.log
2022-04-18 12:59:59 [32mINFO[m i.a.w.t.TemporalAttemptExecution(get):110 - Executing worker wrapper. Airbyte version: 0.35.30-alpha
2022-04-18 13:00:01 [32mINFO[m i.a.w.DefaultReplicationWorker(run):103 - start sync worker. job id: 16 attempt id: 1
2022-04-18 13:00:01 [32mINFO[m i.a.w.DefaultReplicationWorker(run):115 - configured sync modes: {null.tickets=incremental - append_dedup, null.sla_policies=full_refresh - append, null.brands=full_refresh - append, null.ticket_fields=incremental - append_dedup, null.ticket_metric_events=incremental - append_dedup, null.ticket_metrics=incremental - append_dedup, null.tags=full_refresh - append}
2022-04-18 13:00:01 [32mINFO[m i.a.w.p.a.DefaultAirbyteDestination(start):69 - Running destination...
2022-04-18 13:00:02 [32mINFO[m i.a.c.i.LineGobbler(voidCall):82 - Checking if airbyte/destination-redshift:0.3.28 exists...
2022-04-18 13:00:03 [32mINFO[m i.a.c.i.LineGobbler(voidCall):82 - airbyte/destination-redshift:0.3.28 was found locally.
2022-04-18 13:00:03 [32mINFO[m i.a.w.p.DockerProcessFactory(create):157 - Preparing command: docker run --rm --init -i -w /data/16/1 --log-driver none --network host -v airbyte_workspace:/data -v /tmp/airbyte_local:/local airbyte/destination-redshift:0.3.28 write --config destination_config.json --catalog destination_catalog.json
2022-04-18 13:00:03 [32mINFO[m i.a.c.i.LineGobbler(voidCall):82 - Checking if airbyte/source-zendesk-support:0.2.5 exists...
2022-04-18 13:00:04 [32mINFO[m i.a.w.p.DockerProcessFactory(create):157 - Preparing command: docker run --rm --init -i -w /data/16/1 --log-driver none --network host -v airbyte_workspace:/data -v /tmp/airbyte_local:/local airbyte/source-zendesk-support:0.2.5 read --config source_config.json --catalog source_catalog.json --state input_state.json
2022-04-18 13:00:04 [32mINFO[m i.a.c.i.LineGobbler(voidCall):82 - airbyte/source-zendesk-support:0.2.5 was found locally.
2022-04-18 13:00:04 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getDestinationOutputRunnable$6):337 - Destination output thread started.
2022-04-18 13:00:04 [32mINFO[m i.a.w.DefaultReplicationWorker(run):157 - Waiting for source and destination threads to complete.
2022-04-18 13:00:04 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):278 - Replication thread started.
2022-04-18 13:00:08 [43mdestination[0m > SLF4J: Class path contains multiple SLF4J bindings.
2022-04-18 13:00:08 [43mdestination[0m > SLF4J: Found binding in [jar:file:/airbyte/lib/log4j-slf4j-impl-2.17.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]
2022-04-18 13:00:08 [43mdestination[0m > SLF4J: Found binding in [jar:file:/airbyte/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
2022-04-18 13:00:08 [43mdestination[0m > SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
2022-04-18 13:00:09 [43mdestination[0m > SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
2022-04-18 13:00:10 [44msource[0m > Starting syncing SourceZendeskSupport
2022-04-18 13:00:10 [44msource[0m > Syncing stream: brands 
2022-04-18 13:00:10 [44msource[0m > Read 12 records from brands stream
2022-04-18 13:00:10 [44msource[0m > Finished syncing brands
2022-04-18 13:00:10 [44msource[0m > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.351198
2022-04-18 13:00:10 [44msource[0m > Syncing stream: sla_policies 
2022-04-18 13:00:11 [44msource[0m > Read 11 records from sla_policies stream
2022-04-18 13:00:11 [44msource[0m > Finished syncing sla_policies
2022-04-18 13:00:11 [44msource[0m > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.351198
Syncing stream sla_policies 0:00:00.200781
2022-04-18 13:00:11 [44msource[0m > Syncing stream: tags 
2022-04-18 13:00:11 [44msource[0m > Read 196 records from tags stream
2022-04-18 13:00:11 [44msource[0m > Finished syncing tags
2022-04-18 13:00:11 [44msource[0m > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.351198
Syncing stream sla_policies 0:00:00.200781
Syncing stream tags 0:00:00.332287
2022-04-18 13:00:11 [44msource[0m > Syncing stream: ticket_fields 
2022-04-18 13:00:11 [44msource[0m > Read 14 records from ticket_fields stream
2022-04-18 13:00:11 [44msource[0m > Finished syncing ticket_fields
2022-04-18 13:00:11 [44msource[0m > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.351198
Syncing stream sla_policies 0:00:00.200781
Syncing stream tags 0:00:00.332287
Syncing stream ticket_fields 0:00:00.344706
2022-04-18 13:00:11 [44msource[0m > Syncing stream: ticket_metric_events 
2022-04-18 13:00:12 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 1000
2022-04-18 13:00:13 [43mdestination[0m > 2022-04-18 13:00:13 [32mINFO[m i.a.i.d.r.RedshiftDestination(main):77 - starting destination: class io.airbyte.integrations.destination.redshift.RedshiftDestination
2022-04-18 13:00:14 [43mdestination[0m > 2022-04-18 13:00:14 [32mINFO[m i.a.i.b.IntegrationCliParser(parseOptions):118 - integration args: {catalog=destination_catalog.json, write=null, config=destination_config.json}
2022-04-18 13:00:14 [43mdestination[0m > 2022-04-18 13:00:14 [32mINFO[m i.a.i.b.IntegrationRunner(runInternal):121 - Running integration: io.airbyte.integrations.destination.redshift.RedshiftDestination
2022-04-18 13:00:14 [43mdestination[0m > 2022-04-18 13:00:14 [32mINFO[m i.a.i.b.IntegrationRunner(runInternal):122 - Command: WRITE
2022-04-18 13:00:14 [43mdestination[0m > 2022-04-18 13:00:14 [32mINFO[m i.a.i.b.IntegrationRunner(runInternal):123 - Integration config: IntegrationConfig{command=WRITE, configPath='destination_config.json', catalogPath='destination_catalog.json', statePath='null'}
2022-04-18 13:00:15 [43mdestination[0m > 2022-04-18 13:00:15 [33mWARN[m c.n.s.JsonMetaSchema(newValidator):338 - Unknown keyword examples - you should define your own Meta Schema. If the keyword is irrelevant for validation, just use a NonValidationKeyword
2022-04-18 13:00:15 [43mdestination[0m > 2022-04-18 13:00:15 [33mWARN[m c.n.s.JsonMetaSchema(newValidator):338 - Unknown keyword airbyte_secret - you should define your own Meta Schema. If the keyword is irrelevant for validation, just use a NonValidationKeyword
2022-04-18 13:00:15 [43mdestination[0m > 2022-04-18 13:00:15 [32mINFO[m i.a.i.d.j.c.SwitchingDestination(getConsumer):65 - Using destination type: COPY_S3
2022-04-18 13:00:16 [43mdestination[0m > 2022-04-18 13:00:16 [32mINFO[m i.a.i.d.s.S3DestinationConfig(createS3Client):169 - Creating S3 client...
2022-04-18 13:00:19 [43mdestination[0m > 2022-04-18 13:00:19 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(startTracked):141 - class io.airbyte.integrations.destination.buffered_stream_consumer.BufferedStreamConsumer started.
2022-04-18 13:00:19 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 2000
2022-04-18 13:00:20 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 3000
2022-04-18 13:00:20 [44msource[0m > Read 3488 records from ticket_metric_events stream
2022-04-18 13:00:20 [44msource[0m > Finished syncing ticket_metric_events
2022-04-18 13:00:20 [44msource[0m > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.351198
Syncing stream sla_policies 0:00:00.200781
Syncing stream tags 0:00:00.332287
Syncing stream ticket_fields 0:00:00.344706
Syncing stream ticket_metric_events 0:00:02.627175
2022-04-18 13:00:20 [44msource[0m > Syncing stream: ticket_metrics 
2022-04-18 13:00:20 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 4000
2022-04-18 13:00:22 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 5000
2022-04-18 13:00:24 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 6000
2022-04-18 13:00:26 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 7000
2022-04-18 13:00:33 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 8000
2022-04-18 13:00:39 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 9000
2022-04-18 13:00:40 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 10000
2022-04-18 13:00:40 [43mdestination[0m > 2022-04-18 13:00:40 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(flushQueueToDestination):181 - Flushing buffer: 26212902 bytes
2022-04-18 13:00:40 [43mdestination[0m > 2022-04-18 13:00:40 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing brands: 12 records
2022-04-18 13:00:40 [43mdestination[0m > 2022-04-18 13:00:40 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-18 13:00:40 [43mdestination[0m > 2022-04-18 13:00:40 [32mINFO[m i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'brands': s3://travlrdatatesting/airbyte_data/brands/2022_04_18_1650286818677_4a4adfc7-88bf-41c6-974d-c5533a342143.csv
2022-04-18 13:00:40 [43mdestination[0m > 2022-04-18 13:00:40 [32mINFO[m i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/brands/2022_04_18_1650286818677_4a4adfc7-88bf-41c6-974d-c5533a342143.csv with full ID udq.xY22lE.A63SNSXnKBSd6NBEmNSwCDkawQIvq58X6qG6dzWiQnL9ARyHWwDMNQNXItEkSpvzGwJ6l3kBcv_0aBoAmiYM1v_1AVvemtonQKbXiRxUieITa_tDPXj8_xwu3OIvNG44IUv.3hk55Tw--
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing sla_policies: 11 records
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'sla_policies': s3://travlrdatatesting/airbyte_data/sla_policies/2022_04_18_1650286818693_7a689e7d-43a1-4f4f-aa9c-1687423fc517.csv
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/sla_policies/2022_04_18_1650286818693_7a689e7d-43a1-4f4f-aa9c-1687423fc517.csv with full ID Z_HqeN0nc9pe3YvCVuHW1Hr_toOTBG9xXgVZ_FDTsddQb262GDSwydbiygE4N.gEIo1Fo8sQl5u.fy7wMIJWnJkXpzOwLEQPOcIAuKxCCMeES22vXgFTvTqCveeXaEL.wH1XG5RyXgdw1fsr6_T92g--
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_metrics: 6562 records
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'ticket_metrics': s3://travlrdatatesting/airbyte_data/ticket_metrics/2022_04_18_1650286818694_9b4110e3-d230-4858-ba1e-a80f13496a21.csv
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-18 13:00:41 [43mdestination[0m > 2022-04-18 13:00:41 [32mINFO[m a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/ticket_metrics/2022_04_18_1650286818694_9b4110e3-d230-4858-ba1e-a80f13496a21.csv with full ID I_Ura3NPvDKSEweY2pZjarE74eker41yT1Cs70Iys0KccVb0PhOsERXilIRhYG92YsJ.nwBSpDoHhL1uoFeXk3Nxn6YiuWMXwH0s6zpk6hPOShSxisMcPXzMtJNcfqpNvoE7dxfJzGg0sJr2ezlIgw--
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_metric_events: 3488 records
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'ticket_metric_events': s3://travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_18_1650286818694_2be5728a-df0a-4c82-a4d6-8550c08f5f9e.csv
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_18_1650286818694_2be5728a-df0a-4c82-a4d6-8550c08f5f9e.csv with full ID FW.gmxcJyVRGMzLhROWWEPkcc4Uk7Hg6L91ttJMne4fUcZSj8o0yN_6FWhFHqCaWyEmY829913piLlRoZc2BdQI08CO064.jmWdNl2TelcGzb6JXawacPWU8TuBQq8L7tLAmnlWgSWr1goLbK8hw7Q--
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing tags: 196 records
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'tags': s3://travlrdatatesting/airbyte_data/tags/2022_04_18_1650286818693_9bfc358e-8881-44ab-9b43-72e361b69b9b.csv
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/tags/2022_04_18_1650286818693_9bfc358e-8881-44ab-9b43-72e361b69b9b.csv with full ID IaLGcbfe1BoC9LCYwI5_MJwdxuo.ZzY8JZnNjpOtnVC90VSstIT6YyfVMQ_wzROWHzTN6LOYOkkyrRG.oGGcYVClO8fR9nxfaVckXDM6qmPHed1ffRd.Od5gu7BUqwhYOxODEJF2YRlwe1ufKufU_A--
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_fields: 14 records
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'ticket_fields': s3://travlrdatatesting/airbyte_data/ticket_fields/2022_04_18_1650286818693_6d5cdb45-d0ae-44d1-bb50-5debba38c9d4.csv
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-18 13:00:42 [43mdestination[0m > 2022-04-18 13:00:42 [32mINFO[m a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/ticket_fields/2022_04_18_1650286818693_6d5cdb45-d0ae-44d1-bb50-5debba38c9d4.csv with full ID zoaTpbWHqpZHLE87NAfdW364S_spN5YMwAWwY0WCoAEnWFds4lgij3s_leJ0HzFFhcRRuu23Ow4AKq6Wj3w_hxwEGrQT3kkWaugVu.Z6w.PIMNPnfnzwD_ytYPGEPv8fxv5RkWibyy08WBL4uHbkPA--
2022-04-18 13:00:43 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 11000
2022-04-18 13:00:43 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 12000
2022-04-18 13:00:44 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 13000
2022-04-18 13:00:46 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 14000
2022-04-18 13:00:48 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 15000
2022-04-18 13:00:49 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 16000
2022-04-18 13:00:50 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 17000
2022-04-18 13:00:51 [43mdestination[0m > 2022-04-18 13:00:51 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(flushQueueToDestination):181 - Flushing buffer: 26213004 bytes
2022-04-18 13:00:51 [43mdestination[0m > 2022-04-18 13:00:51 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_metrics: 7196 records
2022-04-18 13:00:51 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 18000
2022-04-18 15:54:55 [44msource[0m > Read 14777 records from ticket_metrics stream
2022-04-18 15:54:55 [44msource[0m > Finished syncing ticket_metrics
2022-04-18 15:54:55 [44msource[0m > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.351198
Syncing stream sla_policies 0:00:00.200781
Syncing stream tags 0:00:00.332287
Syncing stream ticket_fields 0:00:00.344706
Syncing stream ticket_metric_events 0:00:02.627175
Syncing stream ticket_metrics 2:54:41.352076
2022-04-18 15:54:55 [44msource[0m > Syncing stream: tickets 
2022-04-18 15:55:00 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 19000
2022-04-18 15:55:01 [44msource[0m > Read 512 records from tickets stream
2022-04-18 15:55:01 [44msource[0m > Finished syncing tickets
2022-04-18 15:55:01 [44msource[0m > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.351198
Syncing stream sla_policies 0:00:00.200781
Syncing stream tags 0:00:00.332287
Syncing stream ticket_fields 0:00:00.344706
Syncing stream ticket_metric_events 0:00:02.627175
Syncing stream ticket_metrics 2:54:41.352076
Syncing stream tickets 0:00:05.330240
2022-04-18 15:55:01 [44msource[0m > Finished syncing SourceZendeskSupport
2022-04-18 15:55:02 [32mINFO[m i.a.w.DefaultReplicationWorker(run):162 - One of source or destination thread complete. Waiting on the other.
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):229 - The main thread is exiting while children non-daemon threads from a connector are still active.
2022-04-18 15:55:02 [43mdestination[0m > Ideally, this situation should not happen...
2022-04-18 15:55:02 [43mdestination[0m > Please check with maintainers if the connector or library code should safely clean up its threads before quitting instead.
2022-04-18 15:55:02 [43mdestination[0m > The main thread is: main (RUNNABLE)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: java.base/java.lang.Thread.getStackTrace(Thread.java:1610)
2022-04-18 15:55:02 [43mdestination[0m >         at io.airbyte.integrations.base.IntegrationRunner.dumpThread(IntegrationRunner.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at io.airbyte.integrations.base.IntegrationRunner.watchForOrphanThreads(IntegrationRunner.java:233)
2022-04-18 15:55:02 [43mdestination[0m >         at io.airbyte.integrations.base.IntegrationRunner.runConsumer(IntegrationRunner.java:190)
2022-04-18 15:55:02 [43mdestination[0m >         at io.airbyte.integrations.base.IntegrationRunner.lambda$runInternal$1(IntegrationRunner.java:163)
2022-04-18 15:55:02 [43mdestination[0m >         at io.airbyte.integrations.base.sentry.AirbyteSentry.executeWithTracing(AirbyteSentry.java:54)
2022-04-18 15:55:02 [43mdestination[0m >         at io.airbyte.integrations.base.sentry.AirbyteSentry.executeWithTracing(AirbyteSentry.java:38)
2022-04-18 15:55:02 [43mdestination[0m >         at io.airbyte.integrations.base.IntegrationRunner.runInternal(IntegrationRunner.java:163)
2022-04-18 15:55:02 [43mdestination[0m >         at io.airbyte.integrations.base.IntegrationRunner.run(IntegrationRunner.java:105)
2022-04-18 15:55:02 [43mdestination[0m >         at io.airbyte.integrations.destination.redshift.RedshiftDestination.main(RedshiftDestination.java:78)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-1 (WAITING)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-2 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-3 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-4 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-5 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-6 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-7 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-8 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-9 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-10 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-1 (WAITING)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-2 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-3 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-4 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-5 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-6 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-7 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-8 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-9 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-10 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-1 (WAITING)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-2 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-3 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-4 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-5 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-6 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-7 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-8 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-9 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-10 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-1 (WAITING)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-2 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-3 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-4 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-5 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-6 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-7 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-8 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-9 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-10 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-1 (WAITING)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-2 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-3 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-4 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-5 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-6 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-7 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-8 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-9 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-10 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-1 (WAITING)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-2 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-3 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-4 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-5 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-6 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-7 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-8 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-9 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [33mWARN[m i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-10 (BLOCKED)
2022-04-18 15:55:02 [43mdestination[0m >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-18 15:55:02 [43mdestination[0m >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-18 15:55:02 [43mdestination[0m >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [32mINFO[m i.a.i.b.FailureTrackingAirbyteMessageConsumer(close):65 - Airbyte message consumer: succeeded.
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(close):217 - executing on success close procedure.
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(flushQueueToDestination):181 - Flushing buffer: 7951051 bytes
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing tickets: 512 records
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [32mINFO[m i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'tickets': s3://travlrdatatesting/airbyte_data/tickets/2022_04_18_1650286818694_f3095ae6-59ee-4c89-ac9c-3f32ee9fd572.csv
2022-04-18 15:55:02 [43mdestination[0m > 2022-04-18 15:55:02 [32mINFO[m i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-18 15:55:03 [43mdestination[0m > 2022-04-18 15:55:03 [32mINFO[m a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/tickets/2022_04_18_1650286818694_f3095ae6-59ee-4c89-ac9c-3f32ee9fd572.csv with full ID fAnO57wkkb..._vKT3fABCDTzo0gJtafBz_knrNI8l1mSqcqGGxzDDfvAQWV8DZeD6VOQlYMrr1WVbIdcBPZL5Rt2uVC748Y28hxy3AmbhDLTCtPze73BsmwhduRYOqJ6hE5O3744xy5krRpGYNqIA--
2022-04-18 15:55:05 [43mdestination[0m > 2022-04-18 15:55:05 [32mINFO[m i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_metrics: 1019 records
2022-04-18 15:55:05 [43mdestination[0m > 2022-04-18 15:55:05 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'brands'.
2022-04-18 15:55:05 [43mdestination[0m > 2022-04-18 15:55:05 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:05 [43mdestination[0m > 2022-04-18 15:55:05 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:05 [43mdestination[0m > 2022-04-18 15:55:05 [33mWARN[m a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-18 15:55:05 [43mdestination[0m > 2022-04-18 15:55:05 [32mINFO[m a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/brands/2022_04_18_1650286818677_4a4adfc7-88bf-41c6-974d-c5533a342143.csv with id udq.xY22l...3hk55Tw--]: Uploading leftover stream [Part number 1 containing 0.02 MB]
2022-04-18 15:55:06 [43mdestination[0m > 2022-04-18 15:55:06 [32mINFO[m a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/brands/2022_04_18_1650286818677_4a4adfc7-88bf-41c6-974d-c5533a342143.csv with id udq.xY22l...3hk55Tw--]: Finished uploading [Part number 1 containing 0.02 MB]
2022-04-18 15:55:06 [43mdestination[0m > 2022-04-18 15:55:06 [32mINFO[m a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/brands/2022_04_18_1650286818677_4a4adfc7-88bf-41c6-974d-c5533a342143.csv with id udq.xY22l...3hk55Tw--]: Completed
2022-04-18 15:55:06 [43mdestination[0m > 2022-04-18 15:55:06 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'brands'.
2022-04-18 15:55:06 [43mdestination[0m > 2022-04-18 15:55:06 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk_intercom
2022-04-18 15:55:08 [43mdestination[0m > 2022-04-18 15:55:08 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: brands, schema: zendesk_intercom, tmp table name: _airbyte_tmp_ggk_brands.
2022-04-18 15:55:08 [43mdestination[0m > 2022-04-18 15:55:08 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_ggk_brands in destination for stream: brands, schema: zendesk_intercom, .
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_ggk_brands in destination for stream brands complete.
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_brands in destination.
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_ggk_brands in destination prepared.
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_ggk_brands to dest table: _airbyte_raw_brands, schema: zendesk_intercom, in destination.
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'sla_policies'.
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [33mWARN[m a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/sla_policies/2022_04_18_1650286818693_7a689e7d-43a1-4f4f-aa9c-1687423fc517.csv with id Z_HqeN0nc...r6_T92g--]: Uploading leftover stream [Part number 1 containing 0.03 MB]
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/sla_policies/2022_04_18_1650286818693_7a689e7d-43a1-4f4f-aa9c-1687423fc517.csv with id Z_HqeN0nc...r6_T92g--]: Finished uploading [Part number 1 containing 0.03 MB]
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/sla_policies/2022_04_18_1650286818693_7a689e7d-43a1-4f4f-aa9c-1687423fc517.csv with id Z_HqeN0nc...r6_T92g--]: Completed
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'sla_policies'.
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk_intercom
2022-04-18 15:55:09 [43mdestination[0m > 2022-04-18 15:55:09 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: sla_policies, schema: zendesk_intercom, tmp table name: _airbyte_tmp_dsu_sla_policies.
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_dsu_sla_policies in destination for stream: sla_policies, schema: zendesk_intercom, .
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_dsu_sla_policies in destination for stream sla_policies complete.
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_sla_policies in destination.
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_dsu_sla_policies in destination prepared.
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_dsu_sla_policies to dest table: _airbyte_raw_sla_policies, schema: zendesk_intercom, in destination.
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'tickets'.
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [33mWARN[m a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/tickets/2022_04_18_1650286818694_f3095ae6-59ee-4c89-ac9c-3f32ee9fd572.csv with id fAnO57wkk...pGYNqIA--]: Uploading leftover stream [Part number 1 containing 1.30 MB]
2022-04-18 15:55:10 [43mdestination[0m > 2022-04-18 15:55:10 [32mINFO[m a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/tickets/2022_04_18_1650286818694_f3095ae6-59ee-4c89-ac9c-3f32ee9fd572.csv with id fAnO57wkk...pGYNqIA--]: Finished uploading [Part number 1 containing 1.30 MB]
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/tickets/2022_04_18_1650286818694_f3095ae6-59ee-4c89-ac9c-3f32ee9fd572.csv with id fAnO57wkk...pGYNqIA--]: Completed
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'tickets'.
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk_intercom
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: tickets, schema: zendesk_intercom, tmp table name: _airbyte_tmp_xzp_tickets.
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_xzp_tickets in destination for stream: tickets, schema: zendesk_intercom, .
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_xzp_tickets in destination for stream tickets complete.
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_tickets in destination.
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_xzp_tickets in destination prepared.
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_xzp_tickets to dest table: _airbyte_raw_tickets, schema: zendesk_intercom, in destination.
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'ticket_metric_events'.
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [33mWARN[m a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_18_1650286818694_2be5728a-df0a-4c82-a4d6-8550c08f5f9e.csv with id FW.gmxcJy...bK8hw7Q--]: Uploading leftover stream [Part number 1 containing 0.77 MB]
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_18_1650286818694_2be5728a-df0a-4c82-a4d6-8550c08f5f9e.csv with id FW.gmxcJy...bK8hw7Q--]: Finished uploading [Part number 1 containing 0.77 MB]
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_18_1650286818694_2be5728a-df0a-4c82-a4d6-8550c08f5f9e.csv with id FW.gmxcJy...bK8hw7Q--]: Completed
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'ticket_metric_events'.
2022-04-18 15:55:11 [43mdestination[0m > 2022-04-18 15:55:11 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk_intercom
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: ticket_metric_events, schema: zendesk_intercom, tmp table name: _airbyte_tmp_auz_ticket_metric_events.
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_auz_ticket_metric_events in destination for stream: ticket_metric_events, schema: zendesk_intercom, .
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_auz_ticket_metric_events in destination for stream ticket_metric_events complete.
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_ticket_metric_events in destination.
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_auz_ticket_metric_events in destination prepared.
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_auz_ticket_metric_events to dest table: _airbyte_raw_ticket_metric_events, schema: zendesk_intercom, in destination.
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'ticket_metrics'.
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:12 [43mdestination[0m > 2022-04-18 15:55:12 [33mWARN[m a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-18 15:55:13 [43mdestination[0m > 2022-04-18 15:55:13 [32mINFO[m a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metrics/2022_04_18_1650286818694_9b4110e3-d230-4858-ba1e-a80f13496a21.csv with id I_Ura3NPv...2ezlIgw--]: Finished uploading [Part number 1 containing 14.99 MB]
2022-04-18 15:55:13 [43mdestination[0m > 2022-04-18 15:55:13 [32mINFO[m a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metrics/2022_04_18_1650286818694_9b4110e3-d230-4858-ba1e-a80f13496a21.csv with id I_Ura3NPv...2ezlIgw--]: Completed
2022-04-18 15:55:13 [43mdestination[0m > 2022-04-18 15:55:13 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'ticket_metrics'.
2022-04-18 15:55:13 [43mdestination[0m > 2022-04-18 15:55:13 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk_intercom
2022-04-18 15:55:13 [43mdestination[0m > 2022-04-18 15:55:13 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: ticket_metrics, schema: zendesk_intercom, tmp table name: _airbyte_tmp_kbf_ticket_metrics.
2022-04-18 15:55:13 [43mdestination[0m > 2022-04-18 15:55:13 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_kbf_ticket_metrics in destination for stream: ticket_metrics, schema: zendesk_intercom, .
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_kbf_ticket_metrics in destination for stream ticket_metrics complete.
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_ticket_metrics in destination.
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_kbf_ticket_metrics in destination prepared.
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_kbf_ticket_metrics to dest table: _airbyte_raw_ticket_metrics, schema: zendesk_intercom, in destination.
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'tags'.
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [33mWARN[m a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/tags/2022_04_18_1650286818693_9bfc358e-8881-44ab-9b43-72e361b69b9b.csv with id IaLGcbfe1...fKufU_A--]: Uploading leftover stream [Part number 1 containing 0.02 MB]
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/tags/2022_04_18_1650286818693_9bfc358e-8881-44ab-9b43-72e361b69b9b.csv with id IaLGcbfe1...fKufU_A--]: Finished uploading [Part number 1 containing 0.02 MB]
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/tags/2022_04_18_1650286818693_9bfc358e-8881-44ab-9b43-72e361b69b9b.csv with id IaLGcbfe1...fKufU_A--]: Completed
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'tags'.
2022-04-18 15:55:14 [43mdestination[0m > 2022-04-18 15:55:14 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk_intercom
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: tags, schema: zendesk_intercom, tmp table name: _airbyte_tmp_kmd_tags.
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_kmd_tags in destination for stream: tags, schema: zendesk_intercom, .
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_kmd_tags in destination for stream tags complete.
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_tags in destination.
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_kmd_tags in destination prepared.
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_kmd_tags to dest table: _airbyte_raw_tags, schema: zendesk_intercom, in destination.
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'ticket_fields'.
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [33mWARN[m a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_fields/2022_04_18_1650286818693_6d5cdb45-d0ae-44d1-bb50-5debba38c9d4.csv with id zoaTpbWHq...4uHbkPA--]: Uploading leftover stream [Part number 1 containing 0.02 MB]
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_fields/2022_04_18_1650286818693_6d5cdb45-d0ae-44d1-bb50-5debba38c9d4.csv with id zoaTpbWHq...4uHbkPA--]: Finished uploading [Part number 1 containing 0.02 MB]
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_fields/2022_04_18_1650286818693_6d5cdb45-d0ae-44d1-bb50-5debba38c9d4.csv with id zoaTpbWHq...4uHbkPA--]: Completed
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'ticket_fields'.
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk_intercom
2022-04-18 15:55:15 [43mdestination[0m > 2022-04-18 15:55:15 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: ticket_fields, schema: zendesk_intercom, tmp table name: _airbyte_tmp_zwe_ticket_fields.
2022-04-18 15:55:16 [43mdestination[0m > 2022-04-18 15:55:16 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_zwe_ticket_fields in destination for stream: ticket_fields, schema: zendesk_intercom, .
2022-04-18 15:55:16 [43mdestination[0m > 2022-04-18 15:55:16 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_zwe_ticket_fields in destination for stream ticket_fields complete.
2022-04-18 15:55:16 [43mdestination[0m > 2022-04-18 15:55:16 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_ticket_fields in destination.
2022-04-18 15:55:16 [43mdestination[0m > 2022-04-18 15:55:16 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_zwe_ticket_fields in destination prepared.
2022-04-18 15:55:16 [43mdestination[0m > 2022-04-18 15:55:16 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_zwe_ticket_fields to dest table: _airbyte_raw_ticket_fields, schema: zendesk_intercom, in destination.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/brands/2022_04_18_1650286818677_4a4adfc7-88bf-41c6-974d-c5533a342143.csv cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_ggk_brands tmp table in destination.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_ggk_brands tmp table in destination cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/ba00b072-6964-4ad2-850c-214df2f0ae35.manifest.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/ba00b072-6964-4ad2-850c-214df2f0ae35.manifest cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/sla_policies/2022_04_18_1650286818693_7a689e7d-43a1-4f4f-aa9c-1687423fc517.csv cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_dsu_sla_policies tmp table in destination.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_dsu_sla_policies tmp table in destination cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/8d23ffc6-45c0-41f9-9faf-fdecaa95cb46.manifest.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/8d23ffc6-45c0-41f9-9faf-fdecaa95cb46.manifest cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/tickets/2022_04_18_1650286818694_f3095ae6-59ee-4c89-ac9c-3f32ee9fd572.csv cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_xzp_tickets tmp table in destination.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_xzp_tickets tmp table in destination cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/639fd481-b18c-4571-8f3d-3600a3c5e69f.manifest.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/639fd481-b18c-4571-8f3d-3600a3c5e69f.manifest cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/ticket_metric_events/2022_04_18_1650286818694_2be5728a-df0a-4c82-a4d6-8550c08f5f9e.csv cleaned.
2022-04-18 15:55:17 [43mdestination[0m > 2022-04-18 15:55:17 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_auz_ticket_metric_events tmp table in destination.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_auz_ticket_metric_events tmp table in destination cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/92ea04da-81cf-4c20-8662-cc28c193064b.manifest.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/92ea04da-81cf-4c20-8662-cc28c193064b.manifest cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/ticket_metrics/2022_04_18_1650286818694_9b4110e3-d230-4858-ba1e-a80f13496a21.csv cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_kbf_ticket_metrics tmp table in destination.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_kbf_ticket_metrics tmp table in destination cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/9ab6ff62-f6c1-41ef-a73e-f7f1c5b6f6bc.manifest.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/9ab6ff62-f6c1-41ef-a73e-f7f1c5b6f6bc.manifest cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/tags/2022_04_18_1650286818693_9bfc358e-8881-44ab-9b43-72e361b69b9b.csv cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_kmd_tags tmp table in destination.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_kmd_tags tmp table in destination cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/18b7723b-c6a6-4410-8d5a-b6cd023fb93c.manifest.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/18b7723b-c6a6-4410-8d5a-b6cd023fb93c.manifest cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/ticket_fields/2022_04_18_1650286818693_6d5cdb45-d0ae-44d1-bb50-5debba38c9d4.csv cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_zwe_ticket_fields tmp table in destination.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_zwe_ticket_fields tmp table in destination cleaned.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/6786e69f-135e-4779-94c4-3efb96bb7e82.manifest.
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/29243caf-06ec-4743-b94b-93389e123e10/zendesk_intercom/6786e69f-135e-4779-94c4-3efb96bb7e82.manifest cleaned.
2022-04-18 15:55:18 [32mINFO[m i.a.w.DefaultReplicationWorker(lambda$getDestinationOutputRunnable$6):347 - State in DefaultReplicationWorker from destination: io.airbyte.protocol.models.AirbyteMessage@601d9df0[type=STATE,log=<null>,spec=<null>,connectionStatus=<null>,catalog=<null>,record=<null>,state=io.airbyte.protocol.models.AirbyteStateMessage@6eda6f83[data={"ticket_fields":{"updated_at":"2022-04-14T06:20:39Z"},"ticket_metric_events":{"time":"2022-04-18T12:51:26Z"},"ticket_metrics":{"updated_at":"2022-04-18T12:51:27Z"},"tickets":{"updated_at":"2022-04-18T15:46:03Z"}},additionalProperties={}],additionalProperties={}]
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.b.IntegrationRunner(runInternal):169 - Completed integration: io.airbyte.integrations.destination.redshift.RedshiftDestination
2022-04-18 15:55:18 [43mdestination[0m > 2022-04-18 15:55:18 [32mINFO[m i.a.i.d.r.RedshiftDestination(main):79 - completed destination: class io.airbyte.integrations.destination.redshift.RedshiftDestination
2022-04-18 15:55:18 [32mINFO[m i.a.w.DefaultReplicationWorker(run):164 - Source and destination threads complete.
2022-04-18 15:55:18 [32mINFO[m i.a.w.DefaultReplicationWorker(run):227 - sync summary: io.airbyte.config.ReplicationAttemptSummary@a81c8c4[status=completed,recordsSynced=19010,bytesSynced=15288160,startTime=1650286801614,endTime=1650297318842,totalStats=io.airbyte.config.SyncStats@544fb084[recordsEmitted=19010,bytesEmitted=15288160,stateMessagesEmitted=4,recordsCommitted=19010],streamStats=[io.airbyte.config.StreamSyncStats@13152444[streamName=ticket_fields,stats=io.airbyte.config.SyncStats@9ded028[recordsEmitted=14,bytesEmitted=14758,stateMessagesEmitted=<null>,recordsCommitted=14]], io.airbyte.config.StreamSyncStats@d1dee3f[streamName=brands,stats=io.airbyte.config.SyncStats@24ffa488[recordsEmitted=12,bytesEmitted=20589,stateMessagesEmitted=<null>,recordsCommitted=12]], io.airbyte.config.StreamSyncStats@1e728357[streamName=tickets,stats=io.airbyte.config.SyncStats@61524f21[recordsEmitted=512,bytesEmitted=1250208,stateMessagesEmitted=<null>,recordsCommitted=512]], io.airbyte.config.StreamSyncStats@35c3922[streamName=ticket_metrics,stats=io.airbyte.config.SyncStats@5ade0625[recordsEmitted=14777,bytesEmitted=13462745,stateMessagesEmitted=<null>,recordsCommitted=14777]], io.airbyte.config.StreamSyncStats@503c5e12[streamName=sla_policies,stats=io.airbyte.config.SyncStats@23e42873[recordsEmitted=11,bytesEmitted=26555,stateMessagesEmitted=<null>,recordsCommitted=11]], io.airbyte.config.StreamSyncStats@41939004[streamName=ticket_metric_events,stats=io.airbyte.config.SyncStats@6bd6493d[recordsEmitted=3488,bytesEmitted=507244,stateMessagesEmitted=<null>,recordsCommitted=3488]], io.airbyte.config.StreamSyncStats@5928469b[streamName=tags,stats=io.airbyte.config.SyncStats@5b8d5a7d[recordsEmitted=196,bytesEmitted=6061,stateMessagesEmitted=<null>,recordsCommitted=196]]]]
2022-04-18 15:55:18 [32mINFO[m i.a.w.DefaultReplicationWorker(run):247 - Source output at least one state message
2022-04-18 15:55:18 [32mINFO[m i.a.w.DefaultReplicationWorker(run):253 - State capture: Updated state to: Optional[io.airbyte.config.State@6c3f2b8[state={"ticket_fields":{"updated_at":"2022-04-14T06:20:39Z"},"ticket_metric_events":{"time":"2022-04-18T12:51:26Z"},"ticket_metrics":{"updated_at":"2022-04-18T12:51:27Z"},"tickets":{"updated_at":"2022-04-18T15:46:03Z"}}]]
2022-04-18 15:55:18 [32mINFO[m i.a.w.t.TemporalAttemptExecution(get):131 - Stopping cancellation check scheduling...
2022-04-18 15:55:18 [32mINFO[m i.a.w.t.s.ReplicationActivityImpl(lambda$replicate$1):144 - sync summary: io.airbyte.config.StandardSyncOutput@51f66476[standardSyncSummary=io.airbyte.config.StandardSyncSummary@7769c0a8[status=completed,recordsSynced=19010,bytesSynced=15288160,startTime=1650286801614,endTime=1650297318842,totalStats=io.airbyte.config.SyncStats@544fb084[recordsEmitted=19010,bytesEmitted=15288160,stateMessagesEmitted=4,recordsCommitted=19010],streamStats=[io.airbyte.config.StreamSyncStats@13152444[streamName=ticket_fields,stats=io.airbyte.config.SyncStats@9ded028[recordsEmitted=14,bytesEmitted=14758,stateMessagesEmitted=<null>,recordsCommitted=14]], io.airbyte.config.StreamSyncStats@d1dee3f[streamName=brands,stats=io.airbyte.config.SyncStats@24ffa488[recordsEmitted=12,bytesEmitted=20589,stateMessagesEmitted=<null>,recordsCommitted=12]], io.airbyte.config.StreamSyncStats@1e728357[streamName=tickets,stats=io.airbyte.config.SyncStats@61524f21[recordsEmitted=512,bytesEmitted=1250208,stateMessagesEmitted=<null>,recordsCommitted=512]], io.airbyte.config.StreamSyncStats@35c3922[streamName=ticket_metrics,stats=io.airbyte.config.SyncStats@5ade0625[recordsEmitted=14777,bytesEmitted=13462745,stateMessagesEmitted=<null>,recordsCommitted=14777]], io.airbyte.config.StreamSyncStats@503c5e12[streamName=sla_policies,stats=io.airbyte.config.SyncStats@23e42873[recordsEmitted=11,bytesEmitted=26555,stateMessagesEmitted=<null>,recordsCommitted=11]], io.airbyte.config.StreamSyncStats@41939004[streamName=ticket_metric_events,stats=io.airbyte.config.SyncStats@6bd6493d[recordsEmitted=3488,bytesEmitted=507244,stateMessagesEmitted=<null>,recordsCommitted=3488]], io.airbyte.config.StreamSyncStats@5928469b[streamName=tags,stats=io.airbyte.config.SyncStats@5b8d5a7d[recordsEmitted=196,bytesEmitted=6061,stateMessagesEmitted=<null>,recordsCommitted=196]]]],state=io.airbyte.config.State@6c3f2b8[state={"ticket_fields":{"updated_at":"2022-04-14T06:20:39Z"},"ticket_metric_events":{"time":"2022-04-18T12:51:26Z"},"ticket_metrics":{"updated_at":"2022-04-18T12:51:27Z"},"tickets":{"updated_at":"2022-04-18T15:46:03Z"}}],outputCatalog=io.airbyte.protocol.models.ConfiguredAirbyteCatalog@566c1615[streams=[io.airbyte.protocol.models.ConfiguredAirbyteStream@2845baa4[stream=io.airbyte.protocol.models.AirbyteStream@4af2ae0e[name=brands,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"url":{"type":["null","string"]},"logo":{"type":["null","string"]},"name":{"type":["null","string"]},"active":{"type":["null","boolean"]},"default":{"type":["null","boolean"]},"brand_url":{"type":["null","string"]},"subdomain":{"type":["null","string"]},"created_at":{"type":["null","string"],"format":"date-time"},"is_deleted":{"type":["null","boolean"]},"updated_at":{"type":["null","string"],"format":"date-time"},"host_mapping":{"type":["null","string"]},"has_help_center":{"type":["null","boolean"]},"ticket_form_ids":{"type":["null","array"]},"help_center_state":{"type":["null","string"]},"signature_template":{"type":["null","string"]}}},supportedSyncModes=[full_refresh],sourceDefinedCursor=<null>,defaultCursorField=[],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=full_refresh,cursorField=[],destinationSyncMode=append,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@73275b78[stream=io.airbyte.protocol.models.AirbyteStream@454d37b7[name=sla_policies,jsonSchema={"type":["object"],"properties":{"id":{"type":["integer"]},"url":{"type":["null","string"]},"title":{"type":["null","string"]},"filter":{"type":["null","object"],"properties":{"all":{"type":["null","array"],"items":{"type":["object"],"properties":{"field":{"type":["null","string"]},"value":{"type":["null","string","number","boolean"]},"operator":{"type":["null","string"]}}}},"any":{"type":["null","array"],"items":{"type":["object"],"properties":{"field":{"type":["null","string"]},"value":{"type":["null","string"]},"operator":{"type":["null","string"]}}}}}},"position":{"type":["null","integer"]},"created_at":{"type":["null","string"],"format":"date-time"},"updated_at":{"type":["null","string"],"format":"date-time"},"description":{"type":["null","string"]},"policy_metrics":{"type":["null","array"],"items":{"type":["null","object"],"properties":{"metric":{"type":["null","string"]},"target":{"type":["null","integer"]},"priority":{"type":["null","string"]},"business_hours":{"type":["null","boolean"]}}}}}},supportedSyncModes=[full_refresh],sourceDefinedCursor=<null>,defaultCursorField=[],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=full_refresh,cursorField=[],destinationSyncMode=append,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@40582520[stream=io.airbyte.protocol.models.AirbyteStream@1f8571fb[name=tags,jsonSchema={"type":["null","object"],"properties":{"name":{"type":["null","string"]},"count":{"type":["null","integer"]}}},supportedSyncModes=[full_refresh],sourceDefinedCursor=<null>,defaultCursorField=[],sourceDefinedPrimaryKey=[[name]],namespace=<null>,additionalProperties={}],syncMode=full_refresh,cursorField=[],destinationSyncMode=append,primaryKey=[[name]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@563015e8[stream=io.airbyte.protocol.models.AirbyteStream@7a6c6016[name=ticket_fields,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"tag":{"type":["null","string"]},"url":{"type":["null","string"]},"type":{"type":["null","string"]},"title":{"type":["null","string"]},"active":{"type":["null","boolean"]},"position":{"type":["null","integer"]},"required":{"type":["null","boolean"]},"raw_title":{"type":["null","string"]},"removable":{"type":["null","boolean"]},"created_at":{"type":["null","string"],"format":"date-time"},"updated_at":{"type":["null","string"],"format":"date-time"},"description":{"type":["null","string"]},"sub_type_id":{"type":["null","integer"]},"raw_description":{"type":["null","string"]},"title_in_portal":{"type":["null","string"]},"agent_description":{"type":["null","string"]},"visible_in_portal":{"type":["null","boolean"]},"editable_in_portal":{"type":["null","boolean"]},"required_in_portal":{"type":["null","boolean"]},"raw_title_in_portal":{"type":["null","string"]},"collapsed_for_agents":{"type":["null","boolean"]},"custom_field_options":{"type":["null","array"],"items":{"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"name":{"type":["null","string"]},"value":{"type":["null","string"]},"default":{"type":["null","boolean"]},"raw_name":{"type":["null","string"]}}}},"system_field_options":{"type":["null","array"]},"regexp_for_validation":{"type":["null","string"]}}},supportedSyncModes=[full_refresh, incremental],sourceDefinedCursor=true,defaultCursorField=[updated_at],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=incremental,cursorField=[updated_at],destinationSyncMode=append_dedup,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@7b9c2b5e[stream=io.airbyte.protocol.models.AirbyteStream@4fa2877f[name=ticket_metric_events,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"time":{"type":["null","string"]},"type":{"type":["null","string"]},"metric":{"type":["null","string"]},"ticket_id":{"type":["null","integer"]},"instance_id":{"type":["null","integer"]}}},supportedSyncModes=[full_refresh, incremental],sourceDefinedCursor=true,defaultCursorField=[time],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=incremental,cursorField=[time],destinationSyncMode=append_dedup,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@19eb4436[stream=io.airbyte.protocol.models.AirbyteStream@1eebcd[name=ticket_metrics,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"url":{"type":["null","string"]},"time":{"type":["null","string"]},"type":{"type":["null","string"]},"metric":{"type":["null","string"]},"status":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"reopens":{"type":["null","integer"]},"replies":{"type":["null","integer"]},"solved_at":{"type":["null","string"],"format":"date-time"},"ticket_id":{"type":["null","integer"]},"created_at":{"type":["null","string"],"format":"date-time"},"updated_at":{"type":["null","string"],"format":"date-time"},"assigned_at":{"type":["null","string"],"format":"date-time"},"instance_id":{"type":["null","integer"]},"group_stations":{"type":["null","integer"]},"assignee_stations":{"type":["null","integer"]},"status_updated_at":{"type":["null","string"],"format":"date-time"},"assignee_updated_at":{"type":["null","string"],"format":"date-time"},"requester_updated_at":{"type":["null","string"],"format":"date-time"},"initially_assigned_at":{"type":["null","string"],"format":"date-time"},"reply_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"latest_comment_added_at":{"type":["null","string"],"format":"date-time"},"on_hold_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"agent_wait_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"requester_wait_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"full_resolution_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"first_resolution_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}}}},supportedSyncModes=[full_refresh, incremental],sourceDefinedCursor=true,defaultCursorField=[updated_at],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=incremental,cursorField=[updated_at],destinationSyncMode=append_dedup,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@1a42f64b[stream=io.airbyte.protocol.models.AirbyteStream@41800303[name=tickets,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"url":{"type":["null","string"]},"via":{"type":["null","object"],"properties":{"source":{"type":["null","object"],"properties":{"to":{"type":["null","object"],"properties":{"name":{"type":["null","string"]},"phone":{"type":["null","string"]},"address":{"type":["null","string"]},"username":{"type":["null","string"]},"email_ccs":{"type":["null","string"]},"facebook_id":{"type":["null","string"]},"profile_url":{"type":["null","string"]},"formatted_phone":{"type":["null","string"]}}},"rel":{"type":["null","string"]},"from":{"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"name":{"type":["null","string"]},"phone":{"type":["null","string"]},"title":{"type":["null","string"]},"address":{"type":["null","string"]},"deleted":{"type":["null","boolean"]},"subject":{"type":["null","string"]},"topic_id":{"type":["null","integer"]},"username":{"type":["null","string"]},"ticket_id":{"type":["null","integer"]},"topic_name":{"type":["null","string"]},"facebook_id":{"type":["null","string"]},"profile_url":{"type":["null","string"]},"revision_id":{"type":["null","integer"]},"formatted_phone":{"type":["null","string"]},"original_recipients":{"type":["null","array"],"items":{"type":["null","string"]}}}}}},"channel":{"type":["null","string"]}}},"tags":{"type":["null","array"],"items":{"type":["null","string"]}},"type":{"type":["null","string"]},"due_at":{"type":["null","string"],"format":"date-time"},"status":{"type":["null","string"]},"subject":{"type":["null","string"]},"brand_id":{"type":["null","integer"]},"group_id":{"type":["null","integer"]},"priority":{"type":["null","string"]},"is_public":{"type":["null","boolean"]},"recipient":{"type":["null","string"]},"created_at":{"type":["null","string"],"format":"date-time"},"problem_id":{"type":["null","integer"]},"updated_at":{"type":["null","string"],"format":"date-time"},"assignee_id":{"type":["null","integer"]},"description":{"type":["null","string"]},"external_id":{"type":["null","string"]},"raw_subject":{"type":["null","string"]},"email_cc_ids":{"type":["null","array"],"items":{"type":["null","integer"]}},"follower_ids":{"type":["null","array"],"items":{"type":["null","integer"]}},"followup_ids":{"type":["null","array"],"items":{"type":["null","integer"]}},"requester_id":{"type":["null","integer"]},"submitter_id":{"type":["null","integer"]},"custom_fields":{"type":["null","array"],"items":{"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"value":{"type":["null","string"]}}}},"has_incidents":{"type":["null","boolean"]},"forum_topic_id":{"type":["null","integer"]},"ticket_form_id":{"type":["null","integer"]},"organization_id":{"type":["null","integer"]},"collaborator_ids":{"type":["null","array"],"items":{"type":["null","integer"]}},"allow_attachments":{"type":["null","boolean"]},"allow_channelback":{"type":["null","boolean"]},"generated_timestamp":{"type":["null","integer"]},"satisfaction_rating":{"type":["null","object","string"],"properties":{"id":{"type":["null","integer"]},"url":{"type":["null","string"]},"score":{"type":["null","string"]},"reason":{"type":["null","string"]},"comment":{"type":["null","string"]},"group_id":{"type":["null","integer"]},"reason_id":{"type":["null","integer"]},"ticket_id":{"type":["null","integer"]},"created_at":{"type":["null","string"],"format":"date-time"},"updated_at":{"type":["null","string"],"format":"date-time"},"assignee_id":{"type":["null","integer"]},"requester_id":{"type":["null","integer"]}}},"sharing_agreement_ids":{"type":["null","array"],"items":{"type":["null","integer"]}}}},supportedSyncModes=[full_refresh, incremental],sourceDefinedCursor=true,defaultCursorField=[updated_at],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=incremental,cursorField=[updated_at],destinationSyncMode=append_dedup,primaryKey=[[id]],additionalProperties={}]],additionalProperties={}],failures=[]]
2022-04-18 15:55:18 [32mINFO[m i.a.w.t.TemporalUtils(withBackgroundHeartbeat):234 - Stopping temporal heartbeating...
2022-04-18 15:55:18 [32mINFO[m i.a.c.p.ConfigRepository(updateConnectionState):545 - Updating connection b50f36e6-70ca-4384-bd12-93d1f650e999 state: io.airbyte.config.State@629fde[state={"ticket_fields":{"updated_at":"2022-04-14T06:20:39Z"},"ticket_metric_events":{"time":"2022-04-18T12:51:26Z"},"ticket_metrics":{"updated_at":"2022-04-18T12:51:27Z"},"tickets":{"updated_at":"2022-04-18T15:46:03Z"}}]
2022-04-18 15:55:19 [32mINFO[m i.a.w.t.TemporalAttemptExecution(get):105 - Docker volume job log path: /tmp/workspace/16/1/logs.log
2022-04-18 15:55:19 [32mINFO[m i.a.w.t.TemporalAttemptExecution(get):110 - Executing worker wrapper. Airbyte version: 0.35.30-alpha
2022-04-18 15:55:19 [32mINFO[m i.a.w.DefaultNormalizationWorker(run):46 - Running normalization.
2022-04-18 15:55:19 [32mINFO[m i.a.w.n.DefaultNormalizationRunner(runProcess):122 - Running with normalization version: airbyte/normalization:0.1.66
2022-04-18 15:55:19 [32mINFO[m i.a.c.i.LineGobbler(voidCall):82 - Checking if airbyte/normalization:0.1.66 exists...
2022-04-18 15:55:19 [32mINFO[m i.a.c.i.LineGobbler(voidCall):82 - airbyte/normalization:0.1.66 was found locally.
2022-04-18 15:55:19 [32mINFO[m i.a.w.p.DockerProcessFactory(create):157 - Preparing command: docker run --rm --init -i -w /data/16/1/normalize --log-driver none --network host -v airbyte_workspace:/data -v /tmp/airbyte_local:/local airbyte/normalization:0.1.66 run --integration-type redshift --config destination_config.json --catalog destination_catalog.json
2022-04-18 15:55:19 [42mnormalization[0m > Running: transform-config --config destination_config.json --integration-type redshift --out /data/16/1/normalize
2022-04-18 15:55:20 [42mnormalization[0m > Namespace(config='destination_config.json', integration_type=<DestinationType.redshift: 'redshift'>, out='/data/16/1/normalize')
2022-04-18 15:55:20 [42mnormalization[0m > transform_redshift
2022-04-18 15:55:20 [42mnormalization[0m > Running: transform-catalog --integration-type redshift --profile-config-dir /data/16/1/normalize --catalog destination_catalog.json --out /data/16/1/normalize/models/generated/ --json-column _airbyte_data
2022-04-18 15:55:21 [42mnormalization[0m > Processing destination_catalog.json...
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/brands_ab1.sql from brands
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/brands_ab2.sql from brands
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/brands_ab3.sql from brands
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/brands.sql from brands
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_ab1.sql from sla_policies
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_ab2.sql from sla_policies
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_ab3.sql from sla_policies
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/sla_policies.sql from sla_policies
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tags_ab1.sql from tags
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tags_ab2.sql from tags
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tags_ab3.sql from tags
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/tags.sql from tags
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_fields_ab1.sql from ticket_fields
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_fields_ab2.sql from ticket_fields
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_views/zendesk_intercom/ticket_fields_stg.sql from ticket_fields
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/scd/zendesk_intercom/ticket_fields_scd.sql from ticket_fields
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_fields.sql from ticket_fields
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metric_events_ab1.sql from ticket_metric_events
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metric_events_ab2.sql from ticket_metric_events
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_views/zendesk_intercom/ticket_metric_events_stg.sql from ticket_metric_events
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/scd/zendesk_intercom/ticket_metric_events_scd.sql from ticket_metric_events
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_metric_events.sql from ticket_metric_events
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_ab1.sql from ticket_metrics
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_ab2.sql from ticket_metrics
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_views/zendesk_intercom/ticket_metrics_stg.sql from ticket_metrics
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/scd/zendesk_intercom/ticket_metrics_scd.sql from ticket_metrics
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_metrics.sql from ticket_metrics
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_ab1.sql from tickets
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_ab2.sql from tickets
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_views/zendesk_intercom/tickets_stg.sql from tickets
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/scd/zendesk_intercom/tickets_scd.sql from tickets
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/tickets.sql from tickets
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_filter_ab1.sql from sla_policies/filter
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_filter_ab2.sql from sla_policies/filter
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_filter_ab3.sql from sla_policies/filter
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/sla_policies_filter.sql from sla_policies/filter
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_policy_metrics_ab1.sql from sla_policies/policy_metrics
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_policy_metrics_ab2.sql from sla_policies/policy_metrics
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_policy_metrics_ab3.sql from sla_policies/policy_metrics
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/sla_policies_policy_metrics.sql from sla_policies/policy_metrics
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_fields_custom_field_options_ab1.sql from ticket_fields/custom_field_options
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_fields_custom_field_options_ab2.sql from ticket_fields/custom_field_options
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_fields_custom_field_options_ab3.sql from ticket_fields/custom_field_options
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_fields_custom_field_options.sql from ticket_fields/custom_field_options
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_status_ab1.sql from ticket_metrics/status
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_status_ab2.sql from ticket_metrics/status
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_status_ab3.sql from ticket_metrics/status
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_metrics_status.sql from ticket_metrics/status
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_reply_time_in_minutes_ab1.sql from ticket_metrics/reply_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_reply_time_in_minutes_ab2.sql from ticket_metrics/reply_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_reply_time_in_minutes_ab3.sql from ticket_metrics/reply_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_metrics_reply_time_in_minutes.sql from ticket_metrics/reply_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_on_hold_time_in_minutes_ab1.sql from ticket_metrics/on_hold_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_on_hold_time_in_minutes_ab2.sql from ticket_metrics/on_hold_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_on_hold_time_in_minutes_ab3.sql from ticket_metrics/on_hold_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_metrics_on_hold_time_in_minutes.sql from ticket_metrics/on_hold_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_agent_wait_time_in_minutes_ab1.sql from ticket_metrics/agent_wait_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_agent_wait_time_in_minutes_ab2.sql from ticket_metrics/agent_wait_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_agent_wait_time_in_minutes_ab3.sql from ticket_metrics/agent_wait_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_metrics_agent_wait_time_in_minutes.sql from ticket_metrics/agent_wait_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_requester_wait_time_in_minutes_ab1.sql from ticket_metrics/requester_wait_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_requester_wait_time_in_minutes_ab2.sql from ticket_metrics/requester_wait_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_requester_wait_time_in_minutes_ab3.sql from ticket_metrics/requester_wait_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_metrics_requester_wait_time_in_minutes.sql from ticket_metrics/requester_wait_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_full_resolution_time_in_minutes_ab1.sql from ticket_metrics/full_resolution_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_full_resolution_time_in_minutes_ab2.sql from ticket_metrics/full_resolution_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_full_resolution_time_in_minutes_ab3.sql from ticket_metrics/full_resolution_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_metrics_full_resolution_time_in_minutes.sql from ticket_metrics/full_resolution_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_first_resolution_time_in_minutes_ab1.sql from ticket_metrics/first_resolution_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_first_resolution_time_in_minutes_ab2.sql from ticket_metrics/first_resolution_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/ticket_metrics_first_resolution_time_in_minutes_ab3.sql from ticket_metrics/first_resolution_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/ticket_metrics_first_resolution_time_in_minutes.sql from ticket_metrics/first_resolution_time_in_minutes
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_ab1.sql from tickets/via
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_ab2.sql from tickets/via
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_ab3.sql from tickets/via
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/tickets_via.sql from tickets/via
2022-04-18 15:55:21 [42mnormalization[0m >   Ignoring stream 'tags' from tickets/tags because properties list is empty
2022-04-18 15:55:21 [42mnormalization[0m >   Ignoring stream 'email_cc_ids' from tickets/email_cc_ids because properties list is empty
2022-04-18 15:55:21 [42mnormalization[0m >   Ignoring stream 'follower_ids' from tickets/follower_ids because properties list is empty
2022-04-18 15:55:21 [42mnormalization[0m >   Ignoring stream 'followup_ids' from tickets/followup_ids because properties list is empty
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_custom_fields_ab1.sql from tickets/custom_fields
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_custom_fields_ab2.sql from tickets/custom_fields
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_custom_fields_ab3.sql from tickets/custom_fields
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/tickets_custom_fields.sql from tickets/custom_fields
2022-04-18 15:55:21 [42mnormalization[0m >   Ignoring stream 'collaborator_ids' from tickets/collaborator_ids because properties list is empty
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_satisfaction_rating_ab1.sql from tickets/satisfaction_rating
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_satisfaction_rating_ab2.sql from tickets/satisfaction_rating
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_satisfaction_rating_ab3.sql from tickets/satisfaction_rating
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/tickets_satisfaction_rating.sql from tickets/satisfaction_rating
2022-04-18 15:55:21 [42mnormalization[0m >   Ignoring stream 'sharing_agreement_ids' from tickets/sharing_agreement_ids because properties list is empty
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_filter_all_ab1.sql from sla_policies/filter/all
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_filter_all_ab2.sql from sla_policies/filter/all
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_filter_all_ab3.sql from sla_policies/filter/all
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/sla_policies_filter_all.sql from sla_policies/filter/all
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_filter_any_ab1.sql from sla_policies/filter/any
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_filter_any_ab2.sql from sla_policies/filter/any
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/sla_policies_filter_any_ab3.sql from sla_policies/filter/any
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/sla_policies_filter_any.sql from sla_policies/filter/any
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_source_ab1.sql from tickets/via/source
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_source_ab2.sql from tickets/via/source
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_source_ab3.sql from tickets/via/source
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/tickets_via_source.sql from tickets/via/source
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_source_to_ab1.sql from tickets/via/source/to
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_source_to_ab2.sql from tickets/via/source/to
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_source_to_ab3.sql from tickets/via/source/to
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/tickets_via_source_to.sql from tickets/via/source/to
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_source_from_ab1.sql from tickets/via/source/from
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_source_from_ab2.sql from tickets/via/source/from
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_ctes/zendesk_intercom/tickets_via_source_from_ab3.sql from tickets/via/source/from
2022-04-18 15:55:21 [42mnormalization[0m >   Generating airbyte_incremental/zendesk_intercom/tickets_via_source_from.sql from tickets/via/source/from
2022-04-18 15:55:21 [42mnormalization[0m >   Ignoring stream 'original_recipients' from tickets/via/source/from/original_recipients because properties list is empty
2022-04-18 15:55:21 [42mnormalization[0m > detected no config file for ssh, assuming ssh is off.
2022-04-18 15:55:24 [42mnormalization[0m > Running with dbt=0.21.1
2022-04-18 15:55:24 [42mnormalization[0m > Unable to do partial parsing because ../build/partial_parse.msgpack not found
2022-04-18 15:55:27 [42mnormalization[0m > [[33mWARNING[0m]: Configuration paths exist in your dbt_project.yml file which do not apply to any resources.
2022-04-18 15:55:27 [42mnormalization[0m > There are 1 unused configuration paths:
2022-04-18 15:55:27 [42mnormalization[0m > - models.airbyte_utils.generated.airbyte_tables
2022-04-18 15:55:27 [42mnormalization[0m > 
2022-04-18 15:55:27 [42mnormalization[0m > Found 104 models, 0 tests, 0 snapshots, 0 analyses, 520 macros, 0 operations, 0 seed files, 7 sources, 0 exposures
2022-04-18 15:55:27 [42mnormalization[0m > 
2022-04-18 15:55:27 [42mnormalization[0m > 15:55:27 | Concurrency: 4 threads (target='prod')
2022-04-18 15:55:27 [42mnormalization[0m > 15:55:27 | 
2022-04-18 15:55:28 [42mnormalization[0m > 15:55:28 | 1 of 33 START view model _airbyte_zendesk_intercom.ticket_metric_events_stg.................................. [RUN]
2022-04-18 15:55:28 [42mnormalization[0m > 15:55:28 | 2 of 33 START incremental model zendesk_intercom.tags........................................................ [RUN]
2022-04-18 15:55:28 [42mnormalization[0m > 15:55:28 | 3 of 33 START view model _airbyte_zendesk_intercom.ticket_fields_stg......................................... [RUN]
2022-04-18 15:55:28 [42mnormalization[0m > 15:55:28 | 4 of 33 START incremental model zendesk_intercom.brands...................................................... [RUN]
2022-04-18 15:55:29 [42mnormalization[0m > 15:55:29 | 1 of 33 OK created view model _airbyte_zendesk_intercom.ticket_metric_events_stg............................. [[32mCREATE VIEW[0m in 0.72s]
2022-04-18 15:55:29 [42mnormalization[0m > 15:55:29 | 5 of 33 START view model _airbyte_zendesk_intercom.ticket_metrics_stg........................................ [RUN]
2022-04-18 15:55:29 [42mnormalization[0m > 15:55:29 | 3 of 33 OK created view model _airbyte_zendesk_intercom.ticket_fields_stg.................................... [[32mCREATE VIEW[0m in 0.78s]
2022-04-18 15:55:29 [42mnormalization[0m > 15:55:29 | 6 of 33 START incremental model zendesk_intercom.sla_policies................................................ [RUN]
2022-04-18 15:55:29 [42mnormalization[0m > 15:55:29 | 5 of 33 OK created view model _airbyte_zendesk_intercom.ticket_metrics_stg................................... [[32mCREATE VIEW[0m in 0.48s]
2022-04-18 15:55:29 [42mnormalization[0m > 15:55:29 | 7 of 33 START view model _airbyte_zendesk_intercom.tickets_stg............................................... [RUN]
2022-04-18 15:55:30 [42mnormalization[0m > 15:55:30 | 7 of 33 OK created view model _airbyte_zendesk_intercom.tickets_stg.......................................... [[32mCREATE VIEW[0m in 0.49s]
2022-04-18 15:55:30 [42mnormalization[0m > 15:55:30 | 8 of 33 START incremental model zendesk_intercom.ticket_metric_events_scd.................................... [RUN]
2022-04-18 15:55:32 [42mnormalization[0m > 15:55:32 | 2 of 33 OK created incremental model zendesk_intercom.tags................................................... [[32mINSERT 0 198[0m in 4.24s]
2022-04-18 15:55:32 [42mnormalization[0m > 15:55:32 | 9 of 33 START incremental model zendesk_intercom.ticket_fields_scd........................................... [RUN]
2022-04-18 15:55:33 [42mnormalization[0m > 15:55:33 | 4 of 33 OK created incremental model zendesk_intercom.brands................................................. [[32mINSERT 0 13[0m in 5.05s]
2022-04-18 15:55:33 [42mnormalization[0m > 15:55:33 | 10 of 33 START incremental model zendesk_intercom.ticket_metrics_scd......................................... [RUN]
2022-04-18 15:55:33 [42mnormalization[0m > 15:55:33 | 6 of 33 OK created incremental model zendesk_intercom.sla_policies........................................... [[32mINSERT 0 12[0m in 4.43s]
2022-04-18 15:55:33 [42mnormalization[0m > 15:55:33 | 11 of 33 START incremental model zendesk_intercom.tickets_scd................................................ [RUN]
2022-04-18 15:55:37 [42mnormalization[0m > 15:55:37 | 8 of 33 OK created incremental model zendesk_intercom.ticket_metric_events_scd............................... [[32mINSERT 0 6872[0m in 7.12s]
2022-04-18 15:55:37 [42mnormalization[0m > 15:55:37 | 12 of 33 START incremental model zendesk_intercom.ticket_metric_events....................................... [RUN]
2022-04-18 15:55:42 [42mnormalization[0m > 15:55:42 | 12 of 33 OK created incremental model zendesk_intercom.ticket_metric_events.................................. [[32mINSERT 0 3486[0m in 4.95s]
2022-04-18 15:55:42 [42mnormalization[0m > 15:55:42 | 11 of 33 ERROR creating incremental model zendesk_intercom.tickets_scd....................................... [[31mERROR[0m in 8.72s]
2022-04-18 15:55:42 [42mnormalization[0m > 15:55:42 | 13 of 33 SKIP relation zendesk_intercom.tickets.............................................................. [[33mSKIP[0m]
2022-04-18 15:55:42 [42mnormalization[0m > 15:55:42 | 14 of 33 START incremental model zendesk_intercom.sla_policies_filter........................................ [RUN]
2022-04-18 15:55:42 [42mnormalization[0m > 15:55:42 | 15 of 33 START incremental model zendesk_intercom.sla_policies_policy_metrics................................ [RUN]
2022-04-18 15:55:47 [42mnormalization[0m > 15:55:47 | 14 of 33 OK created incremental model zendesk_intercom.sla_policies_filter................................... [[32mINSERT 0 12[0m in 4.75s]
2022-04-18 15:55:47 [42mnormalization[0m > 15:55:47 | 16 of 33 SKIP relation zendesk_intercom.tickets_custom_fields................................................ [[33mSKIP[0m]
2022-04-18 15:55:47 [42mnormalization[0m > 15:55:47 | 17 of 33 SKIP relation zendesk_intercom.tickets_satisfaction_rating.......................................... [[33mSKIP[0m]
2022-04-18 15:55:47 [42mnormalization[0m > 15:55:47 | 18 of 33 SKIP relation zendesk_intercom.tickets_via.......................................................... [[33mSKIP[0m]
2022-04-18 15:55:47 [42mnormalization[0m > 15:55:47 | 19 of 33 START incremental model zendesk_intercom.sla_policies_filter_all.................................... [RUN]
2022-04-18 15:55:50 [42mnormalization[0m > 15:55:50 | 10 of 33 OK created incremental model zendesk_intercom.ticket_metrics_scd.................................... [[32mINSERT 0 29547[0m in 17.20s]
2022-04-18 15:55:50 [42mnormalization[0m > 15:55:50 | 20 of 33 START incremental model zendesk_intercom.sla_policies_filter_any.................................... [RUN]
2022-04-18 15:55:52 [42mnormalization[0m > 15:55:52 | 19 of 33 OK created incremental model zendesk_intercom.sla_policies_filter_all............................... [[32mINSERT 0 29[0m in 4.82s]
2022-04-18 15:55:52 [42mnormalization[0m > 15:55:52 | 21 of 33 SKIP relation zendesk_intercom.tickets_via_source................................................... [[33mSKIP[0m]
2022-04-18 15:55:52 [42mnormalization[0m > 15:55:52 | 22 of 33 START incremental model zendesk_intercom.ticket_metrics............................................. [RUN]
2022-04-18 15:55:54 [42mnormalization[0m > 15:55:54 | 15 of 33 OK created incremental model zendesk_intercom.sla_policies_policy_metrics........................... [[32mINSERT 0 576[0m in 12.35s]
2022-04-18 15:55:55 [42mnormalization[0m > 15:55:55 | 23 of 33 START incremental model zendesk_intercom.ticket_metrics_agent_wait_time_in_minutes.................. [RUN]
2022-04-18 15:55:55 [42mnormalization[0m > 15:55:55 | 20 of 33 OK created incremental model zendesk_intercom.sla_policies_filter_any............................... [[32mINSERT 0 13[0m in 4.68s]
2022-04-18 15:55:55 [42mnormalization[0m > 15:55:55 | 24 of 33 START incremental model zendesk_intercom.ticket_metrics_first_resolution_time_in_minutes............ [RUN]
2022-04-18 15:55:56 [42mnormalization[0m > 15:55:56 | 9 of 33 OK created incremental model zendesk_intercom.ticket_fields_scd...................................... [[32mINSERT 0 28[0m in 23.54s]
2022-04-18 15:55:56 [42mnormalization[0m > 15:55:56 | 25 of 33 START incremental model zendesk_intercom.ticket_metrics_full_resolution_time_in_minutes............. [RUN]
2022-04-18 15:55:58 [42mnormalization[0m > 15:55:58 | 22 of 33 OK created incremental model zendesk_intercom.ticket_metrics........................................ [[32mINSERT 0 14776[0m in 5.66s]
2022-04-18 15:55:58 [42mnormalization[0m > 15:55:58 | 26 of 33 START incremental model zendesk_intercom.ticket_metrics_on_hold_time_in_minutes..................... [RUN]
2022-04-18 15:55:59 [42mnormalization[0m > 15:55:59 | 23 of 33 OK created incremental model zendesk_intercom.ticket_metrics_agent_wait_time_in_minutes............. [[32mINSERT 0 14778[0m in 4.24s]
2022-04-18 15:55:59 [42mnormalization[0m > 15:55:59 | 27 of 33 START incremental model zendesk_intercom.ticket_metrics_reply_time_in_minutes....................... [RUN]
2022-04-18 15:55:59 [42mnormalization[0m > 15:55:59 | 24 of 33 OK created incremental model zendesk_intercom.ticket_metrics_first_resolution_time_in_minutes....... [[32mINSERT 0 14778[0m in 4.05s]
2022-04-18 15:55:59 [42mnormalization[0m > 15:55:59 | 28 of 33 START incremental model zendesk_intercom.ticket_metrics_requester_wait_time_in_minutes.............. [RUN]
2022-04-18 15:56:01 [42mnormalization[0m > 15:56:01 | 25 of 33 OK created incremental model zendesk_intercom.ticket_metrics_full_resolution_time_in_minutes........ [[32mINSERT 0 14778[0m in 4.88s]
2022-04-18 15:56:01 [42mnormalization[0m > 15:56:01 | 29 of 33 START incremental model zendesk_intercom.ticket_metrics_status...................................... [RUN]
2022-04-18 15:56:02 [42mnormalization[0m > 15:56:02 | 26 of 33 OK created incremental model zendesk_intercom.ticket_metrics_on_hold_time_in_minutes................ [[32mINSERT 0 14778[0m in 4.41s]
2022-04-18 15:56:02 [42mnormalization[0m > 15:56:02 | 30 of 33 SKIP relation zendesk_intercom.tickets_via_source_from.............................................. [[33mSKIP[0m]
2022-04-18 15:56:02 [42mnormalization[0m > 15:56:02 | 31 of 33 SKIP relation zendesk_intercom.tickets_via_source_to................................................ [[33mSKIP[0m]
2022-04-18 15:56:02 [42mnormalization[0m > 15:56:02 | 32 of 33 START incremental model zendesk_intercom.ticket_fields.............................................. [RUN]
2022-04-18 15:56:03 [42mnormalization[0m > 15:56:03 | 27 of 33 OK created incremental model zendesk_intercom.ticket_metrics_reply_time_in_minutes.................. [[32mINSERT 0 14778[0m in 4.17s]
2022-04-18 15:56:03 [42mnormalization[0m > 15:56:03 | 33 of 33 START incremental model zendesk_intercom.ticket_fields_custom_field_options......................... [RUN]
2022-04-18 15:56:04 [42mnormalization[0m > 15:56:04 | 28 of 33 OK created incremental model zendesk_intercom.ticket_metrics_requester_wait_time_in_minutes......... [[32mINSERT 0 14778[0m in 4.75s]
2022-04-18 15:56:05 [42mnormalization[0m > 15:56:05 | 29 of 33 OK created incremental model zendesk_intercom.ticket_metrics_status................................. [[32mINSERT 0 0[0m in 4.22s]
2022-04-18 15:56:06 [42mnormalization[0m > 15:56:06 | 32 of 33 OK created incremental model zendesk_intercom.ticket_fields......................................... [[32mINSERT 0 14[0m in 4.33s]
2022-04-18 15:56:11 [42mnormalization[0m > 15:56:11 | 33 of 33 OK created incremental model zendesk_intercom.ticket_fields_custom_field_options.................... [[32mINSERT 0 109[0m in 7.20s]
2022-04-18 15:56:11 [42mnormalization[0m > 15:56:11 | 
2022-04-18 15:56:11 [42mnormalization[0m > 15:56:11 | Finished running 4 view models, 29 incremental models in 43.47s.
2022-04-18 15:56:11 [42mnormalization[0m > 
2022-04-18 15:56:11 [42mnormalization[0m > [31mCompleted with 1 error and 0 warnings:[0m
2022-04-18 15:56:11 [42mnormalization[0m > 
2022-04-18 15:56:11 [42mnormalization[0m > [33mDatabase Error in model tickets_scd (models/generated/airbyte_incremental/scd/zendesk_intercom/tickets_scd.sql)[0m
2022-04-18 15:56:11 [42mnormalization[0m >   column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-18 15:56:11 [42mnormalization[0m >   HINT:  You will need to rewrite or cast the expression.
2022-04-18 15:56:11 [42mnormalization[0m > 
2022-04-18 15:56:11 [42mnormalization[0m > Done. PASS=25 WARN=0 ERROR=1 SKIP=7 TOTAL=33
2022-04-18 15:56:11 [42mnormalization[0m > 
2022-04-18 15:56:11 [42mnormalization[0m > Diagnosing dbt debug to check if destination is available for dbt and well configured (1):
2022-04-18 15:56:11 [42mnormalization[0m > 
2022-04-18 15:56:13 [42mnormalization[0m > Running with dbt=0.21.1
2022-04-18 15:56:13 [42mnormalization[0m > dbt version: 0.21.1
2022-04-18 15:56:13 [42mnormalization[0m > python version: 3.8.12
2022-04-18 15:56:13 [42mnormalization[0m > python path: /usr/local/bin/python
2022-04-18 15:56:13 [42mnormalization[0m > os info: Linux-5.10.75-79.358.amzn2.x86_64-x86_64-with-glibc2.2.5
2022-04-18 15:56:13 [42mnormalization[0m > Using profiles.yml file at /data/16/1/normalize/profiles.yml
2022-04-18 15:56:13 [42mnormalization[0m > Using dbt_project.yml file at /data/16/1/normalize/dbt_project.yml
2022-04-18 15:56:13 [42mnormalization[0m > 
2022-04-18 15:56:13 [42mnormalization[0m > Configuration:
2022-04-18 15:56:13 [42mnormalization[0m >   profiles.yml file [OK found and valid]
2022-04-18 15:56:13 [42mnormalization[0m >   dbt_project.yml file [OK found and valid]
2022-04-18 15:56:13 [42mnormalization[0m > 
2022-04-18 15:56:13 [42mnormalization[0m > Required dependencies:
2022-04-18 15:56:13 [42mnormalization[0m >  - git [OK found]
2022-04-18 15:56:13 [42mnormalization[0m > 
2022-04-18 15:56:13 [42mnormalization[0m > Connection:
2022-04-18 15:56:13 [42mnormalization[0m >   host: travlr-data-lake.cs2yeo76upoy.ap-southeast-2.redshift.amazonaws.com
2022-04-18 15:56:13 [42mnormalization[0m >   port: 5439
2022-04-18 15:56:13 [42mnormalization[0m >   user: travlrdatamaster
2022-04-18 15:56:13 [42mnormalization[0m >   database: datalake
2022-04-18 15:56:13 [42mnormalization[0m >   schema: zendesk_intercom
2022-04-18 15:56:13 [42mnormalization[0m >   search_path: None
2022-04-18 15:56:13 [42mnormalization[0m >   keepalives_idle: 240
2022-04-18 15:56:13 [42mnormalization[0m >   sslmode: None
2022-04-18 15:56:13 [42mnormalization[0m >   method: database
2022-04-18 15:56:13 [42mnormalization[0m >   cluster_id: None
2022-04-18 15:56:13 [42mnormalization[0m >   iam_profile: None
2022-04-18 15:56:13 [42mnormalization[0m >   iam_duration_seconds: 900
2022-04-18 15:56:13 [42mnormalization[0m >   Connection test: [OK connection ok]
2022-04-18 15:56:13 [42mnormalization[0m > 
2022-04-18 15:56:13 [42mnormalization[0m > All checks passed!
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > Forward dbt output logs to diagnose/debug errors (0):
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.127819 (MainThread): Running with dbt=0.21.1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.409221 (MainThread): running dbt with arguments Namespace(cls=<class 'dbt.task.run.RunTask'>, debug=False, defer=None, exclude=None, fail_fast=False, full_refresh=False, log_cache_events=False, log_format='default', partial_parse=None, profile=None, profiles_dir='/data/16/1/normalize', project_dir='/data/16/1/normalize', record_timing_info=None, rpc_method='run', select=None, selector_name=None, single_threaded=False, state=None, strict=False, target=None, test_new_parser=False, threads=None, use_cache=True, use_colors=None, use_experimental_parser=False, vars='{}', version_check=True, warn_error=False, which='run', write_json=True)
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.409877 (MainThread): Tracking: do not track
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.437881 (MainThread): Unable to do partial parsing because ../build/partial_parse.msgpack not found
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.474167 (MainThread): Parsing macros/get_custom_schema.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.475462 (MainThread): Parsing macros/incremental.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.483843 (MainThread): Parsing macros/should_full_refresh.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.490372 (MainThread): Parsing macros/star_intersect.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.497785 (MainThread): Parsing macros/cross_db_utils/array.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.514875 (MainThread): Parsing macros/cross_db_utils/concat.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.517658 (MainThread): Parsing macros/cross_db_utils/current_timestamp.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.518458 (MainThread): Parsing macros/cross_db_utils/datatypes.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.532207 (MainThread): Parsing macros/cross_db_utils/except.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.533261 (MainThread): Parsing macros/cross_db_utils/hash.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.534014 (MainThread): Parsing macros/cross_db_utils/json_operations.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.576203 (MainThread): Parsing macros/cross_db_utils/quote.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.578391 (MainThread): Parsing macros/cross_db_utils/surrogate_key.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.580899 (MainThread): Parsing macros/cross_db_utils/type_conversions.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.588087 (MainThread): Parsing macros/schema_tests/equal_rowcount.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.589550 (MainThread): Parsing macros/schema_tests/equality.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.597820 (MainThread): Parsing macros/adapters.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.633144 (MainThread): Parsing macros/catalog.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.648961 (MainThread): Parsing macros/relations.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.649742 (MainThread): Parsing macros/materializations/snapshot_merge.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.650552 (MainThread): Parsing macros/adapters.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.679186 (MainThread): Parsing macros/catalog.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.682080 (MainThread): Parsing macros/relations.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.683707 (MainThread): Parsing macros/materializations/snapshot_merge.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.685855 (MainThread): Parsing macros/core.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.690687 (MainThread): Parsing macros/adapters/common.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.761470 (MainThread): Parsing macros/etc/datetime.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.772750 (MainThread): Parsing macros/etc/get_custom_alias.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.774760 (MainThread): Parsing macros/etc/get_custom_database.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.776850 (MainThread): Parsing macros/etc/get_custom_schema.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.780052 (MainThread): Parsing macros/etc/is_incremental.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.782030 (MainThread): Parsing macros/etc/query.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.783309 (MainThread): Parsing macros/etc/where_subquery.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.785884 (MainThread): Parsing macros/materializations/helpers.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.799040 (MainThread): Parsing macros/materializations/test.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.807774 (MainThread): Parsing macros/materializations/common/merge.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.826843 (MainThread): Parsing macros/materializations/incremental/helpers.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.829238 (MainThread): Parsing macros/materializations/incremental/incremental.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.843687 (MainThread): Parsing macros/materializations/incremental/on_schema_change.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.868957 (MainThread): Parsing macros/materializations/seed/seed.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.898697 (MainThread): Parsing macros/materializations/snapshot/snapshot.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.940488 (MainThread): Parsing macros/materializations/snapshot/snapshot_merge.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.942789 (MainThread): Parsing macros/materializations/snapshot/strategies.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.968232 (MainThread): Parsing macros/materializations/table/table.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.977843 (MainThread): Parsing macros/materializations/view/create_or_replace_view.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.983004 (MainThread): Parsing macros/materializations/view/view.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.992166 (MainThread): Parsing macros/schema_tests/accepted_values.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.995201 (MainThread): Parsing macros/schema_tests/not_null.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.996820 (MainThread): Parsing macros/schema_tests/relationships.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:24.998983 (MainThread): Parsing macros/schema_tests/unique.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.000808 (MainThread): Parsing macros/cross_db_utils/_is_ephemeral.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.003311 (MainThread): Parsing macros/cross_db_utils/_is_relation.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.004826 (MainThread): Parsing macros/cross_db_utils/cast_bool_to_text.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.006767 (MainThread): Parsing macros/cross_db_utils/concat.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.008111 (MainThread): Parsing macros/cross_db_utils/current_timestamp.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.014032 (MainThread): Parsing macros/cross_db_utils/datatypes.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.022371 (MainThread): Parsing macros/cross_db_utils/date_trunc.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.024645 (MainThread): Parsing macros/cross_db_utils/dateadd.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.029332 (MainThread): Parsing macros/cross_db_utils/datediff.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.045541 (MainThread): Parsing macros/cross_db_utils/except.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.047101 (MainThread): Parsing macros/cross_db_utils/hash.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.049153 (MainThread): Parsing macros/cross_db_utils/identifier.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.053143 (MainThread): Parsing macros/cross_db_utils/intersect.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.054612 (MainThread): Parsing macros/cross_db_utils/last_day.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.059389 (MainThread): Parsing macros/cross_db_utils/length.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.061382 (MainThread): Parsing macros/cross_db_utils/literal.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.062801 (MainThread): Parsing macros/cross_db_utils/position.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.065643 (MainThread): Parsing macros/cross_db_utils/replace.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.067308 (MainThread): Parsing macros/cross_db_utils/right.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.070329 (MainThread): Parsing macros/cross_db_utils/safe_cast.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.072882 (MainThread): Parsing macros/cross_db_utils/split_part.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.075340 (MainThread): Parsing macros/cross_db_utils/width_bucket.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.082129 (MainThread): Parsing macros/jinja_helpers/log_info.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.083669 (MainThread): Parsing macros/jinja_helpers/pretty_log_format.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.085532 (MainThread): Parsing macros/jinja_helpers/pretty_time.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.087300 (MainThread): Parsing macros/jinja_helpers/slugify.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.089031 (MainThread): Parsing macros/materializations/insert_by_period_materialization.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.122369 (MainThread): Parsing macros/schema_tests/accepted_range.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.125877 (MainThread): Parsing macros/schema_tests/at_least_one.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.127601 (MainThread): Parsing macros/schema_tests/cardinality_equality.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.130239 (MainThread): Parsing macros/schema_tests/equal_rowcount.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.132548 (MainThread): Parsing macros/schema_tests/equality.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.138042 (MainThread): Parsing macros/schema_tests/expression_is_true.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.140528 (MainThread): Parsing macros/schema_tests/fewer_rows_than.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.142691 (MainThread): Parsing macros/schema_tests/mutually_exclusive_ranges.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.154235 (MainThread): Parsing macros/schema_tests/not_accepted_values.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.157158 (MainThread): Parsing macros/schema_tests/not_constant.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.158827 (MainThread): Parsing macros/schema_tests/not_null_proportion.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.161750 (MainThread): Parsing macros/schema_tests/recency.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.164066 (MainThread): Parsing macros/schema_tests/relationships_where.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.167109 (MainThread): Parsing macros/schema_tests/sequential_values.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.170618 (MainThread): Parsing macros/schema_tests/test_not_null_where.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.172569 (MainThread): Parsing macros/schema_tests/test_unique_where.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.174484 (MainThread): Parsing macros/schema_tests/unique_combination_of_columns.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.178225 (MainThread): Parsing macros/sql/date_spine.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.184534 (MainThread): Parsing macros/sql/generate_series.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.190244 (MainThread): Parsing macros/sql/get_column_values.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.196442 (MainThread): Parsing macros/sql/get_query_results_as_dict.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.199666 (MainThread): Parsing macros/sql/get_relations_by_pattern.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.204304 (MainThread): Parsing macros/sql/get_relations_by_prefix.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.208880 (MainThread): Parsing macros/sql/get_tables_by_pattern_sql.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.218181 (MainThread): Parsing macros/sql/get_tables_by_prefix_sql.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.220419 (MainThread): Parsing macros/sql/groupby.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.222198 (MainThread): Parsing macros/sql/haversine_distance.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.230157 (MainThread): Parsing macros/sql/nullcheck.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.232359 (MainThread): Parsing macros/sql/nullcheck_table.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.234522 (MainThread): Parsing macros/sql/pivot.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.239869 (MainThread): Parsing macros/sql/safe_add.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.242078 (MainThread): Parsing macros/sql/star.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.247066 (MainThread): Parsing macros/sql/surrogate_key.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.251710 (MainThread): Parsing macros/sql/union.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.262604 (MainThread): Parsing macros/sql/unpivot.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.272920 (MainThread): Parsing macros/web/get_url_host.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.275295 (MainThread): Parsing macros/web/get_url_parameter.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.277403 (MainThread): Parsing macros/web/get_url_path.sql
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.842072 (MainThread): Acquiring new redshift connection "model.airbyte_utils.brands_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.897559 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.903183 (MainThread): Acquiring new redshift connection "model.airbyte_utils.brands_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.931607 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.933438 (MainThread): Acquiring new redshift connection "model.airbyte_utils.brands_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.967840 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.969730 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.987246 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.993003 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:25.994785 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.008221 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.010022 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.021562 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.025174 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tags_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.036559 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.038427 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tags_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.046489 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.048207 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tags_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.056135 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.057829 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.092300 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.094151 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.115457 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.117281 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.131731 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.133559 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.144811 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.146632 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.169532 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.178180 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.181284 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.181984 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.182675 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.183361 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.184046 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.187120 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.188896 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.211443 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.213266 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.239411 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.256898 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.259918 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.261718 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.284139 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.285988 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.295280 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.296997 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.304203 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.305876 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.315975 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.317725 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.343048 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.346207 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.356224 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.357913 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.366901 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.368631 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.384136 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.385952 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.395503 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.397148 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.406505 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.408138 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.418016 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.420639 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.428248 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.429941 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.437823 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.439481 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.449072 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.450719 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.458059 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.459740 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.468003 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.470889 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.534377 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.536218 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.544240 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.546521 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.554479 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.556517 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.564984 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.566637 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.574126 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.575777 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.583807 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.585526 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.595849 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.597512 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.604807 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.606719 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.614804 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.616538 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.627057 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.629006 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.636338 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.637980 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.647334 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.649002 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.657623 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.659249 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.666543 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.668645 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.676580 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.678240 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.684101 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.687062 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.688719 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.695944 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.697620 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.707255 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.709002 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_custom_fields_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.722528 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.724289 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_custom_fields_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.732051 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.733918 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_custom_fields_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.742099 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.743803 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_satisfaction_rating_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.766012 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.767770 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_satisfaction_rating_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.780594 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.782383 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_satisfaction_rating_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.793851 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.795997 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.809567 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.811299 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.819574 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.822226 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.832441 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.834150 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.847972 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.849723 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.857869 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.859572 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.868362 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.870272 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.877292 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.878785 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.881186 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.882860 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.891936 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.893626 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.904181 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.905922 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_to_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.924014 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.926575 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_to_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.938230 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.939941 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_to_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.950254 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.951937 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_from_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.981749 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.983484 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_from_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.997104 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:26.998911 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_from_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.012093 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.013841 (MainThread): Acquiring new redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.021486 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.024202 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.031207 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.032911 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.041764 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.043522 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.051194 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.052917 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.060341 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.062009 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.070242 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.071935 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.079114 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.080809 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.088294 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.089962 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.099066 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.100805 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.108149 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.109931 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.116917 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.118593 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.125514 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.127188 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.133965 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.135609 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.142409 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.144048 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.152482 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.154145 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.160842 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.162489 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.169154 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.170791 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.177463 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.179112 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_custom_fields".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.185764 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.187426 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_satisfaction_rating".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.194146 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.195782 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.204122 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.205785 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.212431 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.214262 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.221590 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.223238 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_to".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.229917 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.231558 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_from".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.238221 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.239876 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.254781 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.256532 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.275647 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.277489 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.292991 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.295136 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.309314 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.311014 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.328204 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.329938 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.341252 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.343052 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.358456 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.360098 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.380535 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.496746 (MainThread): [WARNING]: Configuration paths exist in your dbt_project.yml file which do not apply to any resources.
2022-04-18 15:56:14 [42mnormalization[0m > There are 1 unused configuration paths:
2022-04-18 15:56:14 [42mnormalization[0m > - models.airbyte_utils.generated.airbyte_tables
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.594049 (MainThread): write_gpickle is deprecated and will be removed in 3.0.Use ``pickle.dump(G, path, protocol)``
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.595613 (MainThread): Found 104 models, 0 tests, 0 snapshots, 0 analyses, 520 macros, 0 operations, 0 seed files, 7 sources, 0 exposures
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.603647 (MainThread): 
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.604696 (MainThread): Acquiring new redshift connection "master".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.608023 (ThreadPoolExecutor-0_0): Acquiring new redshift connection "list_datalake".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.614219 (ThreadPoolExecutor-0_1): Acquiring new redshift connection "list_datalake".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.626244 (ThreadPoolExecutor-0_1): Using redshift connection "list_datalake".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.626419 (ThreadPoolExecutor-0_1): On list_datalake: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "list_datalake"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select distinct nspname from pg_namespace
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.626571 (ThreadPoolExecutor-0_1): Opening a new connection, currently in state init
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.626700 (ThreadPoolExecutor-0_1): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.623346 (ThreadPoolExecutor-0_0): Using redshift connection "list_datalake".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.627589 (ThreadPoolExecutor-0_0): On list_datalake: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "list_datalake"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select distinct nspname from pg_namespace
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.627749 (ThreadPoolExecutor-0_0): Opening a new connection, currently in state init
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.628070 (ThreadPoolExecutor-0_0): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.674092 (ThreadPoolExecutor-0_0): SQL status: SELECT in 0.05 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.676775 (ThreadPoolExecutor-0_0): On list_datalake: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.676942 (ThreadPoolExecutor-0_1): SQL status: SELECT in 0.05 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.679406 (ThreadPoolExecutor-0_1): On list_datalake: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.683998 (ThreadPoolExecutor-1_0): Acquiring new redshift connection "list_datalake_zendesk_intercom".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.697893 (ThreadPoolExecutor-1_1): Acquiring new redshift connection "list_datalake__airbyte_zendesk_intercom".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.706849 (ThreadPoolExecutor-1_1): Using redshift connection "list_datalake__airbyte_zendesk_intercom".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.710347 (ThreadPoolExecutor-1_0): Using redshift connection "list_datalake_zendesk_intercom".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.710538 (ThreadPoolExecutor-1_1): On list_datalake__airbyte_zendesk_intercom: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.710745 (ThreadPoolExecutor-1_0): On list_datalake_zendesk_intercom: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.710937 (ThreadPoolExecutor-1_1): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.711110 (ThreadPoolExecutor-1_0): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.711272 (ThreadPoolExecutor-1_1): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.711428 (ThreadPoolExecutor-1_0): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.739612 (ThreadPoolExecutor-1_0): SQL status: BEGIN in 0.03 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.739922 (ThreadPoolExecutor-1_0): Using redshift connection "list_datalake_zendesk_intercom".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.740057 (ThreadPoolExecutor-1_0): On list_datalake_zendesk_intercom: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "list_datalake_zendesk_intercom"} */
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >       'datalake' as database,
2022-04-18 15:56:14 [42mnormalization[0m >       tablename as name,
2022-04-18 15:56:14 [42mnormalization[0m >       schemaname as schema,
2022-04-18 15:56:14 [42mnormalization[0m >       'table' as type
2022-04-18 15:56:14 [42mnormalization[0m >     from pg_tables
2022-04-18 15:56:14 [42mnormalization[0m >     where schemaname ilike 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     union all
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       'datalake' as database,
2022-04-18 15:56:14 [42mnormalization[0m >       viewname as name,
2022-04-18 15:56:14 [42mnormalization[0m >       schemaname as schema,
2022-04-18 15:56:14 [42mnormalization[0m >       'view' as type
2022-04-18 15:56:14 [42mnormalization[0m >     from pg_views
2022-04-18 15:56:14 [42mnormalization[0m >     where schemaname ilike 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.740748 (ThreadPoolExecutor-1_1): SQL status: BEGIN in 0.03 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.740945 (ThreadPoolExecutor-1_1): Using redshift connection "list_datalake__airbyte_zendesk_intercom".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.741075 (ThreadPoolExecutor-1_1): On list_datalake__airbyte_zendesk_intercom: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "list_datalake__airbyte_zendesk_intercom"} */
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >       'datalake' as database,
2022-04-18 15:56:14 [42mnormalization[0m >       tablename as name,
2022-04-18 15:56:14 [42mnormalization[0m >       schemaname as schema,
2022-04-18 15:56:14 [42mnormalization[0m >       'table' as type
2022-04-18 15:56:14 [42mnormalization[0m >     from pg_tables
2022-04-18 15:56:14 [42mnormalization[0m >     where schemaname ilike '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     union all
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       'datalake' as database,
2022-04-18 15:56:14 [42mnormalization[0m >       viewname as name,
2022-04-18 15:56:14 [42mnormalization[0m >       schemaname as schema,
2022-04-18 15:56:14 [42mnormalization[0m >       'view' as type
2022-04-18 15:56:14 [42mnormalization[0m >     from pg_views
2022-04-18 15:56:14 [42mnormalization[0m >     where schemaname ilike '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.795102 (ThreadPoolExecutor-1_0): SQL status: SELECT in 0.05 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.795461 (ThreadPoolExecutor-1_1): SQL status: SELECT in 0.05 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.798089 (ThreadPoolExecutor-1_0): On list_datalake_zendesk_intercom: ROLLBACK
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.799544 (ThreadPoolExecutor-1_1): On list_datalake__airbyte_zendesk_intercom: ROLLBACK
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.803179 (ThreadPoolExecutor-1_1): On list_datalake__airbyte_zendesk_intercom: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.803356 (ThreadPoolExecutor-1_0): On list_datalake_zendesk_intercom: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.813669 (MainThread): Using redshift connection "master".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.813837 (MainThread): On master: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.813982 (MainThread): Opening a new connection, currently in state init
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.814109 (MainThread): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.847227 (MainThread): SQL status: BEGIN in 0.03 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.847481 (MainThread): Using redshift connection "master".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.847621 (MainThread): On master: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "master"} */
2022-04-18 15:56:14 [42mnormalization[0m > with relation as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >             pg_rewrite.ev_class as class,
2022-04-18 15:56:14 [42mnormalization[0m >             pg_rewrite.oid as id
2022-04-18 15:56:14 [42mnormalization[0m >         from pg_rewrite
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m >     class as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >             oid as id,
2022-04-18 15:56:14 [42mnormalization[0m >             relname as name,
2022-04-18 15:56:14 [42mnormalization[0m >             relnamespace as schema,
2022-04-18 15:56:14 [42mnormalization[0m >             relkind as kind
2022-04-18 15:56:14 [42mnormalization[0m >         from pg_class
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m >     dependency as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >             pg_depend.objid as id,
2022-04-18 15:56:14 [42mnormalization[0m >             pg_depend.refobjid as ref
2022-04-18 15:56:14 [42mnormalization[0m >         from pg_depend
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m >     schema as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >             pg_namespace.oid as id,
2022-04-18 15:56:14 [42mnormalization[0m >             pg_namespace.nspname as name
2022-04-18 15:56:14 [42mnormalization[0m >         from pg_namespace
2022-04-18 15:56:14 [42mnormalization[0m >         where nspname != 'information_schema' and nspname not like 'pg\_%'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m >     referenced as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >             relation.id AS id,
2022-04-18 15:56:14 [42mnormalization[0m >             referenced_class.name ,
2022-04-18 15:56:14 [42mnormalization[0m >             referenced_class.schema ,
2022-04-18 15:56:14 [42mnormalization[0m >             referenced_class.kind
2022-04-18 15:56:14 [42mnormalization[0m >         from relation
2022-04-18 15:56:14 [42mnormalization[0m >         join class as referenced_class on relation.class=referenced_class.id
2022-04-18 15:56:14 [42mnormalization[0m >         where referenced_class.kind in ('r', 'v')
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m >     relationships as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >             referenced.name as referenced_name,
2022-04-18 15:56:14 [42mnormalization[0m >             referenced.schema as referenced_schema_id,
2022-04-18 15:56:14 [42mnormalization[0m >             dependent_class.name as dependent_name,
2022-04-18 15:56:14 [42mnormalization[0m >             dependent_class.schema as dependent_schema_id,
2022-04-18 15:56:14 [42mnormalization[0m >             referenced.kind as kind
2022-04-18 15:56:14 [42mnormalization[0m >         from referenced
2022-04-18 15:56:14 [42mnormalization[0m >         join dependency on referenced.id=dependency.id
2022-04-18 15:56:14 [42mnormalization[0m >         join class as dependent_class on dependency.ref=dependent_class.id
2022-04-18 15:56:14 [42mnormalization[0m >         where
2022-04-18 15:56:14 [42mnormalization[0m >             (referenced.name != dependent_class.name or
2022-04-18 15:56:14 [42mnormalization[0m >              referenced.schema != dependent_class.schema)
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >         referenced_schema.name as referenced_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         relationships.referenced_name as referenced_name,
2022-04-18 15:56:14 [42mnormalization[0m >         dependent_schema.name as dependent_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         relationships.dependent_name as dependent_name
2022-04-18 15:56:14 [42mnormalization[0m >     from relationships
2022-04-18 15:56:14 [42mnormalization[0m >     join schema as dependent_schema on relationships.dependent_schema_id=dependent_schema.id
2022-04-18 15:56:14 [42mnormalization[0m >     join schema as referenced_schema on relationships.referenced_schema_id=referenced_schema.id
2022-04-18 15:56:14 [42mnormalization[0m >     group by referenced_schema, referenced_name, dependent_schema, dependent_name
2022-04-18 15:56:14 [42mnormalization[0m >     order by referenced_schema, referenced_name, dependent_schema, dependent_name;
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.913592 (MainThread): SQL status: SELECT in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.919875 (MainThread): On master: ROLLBACK
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.922673 (MainThread): Using redshift connection "master".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.922826 (MainThread): On master: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.928056 (MainThread): SQL status: BEGIN in 0.01 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.928224 (MainThread): On master: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.928379 (MainThread): Using redshift connection "master".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.928495 (MainThread): On master: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.930959 (MainThread): SQL status: COMMIT in 0.00 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.931127 (MainThread): On master: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.931727 (MainThread): 15:55:27 | Concurrency: 4 threads (target='prod')
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.931922 (MainThread): 15:55:27 | 
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.941139 (Thread-1): Began running node model.airbyte_utils.brands_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.941395 (Thread-2): Began running node model.airbyte_utils.sla_policies_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.941609 (Thread-3): Began running node model.airbyte_utils.tags_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.941784 (Thread-4): Began running node model.airbyte_utils.ticket_fields_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.942229 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.brands_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.944084 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.944561 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.tags_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.945003 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.945247 (Thread-1): Compiling model.airbyte_utils.brands_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.945443 (Thread-2): Compiling model.airbyte_utils.sla_policies_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.945616 (Thread-3): Compiling model.airbyte_utils.tags_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.945793 (Thread-4): Compiling model.airbyte_utils.ticket_fields_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.984022 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.987900 (Thread-1): Writing injected SQL for node "model.airbyte_utils.brands_ab1"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:27.993063 (Thread-3): Writing injected SQL for node "model.airbyte_utils.tags_ab1"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.013338 (Thread-2): Writing injected SQL for node "model.airbyte_utils.sla_policies_ab1"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.032824 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_fields_ab1"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.033811 (Thread-3): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.034699 (Thread-3): Finished running node model.airbyte_utils.tags_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.034033 (Thread-1): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.036106 (Thread-1): Finished running node model.airbyte_utils.brands_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.035328 (Thread-2): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.035085 (Thread-3): Began running node model.airbyte_utils.ticket_metric_events_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.037776 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.036714 (Thread-1): Began running node model.airbyte_utils.ticket_metrics_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.037286 (Thread-2): Finished running node model.airbyte_utils.sla_policies_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.036446 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.039404 (Thread-4): Finished running node model.airbyte_utils.ticket_fields_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.038449 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.038681 (Thread-2): Began running node model.airbyte_utils.tickets_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.038074 (Thread-3): Compiling model.airbyte_utils.ticket_metric_events_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.039642 (Thread-4): Began running node model.airbyte_utils.tags_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.039902 (Thread-1): Compiling model.airbyte_utils.ticket_metrics_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.040582 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.tickets_ab1".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.051369 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.tags_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.055281 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events_ab1"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.071107 (Thread-2): Compiling model.airbyte_utils.tickets_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.077142 (Thread-4): Compiling model.airbyte_utils.tags_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.080676 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.091623 (Thread-3): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.118705 (Thread-3): Finished running node model.airbyte_utils.ticket_metric_events_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.133545 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.136370 (Thread-4): Writing injected SQL for node "model.airbyte_utils.tags_ab2"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.136631 (Thread-3): Began running node model.airbyte_utils.brands_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.139347 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.brands_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.138661 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.137045 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.139595 (Thread-3): Compiling model.airbyte_utils.brands_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.139828 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.140728 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.162424 (Thread-4): Finished running node model.airbyte_utils.tags_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.178493 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.184691 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.200001 (Thread-4): Began running node model.airbyte_utils.sla_policies_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.201016 (Thread-3): Writing injected SQL for node "model.airbyte_utils.brands_ab2"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.203306 (Thread-2): Writing injected SQL for node "model.airbyte_utils.tickets_ab1"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.204371 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.206145 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.204918 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.206341 (Thread-3): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.207686 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_ab1"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.207917 (Thread-2): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.208117 (Thread-4): Compiling model.airbyte_utils.sla_policies_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.208559 (Thread-3): Finished running node model.airbyte_utils.brands_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.209066 (Thread-2): Finished running node model.airbyte_utils.tickets_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.219771 (Thread-1): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.220037 (Thread-3): Began running node model.airbyte_utils.ticket_fields_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.231417 (Thread-2): Began running node model.airbyte_utils.ticket_metric_events_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.233725 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_ab2"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.234424 (Thread-1): Finished running node model.airbyte_utils.ticket_metrics_ab1
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.235669 (Thread-1): Began running node model.airbyte_utils.tags_ab3
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.235337 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.234953 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.236315 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.tags_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.236556 (Thread-2): Compiling model.airbyte_utils.ticket_metric_events_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.236842 (Thread-3): Compiling model.airbyte_utils.ticket_fields_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.237030 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.237235 (Thread-1): Compiling model.airbyte_utils.tags_ab3
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.263852 (Thread-4): Finished running node model.airbyte_utils.sla_policies_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.276089 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events_ab2"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.297381 (Thread-4): Began running node model.airbyte_utils.brands_ab3
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.301183 (Thread-1): Writing injected SQL for node "model.airbyte_utils.tags_ab3"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.324194 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.brands_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.325819 (Thread-4): Compiling model.airbyte_utils.brands_ab3
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.325252 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_fields_ab2"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.326026 (Thread-2): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.347331 (Thread-1): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.348552 (Thread-1): Finished running node model.airbyte_utils.tags_ab3
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.348006 (Thread-2): Finished running node model.airbyte_utils.ticket_metric_events_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.354051 (Thread-1): Began running node model.airbyte_utils.tickets_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.369616 (Thread-2): Began running node model.airbyte_utils.ticket_metrics_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.370227 (Thread-3): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.373002 (Thread-4): Writing injected SQL for node "model.airbyte_utils.brands_ab3"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.373817 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.tickets_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.374257 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_ab2".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.375386 (Thread-2): Compiling model.airbyte_utils.ticket_metrics_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.374744 (Thread-3): Finished running node model.airbyte_utils.ticket_fields_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.387811 (Thread-3): Began running node model.airbyte_utils.sla_policies_ab3
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.382272 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.409392 (Thread-4): Finished running node model.airbyte_utils.brands_ab3
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.414898 (Thread-4): Began running node model.airbyte_utils.ticket_metric_events_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.415285 (Thread-4): 15:55:28 | 1 of 33 START view model _airbyte_zendesk_intercom.ticket_metric_events_stg.................................. [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.388437 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab3".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.375100 (Thread-1): Compiling model.airbyte_utils.tickets_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.421132 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.426692 (Thread-3): Compiling model.airbyte_utils.sla_policies_ab3
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.438832 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_ab2"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.449671 (Thread-4): Compiling model.airbyte_utils.ticket_metric_events_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.510448 (Thread-3): Writing injected SQL for node "model.airbyte_utils.sla_policies_ab3"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.531471 (Thread-2): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.542567 (Thread-2): Finished running node model.airbyte_utils.ticket_metrics_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.542825 (Thread-2): Began running node model.airbyte_utils.tags
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.543138 (Thread-1): Writing injected SQL for node "model.airbyte_utils.tickets_ab2"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.543455 (Thread-2): 15:55:28 | 2 of 33 START incremental model zendesk_intercom.tags........................................................ [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.548942 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.548107 (Thread-3): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.549705 (Thread-3): Finished running node model.airbyte_utils.sla_policies_ab3
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.549924 (Thread-3): Began running node model.airbyte_utils.ticket_fields_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.550242 (Thread-3): 15:55:28 | 3 of 33 START view model _airbyte_zendesk_intercom.ticket_fields_stg......................................... [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.550824 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.551024 (Thread-3): Compiling model.airbyte_utils.ticket_fields_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.550431 (Thread-1): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.549201 (Thread-2): Compiling model.airbyte_utils.tags
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.547805 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.557031 (Thread-1): Finished running node model.airbyte_utils.tickets_ab2
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.595750 (Thread-1): Began running node model.airbyte_utils.brands
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.606773 (Thread-1): 15:55:28 | 4 of 33 START incremental model zendesk_intercom.brands...................................................... [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.624260 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.633561 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.649863 (Thread-1): Compiling model.airbyte_utils.brands
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.633871 (Thread-2): On model.airbyte_utils.tags: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.654769 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_fields_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.660911 (Thread-2): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.661103 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.668175 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.668528 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.679192 (Thread-1): On model.airbyte_utils.brands: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.695104 (Thread-3): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.700661 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_metric_events_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.701466 (Thread-1): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.705517 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.ticket_fields_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.705926 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.706926 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.707135 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.707298 (Thread-3): On model.airbyte_utils.ticket_fields_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.707473 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.707649 (Thread-3): Opening a new connection, currently in state init
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.707812 (Thread-4): Opening a new connection, currently in state init
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.707965 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.708356 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.726898 (Thread-2): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.727106 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.727242 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.763124 (Thread-3): SQL status: BEGIN in 0.06 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.763369 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.763501 (Thread-3): On model.airbyte_utils.ticket_fields_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   create view "datalake"._airbyte_zendesk_intercom."ticket_fields_stg__dbt_tmp" as (
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > with __dbt__cte__ticket_fields_ab1 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom._airbyte_raw_ticket_fields
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'tag', true) != '' then json_extract_path_text(_airbyte_data, 'tag', true) end as "tag",
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'type', true) != '' then json_extract_path_text(_airbyte_data, 'type', true) end as type,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'title', true) != '' then json_extract_path_text(_airbyte_data, 'title', true) end as title,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'active', true) != '' then json_extract_path_text(_airbyte_data, 'active', true) end as active,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'position', true) != '' then json_extract_path_text(_airbyte_data, 'position', true) end as position,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'required', true) != '' then json_extract_path_text(_airbyte_data, 'required', true) end as required,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'raw_title', true) != '' then json_extract_path_text(_airbyte_data, 'raw_title', true) end as raw_title,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'removable', true) != '' then json_extract_path_text(_airbyte_data, 'removable', true) end as removable,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'description', true) != '' then json_extract_path_text(_airbyte_data, 'description', true) end as description,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'sub_type_id', true) != '' then json_extract_path_text(_airbyte_data, 'sub_type_id', true) end as sub_type_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'raw_description', true) != '' then json_extract_path_text(_airbyte_data, 'raw_description', true) end as raw_description,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'title_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'title_in_portal', true) end as title_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'agent_description', true) != '' then json_extract_path_text(_airbyte_data, 'agent_description', true) end as agent_description,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'visible_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'visible_in_portal', true) end as visible_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'editable_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'editable_in_portal', true) end as editable_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'required_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'required_in_portal', true) end as required_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'raw_title_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'raw_title_in_portal', true) end as raw_title_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'collapsed_for_agents', true) != '' then json_extract_path_text(_airbyte_data, 'collapsed_for_agents', true) end as collapsed_for_agents,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'custom_field_options', true) as custom_field_options,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'system_field_options', true) as system_field_options,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'regexp_for_validation', true) != '' then json_extract_path_text(_airbyte_data, 'regexp_for_validation', true) end as regexp_for_validation,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from "datalake".zendesk_intercom._airbyte_raw_ticket_fields as table_alias
2022-04-18 15:56:14 [42mnormalization[0m > -- ticket_fields
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__ticket_fields_ab2 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_fields_ab1
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     cast(id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast("tag" as varchar) as "tag",
2022-04-18 15:56:14 [42mnormalization[0m >     cast(url as varchar) as url,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(type as varchar) as type,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(title as varchar) as title,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(active, 'true', '1', 'false', '0')::integer as boolean) as active,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(position as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as position,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(required, 'true', '1', 'false', '0')::integer as boolean) as required,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(raw_title as varchar) as raw_title,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(removable, 'true', '1', 'false', '0')::integer as boolean) as removable,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(created_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(updated_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(description as varchar) as description,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(sub_type_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as sub_type_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(raw_description as varchar) as raw_description,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(title_in_portal as varchar) as title_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(agent_description as varchar) as agent_description,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(visible_in_portal, 'true', '1', 'false', '0')::integer as boolean) as visible_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(editable_in_portal, 'true', '1', 'false', '0')::integer as boolean) as editable_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(required_in_portal, 'true', '1', 'false', '0')::integer as boolean) as required_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(raw_title_in_portal as varchar) as raw_title_in_portal,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(collapsed_for_agents, 'true', '1', 'false', '0')::integer as boolean) as collapsed_for_agents,
2022-04-18 15:56:14 [42mnormalization[0m >     custom_field_options,
2022-04-18 15:56:14 [42mnormalization[0m >     system_field_options,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(regexp_for_validation as varchar) as regexp_for_validation,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__ticket_fields_ab1
2022-04-18 15:56:14 [42mnormalization[0m > -- ticket_fields
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > )-- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_fields_ab2
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast("tag" as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast(type as varchar), '') || '-' || coalesce(cast(title as varchar), '') || '-' || coalesce(cast(case when active then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(position as varchar), '') || '-' || coalesce(cast(case when required then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(raw_title as varchar), '') || '-' || coalesce(cast(case when removable then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(description as varchar), '') || '-' || coalesce(cast(sub_type_id as varchar), '') || '-' || coalesce(cast(raw_description as varchar), '') || '-' || coalesce(cast(title_in_portal as varchar), '') || '-' || coalesce(cast(agent_description as varchar), '') || '-' || coalesce(cast(case when visible_in_portal then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(case when editable_in_portal then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(case when required_in_portal then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(raw_title_in_portal as varchar), '') || '-' || coalesce(cast(case when collapsed_for_agents then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(custom_field_options as varchar), '') || '-' || coalesce(cast(system_field_options as varchar), '') || '-' || coalesce(cast(regexp_for_validation as varchar), '') as varchar)) as _airbyte_ticket_fields_hashid,
2022-04-18 15:56:14 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__ticket_fields_ab2 tmp
2022-04-18 15:56:14 [42mnormalization[0m > -- ticket_fields
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   ) ;
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.769486 (Thread-1): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.769663 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.769792 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.781624 (Thread-4): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.781823 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.781955 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   create view "datalake"._airbyte_zendesk_intercom."ticket_metric_events_stg__dbt_tmp" as (
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > with __dbt__cte__ticket_metric_events_ab1 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom._airbyte_raw_ticket_metric_events
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'time', true) != '' then json_extract_path_text(_airbyte_data, 'time', true) end as "time",
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'type', true) != '' then json_extract_path_text(_airbyte_data, 'type', true) end as type,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'metric', true) != '' then json_extract_path_text(_airbyte_data, 'metric', true) end as metric,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'ticket_id', true) != '' then json_extract_path_text(_airbyte_data, 'ticket_id', true) end as ticket_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'instance_id', true) != '' then json_extract_path_text(_airbyte_data, 'instance_id', true) end as instance_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from "datalake".zendesk_intercom._airbyte_raw_ticket_metric_events as table_alias
2022-04-18 15:56:14 [42mnormalization[0m > -- ticket_metric_events
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__ticket_metric_events_ab2 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metric_events_ab1
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     cast(id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast("time" as varchar) as "time",
2022-04-18 15:56:14 [42mnormalization[0m >     cast(type as varchar) as type,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(metric as varchar) as metric,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(ticket_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as ticket_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(instance_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as instance_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__ticket_metric_events_ab1
2022-04-18 15:56:14 [42mnormalization[0m > -- ticket_metric_events
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > )-- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metric_events_ab2
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast("time" as varchar), '') || '-' || coalesce(cast(type as varchar), '') || '-' || coalesce(cast(metric as varchar), '') || '-' || coalesce(cast(ticket_id as varchar), '') || '-' || coalesce(cast(instance_id as varchar), '') as varchar)) as _airbyte_ticket_metric_events_hashid,
2022-04-18 15:56:14 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__ticket_metric_events_ab2 tmp
2022-04-18 15:56:14 [42mnormalization[0m > -- ticket_metric_events
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   ) ;
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.826587 (Thread-4): SQL status: CREATE VIEW in 0.04 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.835907 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.836088 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > alter table "datalake"._airbyte_zendesk_intercom."ticket_metric_events_stg__dbt_tmp" rename to "ticket_metric_events_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.837861 (Thread-3): SQL status: CREATE VIEW in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.841386 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.841567 (Thread-3): On model.airbyte_utils.ticket_fields_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > alter table "datalake"._airbyte_zendesk_intercom."ticket_fields_stg__dbt_tmp" rename to "ticket_fields_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.841899 (Thread-4): SQL status: ALTER TABLE in 0.01 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.851687 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.851873 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.852027 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.852238 (Thread-3): SQL status: ALTER TABLE in 0.01 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.853599 (Thread-3): On model.airbyte_utils.ticket_fields_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.853749 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:28.853872 (Thread-3): On model.airbyte_utils.ticket_fields_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.001633 (Thread-4): SQL status: COMMIT in 0.15 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.002321 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.002530 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.002789 (Thread-3): SQL status: COMMIT in 0.15 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.006511 (Thread-4): SQL status: BEGIN in 0.00 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.016189 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.016382 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > drop view if exists "datalake"._airbyte_zendesk_intercom."ticket_metric_events_stg__dbt_backup" cascade
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.020197 (Thread-4): SQL status: DROP VIEW in 0.00 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.021310 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.021519 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.021697 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.083433 (Thread-2): SQL status: SELECT in 0.36 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.100118 (Thread-2): Writing injected SQL for node "model.airbyte_utils.tags"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.100423 (Thread-4): SQL status: COMMIT in 0.08 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.100898 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.101047 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.101466 (Thread-2): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.112207 (Thread-4): SQL status: BEGIN in 0.01 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.112964 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.113171 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: ROLLBACK
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.113404 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.113553 (Thread-3): On model.airbyte_utils.ticket_fields_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.129770 (Thread-3): SQL status: BEGIN in 0.02 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.135393 (Thread-4): On model.airbyte_utils.ticket_metric_events_stg: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.136481 (Thread-4): 15:55:29 | 1 of 33 OK created view model _airbyte_zendesk_intercom.ticket_metric_events_stg............................. [CREATE VIEW in 0.72s]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.142089 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.142266 (Thread-3): On model.airbyte_utils.ticket_fields_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > drop view if exists "datalake"._airbyte_zendesk_intercom."ticket_fields_stg__dbt_backup" cascade
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.147741 (Thread-4): Finished running node model.airbyte_utils.ticket_metric_events_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.148001 (Thread-3): SQL status: DROP VIEW in 0.01 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.148245 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.169326 (Thread-1): SQL status: SELECT in 0.40 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.170505 (Thread-3): On model.airbyte_utils.ticket_fields_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.173599 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.173998 (Thread-4): 15:55:29 | 5 of 33 START view model _airbyte_zendesk_intercom.ticket_metrics_stg........................................ [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.187082 (Thread-1): Writing injected SQL for node "model.airbyte_utils.brands"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.187313 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.187515 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.188235 (Thread-1): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.188515 (Thread-3): On model.airbyte_utils.ticket_fields_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.194326 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.196503 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.196809 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.196990 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.259196 (Thread-3): SQL status: COMMIT in 0.06 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.286259 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.296890 (Thread-3): On model.airbyte_utils.ticket_fields_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.316245 (Thread-3): SQL status: BEGIN in 0.01 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.321426 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.322217 (Thread-3): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.322619 (Thread-3): On model.airbyte_utils.ticket_fields_stg: ROLLBACK
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.322981 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.327609 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.327833 (Thread-3): On model.airbyte_utils.ticket_fields_stg: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.328321 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.328989 (Thread-3): 15:55:29 | 3 of 33 OK created view model _airbyte_zendesk_intercom.ticket_fields_stg.................................... [CREATE VIEW in 0.78s]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.329252 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.329566 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.329718 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.330198 (Thread-3): Finished running node model.airbyte_utils.ticket_fields_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.330426 (Thread-3): Began running node model.airbyte_utils.sla_policies
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.330935 (Thread-3): 15:55:29 | 6 of 33 START incremental model zendesk_intercom.sla_policies................................................ [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.331570 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.331765 (Thread-3): Compiling model.airbyte_utils.sla_policies
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.341600 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.341778 (Thread-3): On model.airbyte_utils.sla_policies: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.341918 (Thread-3): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.342035 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.402469 (Thread-4): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.402792 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.402934 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   create view "datalake"._airbyte_zendesk_intercom."ticket_metrics_stg__dbt_tmp" as (
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > with __dbt__cte__ticket_metrics_ab1 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom._airbyte_raw_ticket_metrics
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'time', true) != '' then json_extract_path_text(_airbyte_data, 'time', true) end as "time",
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'type', true) != '' then json_extract_path_text(_airbyte_data, 'type', true) end as type,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'metric', true) != '' then json_extract_path_text(_airbyte_data, 'metric', true) end as metric,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'status', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'status', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as status,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'reopens', true) != '' then json_extract_path_text(_airbyte_data, 'reopens', true) end as reopens,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'replies', true) != '' then json_extract_path_text(_airbyte_data, 'replies', true) end as replies,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'solved_at', true) != '' then json_extract_path_text(_airbyte_data, 'solved_at', true) end as solved_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'ticket_id', true) != '' then json_extract_path_text(_airbyte_data, 'ticket_id', true) end as ticket_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'assigned_at', true) != '' then json_extract_path_text(_airbyte_data, 'assigned_at', true) end as assigned_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'instance_id', true) != '' then json_extract_path_text(_airbyte_data, 'instance_id', true) end as instance_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'group_stations', true) != '' then json_extract_path_text(_airbyte_data, 'group_stations', true) end as group_stations,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'assignee_stations', true) != '' then json_extract_path_text(_airbyte_data, 'assignee_stations', true) end as assignee_stations,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'status_updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'status_updated_at', true) end as status_updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'assignee_updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'assignee_updated_at', true) end as assignee_updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'requester_updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'requester_updated_at', true) end as requester_updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'initially_assigned_at', true) != '' then json_extract_path_text(_airbyte_data, 'initially_assigned_at', true) end as initially_assigned_at,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'reply_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'reply_time_in_minutes', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as reply_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'latest_comment_added_at', true) != '' then json_extract_path_text(_airbyte_data, 'latest_comment_added_at', true) end as latest_comment_added_at,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'on_hold_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'on_hold_time_in_minutes', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as on_hold_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'agent_wait_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'agent_wait_time_in_minutes', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as agent_wait_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'requester_wait_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'requester_wait_time_in_minutes', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as requester_wait_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'full_resolution_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'full_resolution_time_in_minutes', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as full_resolution_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'first_resolution_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'first_resolution_time_in_minutes', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as first_resolution_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from "datalake".zendesk_intercom._airbyte_raw_ticket_metrics as table_alias
2022-04-18 15:56:14 [42mnormalization[0m > -- ticket_metrics
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_ab2 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_ab1
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     cast(id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(url as varchar) as url,
2022-04-18 15:56:14 [42mnormalization[0m >     cast("time" as varchar) as "time",
2022-04-18 15:56:14 [42mnormalization[0m >     cast(type as varchar) as type,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(metric as varchar) as metric,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(status as varchar) as status,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(reopens as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as reopens,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(replies as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as replies,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(solved_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as solved_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(ticket_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as ticket_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(created_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(updated_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(assigned_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as assigned_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(instance_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as instance_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(group_stations as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as group_stations,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(assignee_stations as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as assignee_stations,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(status_updated_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as status_updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(assignee_updated_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as assignee_updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(requester_updated_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as requester_updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(initially_assigned_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as initially_assigned_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(reply_time_in_minutes as varchar) as reply_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(latest_comment_added_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as latest_comment_added_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(on_hold_time_in_minutes as varchar) as on_hold_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(agent_wait_time_in_minutes as varchar) as agent_wait_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(requester_wait_time_in_minutes as varchar) as requester_wait_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(full_resolution_time_in_minutes as varchar) as full_resolution_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(first_resolution_time_in_minutes as varchar) as first_resolution_time_in_minutes,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__ticket_metrics_ab1
2022-04-18 15:56:14 [42mnormalization[0m > -- ticket_metrics
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > )-- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_ab2
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast("time" as varchar), '') || '-' || coalesce(cast(type as varchar), '') || '-' || coalesce(cast(metric as varchar), '') || '-' || coalesce(cast(status as varchar), '') || '-' || coalesce(cast(reopens as varchar), '') || '-' || coalesce(cast(replies as varchar), '') || '-' || coalesce(cast(solved_at as varchar), '') || '-' || coalesce(cast(ticket_id as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(assigned_at as varchar), '') || '-' || coalesce(cast(instance_id as varchar), '') || '-' || coalesce(cast(group_stations as varchar), '') || '-' || coalesce(cast(assignee_stations as varchar), '') || '-' || coalesce(cast(status_updated_at as varchar), '') || '-' || coalesce(cast(assignee_updated_at as varchar), '') || '-' || coalesce(cast(requester_updated_at as varchar), '') || '-' || coalesce(cast(initially_assigned_at as varchar), '') || '-' || coalesce(cast(reply_time_in_minutes as varchar), '') || '-' || coalesce(cast(latest_comment_added_at as varchar), '') || '-' || coalesce(cast(on_hold_time_in_minutes as varchar), '') || '-' || coalesce(cast(agent_wait_time_in_minutes as varchar), '') || '-' || coalesce(cast(requester_wait_time_in_minutes as varchar), '') || '-' || coalesce(cast(full_resolution_time_in_minutes as varchar), '') || '-' || coalesce(cast(first_resolution_time_in_minutes as varchar), '') as varchar)) as _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:14 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__ticket_metrics_ab2 tmp
2022-04-18 15:56:14 [42mnormalization[0m > -- ticket_metrics
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   ) ;
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.413376 (Thread-3): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.413583 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.413723 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.454205 (Thread-2): SQL status: SELECT in 0.27 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.480676 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.480927 (Thread-4): SQL status: CREATE VIEW in 0.08 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.481126 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:14 [42mnormalization[0m >     "tags__dbt_tmp155529168416"
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:14 [42mnormalization[0m >   as (
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > with __dbt__cte__tags_ab1 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom._airbyte_raw_tags
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'name', true) != '' then json_extract_path_text(_airbyte_data, 'name', true) end as name,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'count', true) != '' then json_extract_path_text(_airbyte_data, 'count', true) end as count,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from "datalake".zendesk_intercom._airbyte_raw_tags as table_alias
2022-04-18 15:56:14 [42mnormalization[0m > -- tags
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__tags_ab2 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__tags_ab1
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     cast(name as varchar) as name,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(count as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as count,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__tags_ab1
2022-04-18 15:56:14 [42mnormalization[0m > -- tags
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__tags_ab3 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__tags_ab2
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     md5(cast(coalesce(cast(name as varchar), '') || '-' || coalesce(cast(count as varchar), '') as varchar)) as _airbyte_tags_hashid,
2022-04-18 15:56:14 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__tags_ab2 tmp
2022-04-18 15:56:14 [42mnormalization[0m > -- tags
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__tags_ab3
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     name,
2022-04-18 15:56:14 [42mnormalization[0m >     count,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_tags_hashid
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__tags_ab3
2022-04-18 15:56:14 [42mnormalization[0m > -- tags from "datalake".zendesk_intercom._airbyte_raw_tags
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:14 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > )) from "datalake".zendesk_intercom."tags"),
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     true)
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   );
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.485133 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.485504 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > alter table "datalake"._airbyte_zendesk_intercom."ticket_metrics_stg__dbt_tmp" rename to "ticket_metrics_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.490442 (Thread-4): SQL status: ALTER TABLE in 0.00 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.491831 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.491987 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.492112 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.586676 (Thread-4): SQL status: COMMIT in 0.09 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.587286 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.587432 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.591337 (Thread-4): SQL status: BEGIN in 0.00 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.596382 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.596552 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > drop view if exists "datalake"._airbyte_zendesk_intercom."ticket_metrics_stg__dbt_backup" cascade
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.600305 (Thread-4): SQL status: DROP VIEW in 0.00 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.601396 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.601552 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.601685 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.644779 (Thread-1): SQL status: SELECT in 0.38 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.649745 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.649915 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:14 [42mnormalization[0m >     "brands__dbt_tmp155529192851"
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:14 [42mnormalization[0m >   as (
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > with __dbt__cte__brands_ab1 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom._airbyte_raw_brands
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'logo', true) != '' then json_extract_path_text(_airbyte_data, 'logo', true) end as logo,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'name', true) != '' then json_extract_path_text(_airbyte_data, 'name', true) end as name,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'active', true) != '' then json_extract_path_text(_airbyte_data, 'active', true) end as active,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'default', true) != '' then json_extract_path_text(_airbyte_data, 'default', true) end as "default",
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'brand_url', true) != '' then json_extract_path_text(_airbyte_data, 'brand_url', true) end as brand_url,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'subdomain', true) != '' then json_extract_path_text(_airbyte_data, 'subdomain', true) end as subdomain,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'is_deleted', true) != '' then json_extract_path_text(_airbyte_data, 'is_deleted', true) end as is_deleted,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'host_mapping', true) != '' then json_extract_path_text(_airbyte_data, 'host_mapping', true) end as host_mapping,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'has_help_center', true) != '' then json_extract_path_text(_airbyte_data, 'has_help_center', true) end as has_help_center,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'ticket_form_ids', true) as ticket_form_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'help_center_state', true) != '' then json_extract_path_text(_airbyte_data, 'help_center_state', true) end as help_center_state,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'signature_template', true) != '' then json_extract_path_text(_airbyte_data, 'signature_template', true) end as signature_template,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from "datalake".zendesk_intercom._airbyte_raw_brands as table_alias
2022-04-18 15:56:14 [42mnormalization[0m > -- brands
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__brands_ab2 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__brands_ab1
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     cast(id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(url as varchar) as url,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(logo as varchar) as logo,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(name as varchar) as name,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(active, 'true', '1', 'false', '0')::integer as boolean) as active,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode("default", 'true', '1', 'false', '0')::integer as boolean) as "default",
2022-04-18 15:56:14 [42mnormalization[0m >     cast(brand_url as varchar) as brand_url,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(subdomain as varchar) as subdomain,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(created_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(is_deleted, 'true', '1', 'false', '0')::integer as boolean) as is_deleted,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(updated_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(host_mapping as varchar) as host_mapping,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(has_help_center, 'true', '1', 'false', '0')::integer as boolean) as has_help_center,
2022-04-18 15:56:14 [42mnormalization[0m >     ticket_form_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(help_center_state as varchar) as help_center_state,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(signature_template as varchar) as signature_template,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__brands_ab1
2022-04-18 15:56:14 [42mnormalization[0m > -- brands
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__brands_ab3 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__brands_ab2
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast(logo as varchar), '') || '-' || coalesce(cast(name as varchar), '') || '-' || coalesce(cast(case when active then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(case when "default" then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(brand_url as varchar), '') || '-' || coalesce(cast(subdomain as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(case when is_deleted then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(host_mapping as varchar), '') || '-' || coalesce(cast(case when has_help_center then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(ticket_form_ids as varchar), '') || '-' || coalesce(cast(help_center_state as varchar), '') || '-' || coalesce(cast(signature_template as varchar), '') as varchar)) as _airbyte_brands_hashid,
2022-04-18 15:56:14 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__brands_ab2 tmp
2022-04-18 15:56:14 [42mnormalization[0m > -- brands
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__brands_ab3
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     id,
2022-04-18 15:56:14 [42mnormalization[0m >     url,
2022-04-18 15:56:14 [42mnormalization[0m >     logo,
2022-04-18 15:56:14 [42mnormalization[0m >     name,
2022-04-18 15:56:14 [42mnormalization[0m >     active,
2022-04-18 15:56:14 [42mnormalization[0m >     "default",
2022-04-18 15:56:14 [42mnormalization[0m >     brand_url,
2022-04-18 15:56:14 [42mnormalization[0m >     subdomain,
2022-04-18 15:56:14 [42mnormalization[0m >     created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     is_deleted,
2022-04-18 15:56:14 [42mnormalization[0m >     updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     host_mapping,
2022-04-18 15:56:14 [42mnormalization[0m >     has_help_center,
2022-04-18 15:56:14 [42mnormalization[0m >     ticket_form_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     help_center_state,
2022-04-18 15:56:14 [42mnormalization[0m >     signature_template,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_brands_hashid
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__brands_ab3
2022-04-18 15:56:14 [42mnormalization[0m > -- brands from "datalake".zendesk_intercom._airbyte_raw_brands
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:14 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > )) from "datalake".zendesk_intercom."brands"),
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     true)
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   );
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.667642 (Thread-4): SQL status: COMMIT in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.667840 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.667972 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.671763 (Thread-4): SQL status: BEGIN in 0.00 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.672460 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.672669 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: ROLLBACK
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.676009 (Thread-4): On model.airbyte_utils.ticket_metrics_stg: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.676856 (Thread-4): 15:55:29 | 5 of 33 OK created view model _airbyte_zendesk_intercom.ticket_metrics_stg................................... [CREATE VIEW in 0.48s]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.677200 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.677504 (Thread-4): Began running node model.airbyte_utils.tickets_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.677779 (Thread-4): 15:55:29 | 7 of 33 START view model _airbyte_zendesk_intercom.tickets_stg............................................... [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.678148 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.678324 (Thread-4): Compiling model.airbyte_utils.tickets_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.798136 (Thread-4): Writing injected SQL for node "model.airbyte_utils.tickets_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.798639 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.803017 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.tickets_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.803414 (Thread-4): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.803558 (Thread-4): On model.airbyte_utils.tickets_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.803698 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.803813 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.816185 (Thread-2): SQL status: SELECT in 0.33 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.820757 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.820961 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tags__dbt_tmp155529168416'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tags__dbt_tmp155529168416'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tags__dbt_tmp155529168416'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.828404 (Thread-3): SQL status: SELECT in 0.41 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.839797 (Thread-3): Writing injected SQL for node "model.airbyte_utils.sla_policies"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.840384 (Thread-3): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.849117 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.849303 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.856841 (Thread-4): SQL status: BEGIN in 0.05 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.857048 (Thread-4): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.857189 (Thread-4): On model.airbyte_utils.tickets_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   create view "datalake"._airbyte_zendesk_intercom."tickets_stg__dbt_tmp" as (
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > with __dbt__cte__tickets_ab1 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom._airbyte_raw_tickets
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'via', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'via', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as via,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'tags', true) as tags,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'type', true) != '' then json_extract_path_text(_airbyte_data, 'type', true) end as type,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'due_at', true) != '' then json_extract_path_text(_airbyte_data, 'due_at', true) end as due_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'status', true) != '' then json_extract_path_text(_airbyte_data, 'status', true) end as status,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'subject', true) != '' then json_extract_path_text(_airbyte_data, 'subject', true) end as subject,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'brand_id', true) != '' then json_extract_path_text(_airbyte_data, 'brand_id', true) end as brand_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'group_id', true) != '' then json_extract_path_text(_airbyte_data, 'group_id', true) end as group_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'priority', true) != '' then json_extract_path_text(_airbyte_data, 'priority', true) end as priority,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'is_public', true) != '' then json_extract_path_text(_airbyte_data, 'is_public', true) end as is_public,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'recipient', true) != '' then json_extract_path_text(_airbyte_data, 'recipient', true) end as recipient,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'problem_id', true) != '' then json_extract_path_text(_airbyte_data, 'problem_id', true) end as problem_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'assignee_id', true) != '' then json_extract_path_text(_airbyte_data, 'assignee_id', true) end as assignee_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'description', true) != '' then json_extract_path_text(_airbyte_data, 'description', true) end as description,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'external_id', true) != '' then json_extract_path_text(_airbyte_data, 'external_id', true) end as external_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'raw_subject', true) != '' then json_extract_path_text(_airbyte_data, 'raw_subject', true) end as raw_subject,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'email_cc_ids', true) as email_cc_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'follower_ids', true) as follower_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'followup_ids', true) as followup_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'requester_id', true) != '' then json_extract_path_text(_airbyte_data, 'requester_id', true) end as requester_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'submitter_id', true) != '' then json_extract_path_text(_airbyte_data, 'submitter_id', true) end as submitter_id,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'custom_fields', true) as custom_fields,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'has_incidents', true) != '' then json_extract_path_text(_airbyte_data, 'has_incidents', true) end as has_incidents,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'forum_topic_id', true) != '' then json_extract_path_text(_airbyte_data, 'forum_topic_id', true) end as forum_topic_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'ticket_form_id', true) != '' then json_extract_path_text(_airbyte_data, 'ticket_form_id', true) end as ticket_form_id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'organization_id', true) != '' then json_extract_path_text(_airbyte_data, 'organization_id', true) end as organization_id,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'collaborator_ids', true) as collaborator_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'allow_attachments', true) != '' then json_extract_path_text(_airbyte_data, 'allow_attachments', true) end as allow_attachments,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'allow_channelback', true) != '' then json_extract_path_text(_airbyte_data, 'allow_channelback', true) end as allow_channelback,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'generated_timestamp', true) != '' then json_extract_path_text(_airbyte_data, 'generated_timestamp', true) end as generated_timestamp,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'satisfaction_rating', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'satisfaction_rating', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as satisfaction_rating,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'sharing_agreement_ids', true) as sharing_agreement_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from "datalake".zendesk_intercom._airbyte_raw_tickets as table_alias
2022-04-18 15:56:14 [42mnormalization[0m > -- tickets
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__tickets_ab2 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__tickets_ab1
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     cast(id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(url as varchar) as url,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(via as varchar) as via,
2022-04-18 15:56:14 [42mnormalization[0m >     tags,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(type as varchar) as type,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(due_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as due_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(status as varchar) as status,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(subject as varchar) as subject,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(brand_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as brand_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(group_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as group_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(priority as varchar) as priority,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(is_public, 'true', '1', 'false', '0')::integer as boolean) as is_public,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(recipient as varchar) as recipient,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(created_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(problem_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as problem_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(updated_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(assignee_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as assignee_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(description as varchar) as description,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(external_id as varchar) as external_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(raw_subject as varchar) as raw_subject,
2022-04-18 15:56:14 [42mnormalization[0m >     email_cc_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     follower_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     followup_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(requester_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as requester_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(submitter_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as submitter_id,
2022-04-18 15:56:14 [42mnormalization[0m >     custom_fields,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(has_incidents, 'true', '1', 'false', '0')::integer as boolean) as has_incidents,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(forum_topic_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as forum_topic_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(ticket_form_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as ticket_form_id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(organization_id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as organization_id,
2022-04-18 15:56:14 [42mnormalization[0m >     collaborator_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(allow_attachments, 'true', '1', 'false', '0')::integer as boolean) as allow_attachments,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(decode(allow_channelback, 'true', '1', 'false', '0')::integer as boolean) as allow_channelback,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(generated_timestamp as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as generated_timestamp,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(satisfaction_rating as varchar) as satisfaction_rating,
2022-04-18 15:56:14 [42mnormalization[0m >     sharing_agreement_ids,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__tickets_ab1
2022-04-18 15:56:14 [42mnormalization[0m > -- tickets
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > )-- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__tickets_ab2
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast(via as varchar), '') || '-' || coalesce(cast(tags as varchar), '') || '-' || coalesce(cast(type as varchar), '') || '-' || coalesce(cast(due_at as varchar), '') || '-' || coalesce(cast(status as varchar), '') || '-' || coalesce(cast(subject as varchar), '') || '-' || coalesce(cast(brand_id as varchar), '') || '-' || coalesce(cast(group_id as varchar), '') || '-' || coalesce(cast(priority as varchar), '') || '-' || coalesce(cast(case when is_public then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(recipient as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(problem_id as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(assignee_id as varchar), '') || '-' || coalesce(cast(description as varchar), '') || '-' || coalesce(cast(external_id as varchar), '') || '-' || coalesce(cast(raw_subject as varchar), '') || '-' || coalesce(cast(email_cc_ids as varchar), '') || '-' || coalesce(cast(follower_ids as varchar), '') || '-' || coalesce(cast(followup_ids as varchar), '') || '-' || coalesce(cast(requester_id as varchar), '') || '-' || coalesce(cast(submitter_id as varchar), '') || '-' || coalesce(cast(custom_fields as varchar), '') || '-' || coalesce(cast(case when has_incidents then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(forum_topic_id as varchar), '') || '-' || coalesce(cast(ticket_form_id as varchar), '') || '-' || coalesce(cast(organization_id as varchar), '') || '-' || coalesce(cast(collaborator_ids as varchar), '') || '-' || coalesce(cast(case when allow_attachments then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(case when allow_channelback then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(generated_timestamp as varchar), '') || '-' || coalesce(cast(satisfaction_rating as varchar), '') || '-' || coalesce(cast(sharing_agreement_ids as varchar), '') as varchar)) as _airbyte_tickets_hashid,
2022-04-18 15:56:14 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__tickets_ab2 tmp
2022-04-18 15:56:14 [42mnormalization[0m > -- tickets
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   ) ;
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.940343 (Thread-4): SQL status: CREATE VIEW in 0.08 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.945000 (Thread-4): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.945218 (Thread-4): On model.airbyte_utils.tickets_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > alter table "datalake"."_airbyte_zendesk_intercom"."tickets_stg" rename to "tickets_stg__dbt_backup"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.950906 (Thread-4): SQL status: ALTER TABLE in 0.01 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.954920 (Thread-4): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.955088 (Thread-4): On model.airbyte_utils.tickets_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > alter table "datalake"._airbyte_zendesk_intercom."tickets_stg__dbt_tmp" rename to "tickets_stg"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.960446 (Thread-4): SQL status: ALTER TABLE in 0.01 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.961961 (Thread-4): On model.airbyte_utils.tickets_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.962117 (Thread-4): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:29.962246 (Thread-4): On model.airbyte_utils.tickets_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.058227 (Thread-4): SQL status: COMMIT in 0.10 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.058849 (Thread-4): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.058999 (Thread-4): On model.airbyte_utils.tickets_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.062847 (Thread-4): SQL status: BEGIN in 0.00 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.066096 (Thread-4): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.066260 (Thread-4): On model.airbyte_utils.tickets_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_stg"} */
2022-04-18 15:56:14 [42mnormalization[0m > drop view if exists "datalake"._airbyte_zendesk_intercom."tickets_stg__dbt_backup" cascade
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.082691 (Thread-4): SQL status: DROP VIEW in 0.02 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.083943 (Thread-4): On model.airbyte_utils.tickets_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.084099 (Thread-4): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.084229 (Thread-4): On model.airbyte_utils.tickets_stg: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.157565 (Thread-4): SQL status: COMMIT in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.157924 (Thread-4): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.158069 (Thread-4): On model.airbyte_utils.tickets_stg: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.161929 (Thread-4): SQL status: BEGIN in 0.00 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.162728 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.162945 (Thread-4): On model.airbyte_utils.tickets_stg: ROLLBACK
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.166576 (Thread-4): On model.airbyte_utils.tickets_stg: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.167534 (Thread-4): 15:55:30 | 7 of 33 OK created view model _airbyte_zendesk_intercom.tickets_stg.......................................... [CREATE VIEW in 0.49s]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.167853 (Thread-4): Finished running node model.airbyte_utils.tickets_stg
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.168092 (Thread-4): Began running node model.airbyte_utils.ticket_metric_events_scd
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.168861 (Thread-4): 15:55:30 | 8 of 33 START incremental model zendesk_intercom.ticket_metric_events_scd.................................... [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.169268 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.169482 (Thread-4): Compiling model.airbyte_utils.ticket_metric_events_scd
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.174853 (Thread-1): SQL status: SELECT in 0.52 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.185528 (Thread-3): SQL status: SELECT in 0.34 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.190953 (Thread-2): SQL status: SELECT in 0.37 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.191867 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.193635 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.198037 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.202380 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.202658 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'brands__dbt_tmp155529192851'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'brands__dbt_tmp155529192851'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'brands__dbt_tmp155529192851'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.202869 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.203050 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:14 [42mnormalization[0m >     "sla_policies__dbt_tmp155529846195"
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:14 [42mnormalization[0m >   as (
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > with __dbt__cte__sla_policies_ab1 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom._airbyte_raw_sla_policies
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'title', true) != '' then json_extract_path_text(_airbyte_data, 'title', true) end as title,
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >         case when json_extract_path_text(table_alias._airbyte_data, 'filter', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'filter', true) end
2022-04-18 15:56:14 [42mnormalization[0m >      as filter,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'position', true) != '' then json_extract_path_text(_airbyte_data, 'position', true) end as position,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_data, 'description', true) != '' then json_extract_path_text(_airbyte_data, 'description', true) end as description,
2022-04-18 15:56:14 [42mnormalization[0m >     json_extract_path_text(_airbyte_data, 'policy_metrics', true) as policy_metrics,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from "datalake".zendesk_intercom._airbyte_raw_sla_policies as table_alias
2022-04-18 15:56:14 [42mnormalization[0m > -- sla_policies
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__sla_policies_ab2 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_ab1
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     cast(id as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as id,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(url as varchar) as url,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(title as varchar) as title,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(filter as varchar) as filter,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(position as 
2022-04-18 15:56:14 [42mnormalization[0m >     bigint
2022-04-18 15:56:14 [42mnormalization[0m > ) as position,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(created_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(nullif(updated_at, '') as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) as updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     cast(description as varchar) as description,
2022-04-18 15:56:14 [42mnormalization[0m >     policy_metrics,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__sla_policies_ab1
2022-04-18 15:56:14 [42mnormalization[0m > -- sla_policies
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),  __dbt__cte__sla_policies_ab3 as (
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_ab2
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast(title as varchar), '') || '-' || coalesce(cast(filter as varchar), '') || '-' || coalesce(cast(position as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(description as varchar), '') || '-' || coalesce(cast(policy_metrics as varchar), '') as varchar)) as _airbyte_sla_policies_hashid,
2022-04-18 15:56:14 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__sla_policies_ab2 tmp
2022-04-18 15:56:14 [42mnormalization[0m > -- sla_policies
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_ab3
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     id,
2022-04-18 15:56:14 [42mnormalization[0m >     url,
2022-04-18 15:56:14 [42mnormalization[0m >     title,
2022-04-18 15:56:14 [42mnormalization[0m >     filter,
2022-04-18 15:56:14 [42mnormalization[0m >     position,
2022-04-18 15:56:14 [42mnormalization[0m >     created_at,
2022-04-18 15:56:14 [42mnormalization[0m >     updated_at,
2022-04-18 15:56:14 [42mnormalization[0m >     description,
2022-04-18 15:56:14 [42mnormalization[0m >     policy_metrics,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_sla_policies_hashid
2022-04-18 15:56:14 [42mnormalization[0m > from __dbt__cte__sla_policies_ab3
2022-04-18 15:56:14 [42mnormalization[0m > -- sla_policies from "datalake".zendesk_intercom._airbyte_raw_sla_policies
2022-04-18 15:56:14 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:14 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > )) from "datalake".zendesk_intercom."sla_policies"),
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     true)
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   );
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.203228 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.203502 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.203867 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.257999 (Thread-4): SQL status: BEGIN in 0.05 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.258310 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.258452 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.594437 (Thread-3): SQL status: SELECT in 0.39 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.599075 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.599280 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'sla_policies__dbt_tmp155529846195'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'sla_policies__dbt_tmp155529846195'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'sla_policies__dbt_tmp155529846195'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.628238 (Thread-1): SQL status: SELECT in 0.42 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.633636 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.633838 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.650743 (Thread-2): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.674472 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.674687 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tags__dbt_tmp155529168416'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tags__dbt_tmp155529168416'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tags__dbt_tmp155529168416'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.804238 (Thread-4): SQL status: SELECT in 0.55 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.817109 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:30.817303 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.091765 (Thread-3): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.098833 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.099020 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.099324 (Thread-2): SQL status: SELECT in 0.42 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.107533 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.107698 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.123962 (Thread-1): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.129312 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.129484 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'brands__dbt_tmp155529192851'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'brands__dbt_tmp155529192851'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'brands__dbt_tmp155529192851'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.227565 (Thread-4): SQL status: SELECT in 0.41 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.260481 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.260802 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metric_events_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metric_events_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metric_events_stg'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.569047 (Thread-1): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.575112 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.575294 (Thread-2): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.575468 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.588547 (Thread-2): 
2022-04-18 15:56:14 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."tags":
2022-04-18 15:56:14 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:14 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:14 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:14 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.595324 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.595498 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.606015 (Thread-3): SQL status: SELECT in 0.51 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.613004 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.613200 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'sla_policies__dbt_tmp155529846195'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'sla_policies__dbt_tmp155529846195'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'sla_policies__dbt_tmp155529846195'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.691683 (Thread-4): SQL status: SELECT in 0.43 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.693969 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.694153 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.694296 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.694428 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.694558 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.694686 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.694814 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.694938 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.695062 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.695185 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.698386 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:31.698548 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.014216 (Thread-3): SQL status: SELECT in 0.40 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.019487 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.019692 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.026606 (Thread-2): SQL status: SELECT in 0.43 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.028799 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.tags"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.029277 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.029420 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       delete
2022-04-18 15:56:14 [42mnormalization[0m >     from "datalake".zendesk_intercom."tags"
2022-04-18 15:56:14 [42mnormalization[0m >     where (_airbyte_ab_id) in (
2022-04-18 15:56:14 [42mnormalization[0m >         select (_airbyte_ab_id)
2022-04-18 15:56:14 [42mnormalization[0m >         from "tags__dbt_tmp155529168416"
2022-04-18 15:56:14 [42mnormalization[0m >     );
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."tags" ("name", "count", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_tags_hashid")
2022-04-18 15:56:14 [42mnormalization[0m >     (
2022-04-18 15:56:14 [42mnormalization[0m >        select "name", "count", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_tags_hashid"
2022-04-18 15:56:14 [42mnormalization[0m >        from "tags__dbt_tmp155529168416"
2022-04-18 15:56:14 [42mnormalization[0m >     );
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.054844 (Thread-1): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.060450 (Thread-1): 
2022-04-18 15:56:14 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."brands":
2022-04-18 15:56:14 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:14 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:14 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:14 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.063927 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.064100 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.147006 (Thread-4): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.149749 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.149936 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.150088 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.150232 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.150367 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.150500 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.150632 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.150766 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.150897 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.151025 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.151153 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.151279 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.151407 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.151539 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.151667 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.151832 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.151978 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.152120 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.152288 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.152452 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.152593 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.152730 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.152873 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.153013 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.153151 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.166889 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.167112 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metric_events_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metric_events_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metric_events_stg'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.171261 (Thread-2): SQL status: INSERT 0 198 in 0.14 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.175760 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.175919 (Thread-2): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tags'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.508415 (Thread-3): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.514263 (Thread-3): 
2022-04-18 15:56:14 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."sla_policies":
2022-04-18 15:56:14 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:14 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:14 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:14 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.517315 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.517478 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.542485 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.545184 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.brands"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.545593 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.545736 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       delete
2022-04-18 15:56:14 [42mnormalization[0m >     from "datalake".zendesk_intercom."brands"
2022-04-18 15:56:14 [42mnormalization[0m >     where (_airbyte_ab_id) in (
2022-04-18 15:56:14 [42mnormalization[0m >         select (_airbyte_ab_id)
2022-04-18 15:56:14 [42mnormalization[0m >         from "brands__dbt_tmp155529192851"
2022-04-18 15:56:14 [42mnormalization[0m >     );
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."brands" ("id", "url", "logo", "name", "active", "default", "brand_url", "subdomain", "created_at", "is_deleted", "updated_at", "host_mapping", "has_help_center", "ticket_form_ids", "help_center_state", "signature_template", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_brands_hashid")
2022-04-18 15:56:14 [42mnormalization[0m >     (
2022-04-18 15:56:14 [42mnormalization[0m >        select "id", "url", "logo", "name", "active", "default", "brand_url", "subdomain", "created_at", "is_deleted", "updated_at", "host_mapping", "has_help_center", "ticket_form_ids", "help_center_state", "signature_template", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_brands_hashid"
2022-04-18 15:56:14 [42mnormalization[0m >        from "brands__dbt_tmp155529192851"
2022-04-18 15:56:14 [42mnormalization[0m >     );
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.582268 (Thread-4): SQL status: SELECT in 0.41 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.584492 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.584682 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.584837 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.584976 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.585111 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.585244 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.585377 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.585510 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.585641 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.585771 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.585932 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.586462 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.586615 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.586761 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.586902 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.587042 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.587179 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.587317 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.587453 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.587596 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.592529 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.596381 (Thread-2): SQL status: SELECT in 0.42 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.598644 (Thread-2): On model.airbyte_utils.tags: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.598805 (Thread-2): Using redshift connection "model.airbyte_utils.tags".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.598942 (Thread-2): On model.airbyte_utils.tags: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.596576 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metric_events_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metric_events_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metric_events_stg'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.788780 (Thread-2): SQL status: COMMIT in 0.19 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.789696 (Thread-2): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.789912 (Thread-2): On model.airbyte_utils.tags: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.790753 (Thread-2): 15:55:32 | 2 of 33 OK created incremental model zendesk_intercom.tags................................................... [INSERT 0 198 in 4.24s]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.791751 (Thread-2): Finished running node model.airbyte_utils.tags
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.791982 (Thread-2): Began running node model.airbyte_utils.ticket_fields_scd
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.792539 (Thread-2): 15:55:32 | 9 of 33 START incremental model zendesk_intercom.ticket_fields_scd........................................... [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.793355 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.793543 (Thread-2): Compiling model.airbyte_utils.ticket_fields_scd
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.821511 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.821766 (Thread-2): On model.airbyte_utils.ticket_fields_scd: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.821915 (Thread-2): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.822033 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.859021 (Thread-3): SQL status: SELECT in 0.34 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.861967 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.sla_policies"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.862376 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.862515 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       delete
2022-04-18 15:56:14 [42mnormalization[0m >     from "datalake".zendesk_intercom."sla_policies"
2022-04-18 15:56:14 [42mnormalization[0m >     where (_airbyte_ab_id) in (
2022-04-18 15:56:14 [42mnormalization[0m >         select (_airbyte_ab_id)
2022-04-18 15:56:14 [42mnormalization[0m >         from "sla_policies__dbt_tmp155529846195"
2022-04-18 15:56:14 [42mnormalization[0m >     );
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."sla_policies" ("id", "url", "title", "filter", "position", "created_at", "updated_at", "description", "policy_metrics", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_sla_policies_hashid")
2022-04-18 15:56:14 [42mnormalization[0m >     (
2022-04-18 15:56:14 [42mnormalization[0m >        select "id", "url", "title", "filter", "position", "created_at", "updated_at", "description", "policy_metrics", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_sla_policies_hashid"
2022-04-18 15:56:14 [42mnormalization[0m >        from "sla_policies__dbt_tmp155529846195"
2022-04-18 15:56:14 [42mnormalization[0m >     );
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.869292 (Thread-2): SQL status: BEGIN in 0.05 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.869470 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.869600 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_fields_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_fields_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_fields_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.890918 (Thread-1): SQL status: INSERT 0 13 in 0.35 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.895710 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.895887 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'brands'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.912475 (Thread-4): SQL status: SELECT in 0.31 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.914732 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.914901 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.915040 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.915171 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.915297 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.915428 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.915550 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.915674 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.915796 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.915920 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.916071 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.916208 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.916375 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.916511 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.916643 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.916781 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.916911 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.917040 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.917170 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.917300 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.924787 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events_scd"
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.925263 (Thread-4): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.934038 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:32.934213 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.296897 (Thread-3): SQL status: INSERT 0 12 in 0.43 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.297297 (Thread-4): SQL status: SELECT in 0.36 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.302262 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.306683 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'sla_policies'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.312053 (Thread-1): SQL status: SELECT in 0.42 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.306465 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.314305 (Thread-1): On model.airbyte_utils.brands: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.314515 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:14 [42mnormalization[0m >     "ticket_metric_events_scd__dbt_tmp155532930967"
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >       compound sortkey(_airbyte_active_row,_airbyte_unique_key_scd,_airbyte_emitted_at)
2022-04-18 15:56:14 [42mnormalization[0m >   as (
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > -- depends_on: ref('ticket_metric_events_stg')
2022-04-18 15:56:14 [42mnormalization[0m > with
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > new_data as (
2022-04-18 15:56:14 [42mnormalization[0m >     -- retrieve incremental "new" data
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >         *
2022-04-18 15:56:14 [42mnormalization[0m >     from "datalake"._airbyte_zendesk_intercom."ticket_metric_events_stg"
2022-04-18 15:56:14 [42mnormalization[0m >     -- ticket_metric_events from "datalake".zendesk_intercom._airbyte_raw_ticket_metric_events
2022-04-18 15:56:14 [42mnormalization[0m >     where 1 = 1
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:14 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:14 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:14 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metric_events_scd"),
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     true)
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > ),
2022-04-18 15:56:14 [42mnormalization[0m > new_data_ids as (
2022-04-18 15:56:14 [42mnormalization[0m >     -- build a subset of _airbyte_unique_key from rows that are new
2022-04-18 15:56:14 [42mnormalization[0m >     select distinct
2022-04-18 15:56:14 [42mnormalization[0m >         md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key
2022-04-18 15:56:14 [42mnormalization[0m >     from new_data
2022-04-18 15:56:14 [42mnormalization[0m > ),
2022-04-18 15:56:14 [42mnormalization[0m > empty_new_data as (
2022-04-18 15:56:14 [42mnormalization[0m >     -- build an empty table to only keep the table's column types
2022-04-18 15:56:14 [42mnormalization[0m >     select * from new_data where 1 = 0
2022-04-18 15:56:14 [42mnormalization[0m > ),
2022-04-18 15:56:14 [42mnormalization[0m > previous_active_scd_data as (
2022-04-18 15:56:14 [42mnormalization[0m >     -- retrieve "incomplete old" data that needs to be updated with an end date because of new changes
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >         this_data."_airbyte_ticket_metric_events_hashid",
2022-04-18 15:56:14 [42mnormalization[0m >   this_data."id",
2022-04-18 15:56:14 [42mnormalization[0m >   this_data."time",
2022-04-18 15:56:14 [42mnormalization[0m >   this_data."type",
2022-04-18 15:56:14 [42mnormalization[0m >   this_data."metric",
2022-04-18 15:56:14 [42mnormalization[0m >   this_data."ticket_id",
2022-04-18 15:56:14 [42mnormalization[0m >   this_data."instance_id",
2022-04-18 15:56:14 [42mnormalization[0m >   this_data."_airbyte_ab_id",
2022-04-18 15:56:14 [42mnormalization[0m >   this_data."_airbyte_emitted_at",
2022-04-18 15:56:14 [42mnormalization[0m >   this_data."_airbyte_normalized_at"
2022-04-18 15:56:14 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_metric_events_scd" as this_data
2022-04-18 15:56:14 [42mnormalization[0m >     -- make a join with new_data using primary key to filter active data that need to be updated only
2022-04-18 15:56:14 [42mnormalization[0m >     join new_data_ids on this_data._airbyte_unique_key = new_data_ids._airbyte_unique_key
2022-04-18 15:56:14 [42mnormalization[0m >     -- force left join to NULL values (we just need to transfer column types only for the star_intersect macro on schema changes)
2022-04-18 15:56:14 [42mnormalization[0m >     left join empty_new_data as inc_data on this_data._airbyte_ab_id = inc_data._airbyte_ab_id
2022-04-18 15:56:14 [42mnormalization[0m >     where _airbyte_active_row = 1
2022-04-18 15:56:14 [42mnormalization[0m > ),
2022-04-18 15:56:14 [42mnormalization[0m > input_data as (
2022-04-18 15:56:14 [42mnormalization[0m >     select "_airbyte_ticket_metric_events_hashid",
2022-04-18 15:56:14 [42mnormalization[0m >   "id",
2022-04-18 15:56:14 [42mnormalization[0m >   "time",
2022-04-18 15:56:14 [42mnormalization[0m >   "type",
2022-04-18 15:56:14 [42mnormalization[0m >   "metric",
2022-04-18 15:56:14 [42mnormalization[0m >   "ticket_id",
2022-04-18 15:56:14 [42mnormalization[0m >   "instance_id",
2022-04-18 15:56:14 [42mnormalization[0m >   "_airbyte_ab_id",
2022-04-18 15:56:14 [42mnormalization[0m >   "_airbyte_emitted_at",
2022-04-18 15:56:14 [42mnormalization[0m >   "_airbyte_normalized_at" from new_data
2022-04-18 15:56:14 [42mnormalization[0m >     union all
2022-04-18 15:56:14 [42mnormalization[0m >     select "_airbyte_ticket_metric_events_hashid",
2022-04-18 15:56:14 [42mnormalization[0m >   "id",
2022-04-18 15:56:14 [42mnormalization[0m >   "time",
2022-04-18 15:56:14 [42mnormalization[0m >   "type",
2022-04-18 15:56:14 [42mnormalization[0m >   "metric",
2022-04-18 15:56:14 [42mnormalization[0m >   "ticket_id",
2022-04-18 15:56:14 [42mnormalization[0m >   "instance_id",
2022-04-18 15:56:14 [42mnormalization[0m >   "_airbyte_ab_id",
2022-04-18 15:56:14 [42mnormalization[0m >   "_airbyte_emitted_at",
2022-04-18 15:56:14 [42mnormalization[0m >   "_airbyte_normalized_at" from previous_active_scd_data
2022-04-18 15:56:14 [42mnormalization[0m > ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m > scd_data as (
2022-04-18 15:56:14 [42mnormalization[0m >     -- SQL model to build a Type 2 Slowly Changing Dimension (SCD) table for each record identified by their primary key
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key,
2022-04-18 15:56:14 [42mnormalization[0m >       id,
2022-04-18 15:56:14 [42mnormalization[0m >       "time",
2022-04-18 15:56:14 [42mnormalization[0m >       type,
2022-04-18 15:56:14 [42mnormalization[0m >       metric,
2022-04-18 15:56:14 [42mnormalization[0m >       ticket_id,
2022-04-18 15:56:14 [42mnormalization[0m >       instance_id,
2022-04-18 15:56:14 [42mnormalization[0m >       "time" as _airbyte_start_at,
2022-04-18 15:56:14 [42mnormalization[0m >       lag("time") over (
2022-04-18 15:56:14 [42mnormalization[0m >         partition by id
2022-04-18 15:56:14 [42mnormalization[0m >         order by
2022-04-18 15:56:14 [42mnormalization[0m >             "time" is null asc,
2022-04-18 15:56:14 [42mnormalization[0m >             "time" desc,
2022-04-18 15:56:14 [42mnormalization[0m >             _airbyte_emitted_at desc
2022-04-18 15:56:14 [42mnormalization[0m >       ) as _airbyte_end_at,
2022-04-18 15:56:14 [42mnormalization[0m >       case when row_number() over (
2022-04-18 15:56:14 [42mnormalization[0m >         partition by id
2022-04-18 15:56:14 [42mnormalization[0m >         order by
2022-04-18 15:56:14 [42mnormalization[0m >             "time" is null asc,
2022-04-18 15:56:14 [42mnormalization[0m >             "time" desc,
2022-04-18 15:56:14 [42mnormalization[0m >             _airbyte_emitted_at desc
2022-04-18 15:56:14 [42mnormalization[0m >       ) = 1 then 1 else 0 end as _airbyte_active_row,
2022-04-18 15:56:14 [42mnormalization[0m >       _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >       _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >       _airbyte_ticket_metric_events_hashid
2022-04-18 15:56:14 [42mnormalization[0m >     from input_data
2022-04-18 15:56:14 [42mnormalization[0m > ),
2022-04-18 15:56:14 [42mnormalization[0m > dedup_data as (
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >         -- we need to ensure de-duplicated rows for merge/update queries
2022-04-18 15:56:14 [42mnormalization[0m >         -- additionally, we generate a unique key for the scd table
2022-04-18 15:56:14 [42mnormalization[0m >         row_number() over (
2022-04-18 15:56:14 [42mnormalization[0m >             partition by
2022-04-18 15:56:14 [42mnormalization[0m >                 _airbyte_unique_key,
2022-04-18 15:56:14 [42mnormalization[0m >                 _airbyte_start_at,
2022-04-18 15:56:14 [42mnormalization[0m >                 _airbyte_emitted_at
2022-04-18 15:56:14 [42mnormalization[0m >             order by _airbyte_active_row desc, _airbyte_ab_id
2022-04-18 15:56:14 [42mnormalization[0m >         ) as _airbyte_row_num,
2022-04-18 15:56:14 [42mnormalization[0m >         md5(cast(coalesce(cast(_airbyte_unique_key as varchar), '') || '-' || coalesce(cast(_airbyte_start_at as varchar), '') || '-' || coalesce(cast(_airbyte_emitted_at as varchar), '') as varchar)) as _airbyte_unique_key_scd,
2022-04-18 15:56:14 [42mnormalization[0m >         scd_data.*
2022-04-18 15:56:14 [42mnormalization[0m >     from scd_data
2022-04-18 15:56:14 [42mnormalization[0m > )
2022-04-18 15:56:14 [42mnormalization[0m > select
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_unique_key,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_unique_key_scd,
2022-04-18 15:56:14 [42mnormalization[0m >     id,
2022-04-18 15:56:14 [42mnormalization[0m >     "time",
2022-04-18 15:56:14 [42mnormalization[0m >     type,
2022-04-18 15:56:14 [42mnormalization[0m >     metric,
2022-04-18 15:56:14 [42mnormalization[0m >     ticket_id,
2022-04-18 15:56:14 [42mnormalization[0m >     instance_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_start_at,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_end_at,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_active_row,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:14 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:14 [42mnormalization[0m >     _airbyte_ticket_metric_events_hashid
2022-04-18 15:56:14 [42mnormalization[0m > from dedup_data where _airbyte_row_num = 1
2022-04-18 15:56:14 [42mnormalization[0m >   );
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.314738 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.315014 (Thread-1): On model.airbyte_utils.brands: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.327568 (Thread-2): SQL status: SELECT in 0.46 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.334534 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.334695 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_fields_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_fields_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_fields_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.627771 (Thread-3): SQL status: SELECT in 0.32 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.630472 (Thread-3): On model.airbyte_utils.sla_policies: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.630664 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.630794 (Thread-3): On model.airbyte_utils.sla_policies: COMMIT
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.643934 (Thread-2): SQL status: SELECT in 0.31 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.652419 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.652601 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_fields_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_fields_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_fields_stg'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.684991 (Thread-1): SQL status: COMMIT in 0.37 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.685888 (Thread-1): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.686103 (Thread-1): On model.airbyte_utils.brands: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.686950 (Thread-1): 15:55:33 | 4 of 33 OK created incremental model zendesk_intercom.brands................................................. [INSERT 0 13 in 5.05s]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.687945 (Thread-1): Finished running node model.airbyte_utils.brands
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.688177 (Thread-1): Began running node model.airbyte_utils.ticket_metrics_scd
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.688670 (Thread-1): 15:55:33 | 10 of 33 START incremental model zendesk_intercom.ticket_metrics_scd......................................... [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.689460 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.689647 (Thread-1): Compiling model.airbyte_utils.ticket_metrics_scd
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.706990 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.707206 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.707352 (Thread-1): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.707471 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.758103 (Thread-3): SQL status: COMMIT in 0.13 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.759043 (Thread-3): finished collecting timing info
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.759265 (Thread-3): On model.airbyte_utils.sla_policies: Close
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.760125 (Thread-3): 15:55:33 | 6 of 33 OK created incremental model zendesk_intercom.sla_policies........................................... [INSERT 0 12 in 4.43s]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.761295 (Thread-3): Finished running node model.airbyte_utils.sla_policies
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.761567 (Thread-3): Began running node model.airbyte_utils.tickets_scd
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.762207 (Thread-3): 15:55:33 | 11 of 33 START incremental model zendesk_intercom.tickets_scd................................................ [RUN]
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.763047 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.763238 (Thread-3): Compiling model.airbyte_utils.tickets_scd
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.777051 (Thread-1): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.777246 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.777379 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.778913 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.779074 (Thread-3): On model.airbyte_utils.tickets_scd: BEGIN
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.779222 (Thread-3): Opening a new connection, currently in state closed
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.779343 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.843973 (Thread-3): SQL status: BEGIN in 0.06 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.844316 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:33.844460 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tickets_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tickets_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tickets_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.003494 (Thread-2): SQL status: SELECT in 0.35 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.006486 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.006677 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.006840 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.006988 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.007130 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.007274 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.007414 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.007554 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.007695 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.007833 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.007972 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.008111 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.008248 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.008422 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.008563 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.008700 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.008838 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.008974 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.009112 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.009248 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.009384 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.009519 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.009655 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.009795 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.009930 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.010064 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.010199 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.010334 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.010470 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.013679 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.013839 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_fields_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_fields_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_fields_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.385224 (Thread-3): SQL status: SELECT in 0.54 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.394289 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.394477 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tickets_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tickets_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tickets_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.410158 (Thread-4): SQL status: SELECT in 1.10 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.414384 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.414547 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd__dbt_tmp155532930967'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd__dbt_tmp155532930967'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd__dbt_tmp155532930967'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.442215 (Thread-1): SQL status: SELECT in 0.66 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.451814 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.452033 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.506316 (Thread-2): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.509320 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.509516 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.509670 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.509817 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.509963 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.510116 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.510266 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.510408 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.510551 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.510699 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.510840 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.510987 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.511126 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.511332 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.511471 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.511604 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.511734 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.511862 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.511990 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.512126 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.512275 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.512414 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.512554 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.512682 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.512810 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.512935 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.513081 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.513209 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.513335 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.513461 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.513592 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.513730 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.513858 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.513989 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.514174 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.514320 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.514463 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.514605 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.514747 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.514883 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.515332 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.515495 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.515639 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.515779 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.515918 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.516054 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.516189 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.516364 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.516512 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.516648 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.516785 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.516923 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.517058 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.517202 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.517338 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.517475 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.517610 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.517747 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.517887 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.518024 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.518231 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.518374 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.518520 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.522480 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.522644 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_fields_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_fields_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_fields_stg'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.778556 (Thread-4): SQL status: SELECT in 0.36 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.784602 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.784785 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.932724 (Thread-3): SQL status: SELECT in 0.54 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.942832 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.943172 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'tickets_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'tickets_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'tickets_stg'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.956385 (Thread-2): SQL status: SELECT in 0.43 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.959194 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.959379 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.959523 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.959667 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.959801 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.959933 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.960068 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.960202 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.960378 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.960511 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.960647 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.960778 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.960905 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.961032 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.961159 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.961285 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.961417 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.961552 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.961686 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.961814 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.961941 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.962067 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.962262 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.962455 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.962602 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.962730 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.962863 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.962992 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.963118 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.963273 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.963413 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.963557 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.963692 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.963828 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.963968 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.964102 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.964244 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.964421 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.964573 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.964706 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.964848 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.964982 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.965122 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.965258 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.965391 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.965530 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.965814 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.965990 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.966136 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.966273 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.966413 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.966547 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.966686 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.966828 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.966968 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.967107 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.967245 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.967383 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.971261 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.971442 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_fields_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_fields_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else external_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m >       from
2022-04-18 15:56:14 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:14 [42mnormalization[0m >       where
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >         and tablename = 'ticket_fields_stg'
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:14 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:14 [42mnormalization[0m >       union all
2022-04-18 15:56:14 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:14 [42mnormalization[0m >     )
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     select
2022-04-18 15:56:14 [42mnormalization[0m >       column_name,
2022-04-18 15:56:14 [42mnormalization[0m >       data_type,
2022-04-18 15:56:14 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     from unioned
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:14 [42mnormalization[0m >     
2022-04-18 15:56:14 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:14 [42mnormalization[0m >   
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.974907 (Thread-1): SQL status: SELECT in 0.52 seconds
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.982614 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:14 [42mnormalization[0m > 2022-04-18 15:55:34.982785 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >         select
2022-04-18 15:56:14 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:14 [42mnormalization[0m >           column_name,
2022-04-18 15:56:14 [42mnormalization[0m >           data_type,
2022-04-18 15:56:14 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:14 [42mnormalization[0m >         where table_name = 'ticket_metrics_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:14 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:14 [42mnormalization[0m >         col_name,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:14 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:14 [42mnormalization[0m >           else col_type
2022-04-18 15:56:14 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:14 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:14 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:14 [42mnormalization[0m >             '')::int
2022-04-18 15:56:14 [42mnormalization[0m >           else null
2022-04-18 15:56:14 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:14 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:14 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:14 [42mnormalization[0m >       where view_name = 'ticket_metrics_stg'
2022-04-18 15:56:14 [42mnormalization[0m >     ),
2022-04-18 15:56:14 [42mnormalization[0m > 
2022-04-18 15:56:14 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:14 [42mnormalization[0m >       select
2022-04-18 15:56:14 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:14 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:14 [42mnormalization[0m >         columnname,
2022-04-18 15:56:14 [42mnormalization[0m >         case
2022-04-18 15:56:14 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_stg'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.155529 (Thread-4): SQL status: SELECT in 0.37 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.163294 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.163514 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd__dbt_tmp155532930967'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd__dbt_tmp155532930967'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd__dbt_tmp155532930967'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.439214 (Thread-3): SQL status: SELECT in 0.50 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.442456 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.442644 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.442786 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.442922 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.443052 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.443180 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.443306 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.443428 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.443551 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.443675 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.443799 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.443920 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.444047 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.444169 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.444313 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.444444 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.444568 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.444689 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.444811 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.444934 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.445066 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.445188 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.445309 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.445439 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.445561 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.445682 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.445803 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.445925 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.446047 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.446169 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.446291 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.446411 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.446531 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.446651 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.446777 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.446899 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.447026 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.447148 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.447283 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.447407 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.450518 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.450726 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.450949 (Thread-2): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.453451 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.453630 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.453777 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.453919 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.454063 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.454199 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.454328 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.454453 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.454582 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.454705 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.454827 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.454949 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.455071 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.455192 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.455323 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.455445 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.455572 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.455700 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.455821 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.455941 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.456067 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.456188 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.456334 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.456460 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.456586 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.456707 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.456830 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.456953 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.457079 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.457229 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.457369 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.457506 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.457635 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.457768 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.457903 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.458032 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.458160 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.458288 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.458416 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.458542 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.458669 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.458801 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.458935 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.459064 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.459203 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.459373 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.459529 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.459679 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.459826 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.459973 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.460122 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.460540 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.460682 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.460816 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.460945 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.461073 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.461200 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.461327 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.464488 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_fields_scd"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.464867 (Thread-2): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.471770 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.471941 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.482816 (Thread-1): SQL status: SELECT in 0.50 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.485510 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.485686 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.485834 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.485974 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.486111 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.486256 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.486394 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.486529 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.486670 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.486813 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.486947 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.487075 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.487198 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.487321 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.487443 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.487566 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.487686 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.487808 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.487929 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.488058 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.488180 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.488331 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.488467 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.488594 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.488714 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.488834 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.488955 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.489076 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.489197 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.489326 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.489453 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.492297 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.492452 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.565773 (Thread-4): SQL status: SELECT in 0.40 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.657609 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.657905 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.902878 (Thread-1): SQL status: SELECT in 0.41 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.905908 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.906083 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.906226 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.906364 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.906507 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.906649 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.906784 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.906918 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.907052 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.907185 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.907318 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.907517 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.907703 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.907866 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.908019 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.908219 (Thread-2): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.908469 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.913485 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.913720 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.913895 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:15 [42mnormalization[0m >     "ticket_fields_scd__dbt_tmp155535468764"
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >       compound sortkey(_airbyte_active_row,_airbyte_unique_key_scd,_airbyte_emitted_at)
2022-04-18 15:56:15 [42mnormalization[0m >   as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: ref('ticket_fields_stg')
2022-04-18 15:56:15 [42mnormalization[0m > with
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > new_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- retrieve incremental "new" data
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         *
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake"._airbyte_zendesk_intercom."ticket_fields_stg"
2022-04-18 15:56:15 [42mnormalization[0m >     -- ticket_fields from "datalake".zendesk_intercom._airbyte_raw_ticket_fields
2022-04-18 15:56:15 [42mnormalization[0m >     where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:15 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_fields_scd"),
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     true)
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > new_data_ids as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- build a subset of _airbyte_unique_key from rows that are new
2022-04-18 15:56:15 [42mnormalization[0m >     select distinct
2022-04-18 15:56:15 [42mnormalization[0m >         md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key
2022-04-18 15:56:15 [42mnormalization[0m >     from new_data
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > empty_new_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- build an empty table to only keep the table's column types
2022-04-18 15:56:15 [42mnormalization[0m >     select * from new_data where 1 = 0
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > previous_active_scd_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- retrieve "incomplete old" data that needs to be updated with an end date because of new changes
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         this_data."_airbyte_ticket_fields_hashid",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."tag",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."url",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."type",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."title",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."active",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."position",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."required",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."raw_title",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."removable",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."created_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."description",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."sub_type_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."raw_description",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."title_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."agent_description",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."visible_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."editable_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."required_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."raw_title_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."collapsed_for_agents",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."custom_field_options",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."system_field_options",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."regexp_for_validation",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."_airbyte_ab_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."_airbyte_emitted_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."_airbyte_normalized_at"
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_fields_scd" as this_data
2022-04-18 15:56:15 [42mnormalization[0m >     -- make a join with new_data using primary key to filter active data that need to be updated only
2022-04-18 15:56:15 [42mnormalization[0m >     join new_data_ids on this_data._airbyte_unique_key = new_data_ids._airbyte_unique_key
2022-04-18 15:56:15 [42mnormalization[0m >     -- force left join to NULL values (we just need to transfer column types only for the star_intersect macro on schema changes)
2022-04-18 15:56:15 [42mnormalization[0m >     left join empty_new_data as inc_data on this_data._airbyte_ab_id = inc_data._airbyte_ab_id
2022-04-18 15:56:15 [42mnormalization[0m >     where _airbyte_active_row = 1
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > input_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     select "_airbyte_ticket_fields_hashid",
2022-04-18 15:56:15 [42mnormalization[0m >   "id",
2022-04-18 15:56:15 [42mnormalization[0m >   "tag",
2022-04-18 15:56:15 [42mnormalization[0m >   "url",
2022-04-18 15:56:15 [42mnormalization[0m >   "type",
2022-04-18 15:56:15 [42mnormalization[0m >   "title",
2022-04-18 15:56:15 [42mnormalization[0m >   "active",
2022-04-18 15:56:15 [42mnormalization[0m >   "position",
2022-04-18 15:56:15 [42mnormalization[0m >   "required",
2022-04-18 15:56:15 [42mnormalization[0m >   "raw_title",
2022-04-18 15:56:15 [42mnormalization[0m >   "removable",
2022-04-18 15:56:15 [42mnormalization[0m >   "created_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "description",
2022-04-18 15:56:15 [42mnormalization[0m >   "sub_type_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "raw_description",
2022-04-18 15:56:15 [42mnormalization[0m >   "title_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "agent_description",
2022-04-18 15:56:15 [42mnormalization[0m >   "visible_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "editable_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "required_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "raw_title_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "collapsed_for_agents",
2022-04-18 15:56:15 [42mnormalization[0m >   "custom_field_options",
2022-04-18 15:56:15 [42mnormalization[0m >   "system_field_options",
2022-04-18 15:56:15 [42mnormalization[0m >   "regexp_for_validation",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_ab_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_emitted_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_normalized_at" from new_data
2022-04-18 15:56:15 [42mnormalization[0m >     union all
2022-04-18 15:56:15 [42mnormalization[0m >     select "_airbyte_ticket_fields_hashid",
2022-04-18 15:56:15 [42mnormalization[0m >   "id",
2022-04-18 15:56:15 [42mnormalization[0m >   "tag",
2022-04-18 15:56:15 [42mnormalization[0m >   "url",
2022-04-18 15:56:15 [42mnormalization[0m >   "type",
2022-04-18 15:56:15 [42mnormalization[0m >   "title",
2022-04-18 15:56:15 [42mnormalization[0m >   "active",
2022-04-18 15:56:15 [42mnormalization[0m >   "position",
2022-04-18 15:56:15 [42mnormalization[0m >   "required",
2022-04-18 15:56:15 [42mnormalization[0m >   "raw_title",
2022-04-18 15:56:15 [42mnormalization[0m >   "removable",
2022-04-18 15:56:15 [42mnormalization[0m >   "created_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "description",
2022-04-18 15:56:15 [42mnormalization[0m >   "sub_type_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "raw_description",
2022-04-18 15:56:15 [42mnormalization[0m >   "title_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "agent_description",
2022-04-18 15:56:15 [42mnormalization[0m >   "visible_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "editable_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "required_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "raw_title_in_portal",
2022-04-18 15:56:15 [42mnormalization[0m >   "collapsed_for_agents",
2022-04-18 15:56:15 [42mnormalization[0m >   "custom_field_options",
2022-04-18 15:56:15 [42mnormalization[0m >   "system_field_options",
2022-04-18 15:56:15 [42mnormalization[0m >   "regexp_for_validation",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_ab_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_emitted_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_normalized_at" from previous_active_scd_data
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > scd_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- SQL model to build a Type 2 Slowly Changing Dimension (SCD) table for each record identified by their primary key
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >       id,
2022-04-18 15:56:15 [42mnormalization[0m >       "tag",
2022-04-18 15:56:15 [42mnormalization[0m >       url,
2022-04-18 15:56:15 [42mnormalization[0m >       type,
2022-04-18 15:56:15 [42mnormalization[0m >       title,
2022-04-18 15:56:15 [42mnormalization[0m >       active,
2022-04-18 15:56:15 [42mnormalization[0m >       position,
2022-04-18 15:56:15 [42mnormalization[0m >       required,
2022-04-18 15:56:15 [42mnormalization[0m >       raw_title,
2022-04-18 15:56:15 [42mnormalization[0m >       removable,
2022-04-18 15:56:15 [42mnormalization[0m >       created_at,
2022-04-18 15:56:15 [42mnormalization[0m >       updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >       description,
2022-04-18 15:56:15 [42mnormalization[0m >       sub_type_id,
2022-04-18 15:56:15 [42mnormalization[0m >       raw_description,
2022-04-18 15:56:15 [42mnormalization[0m >       title_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >       agent_description,
2022-04-18 15:56:15 [42mnormalization[0m >       visible_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >       editable_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >       required_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >       raw_title_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >       collapsed_for_agents,
2022-04-18 15:56:15 [42mnormalization[0m >       custom_field_options,
2022-04-18 15:56:15 [42mnormalization[0m >       system_field_options,
2022-04-18 15:56:15 [42mnormalization[0m >       regexp_for_validation,
2022-04-18 15:56:15 [42mnormalization[0m >       updated_at as _airbyte_start_at,
2022-04-18 15:56:15 [42mnormalization[0m >       lag(updated_at) over (
2022-04-18 15:56:15 [42mnormalization[0m >         partition by id
2022-04-18 15:56:15 [42mnormalization[0m >         order by
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at is null asc,
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at desc,
2022-04-18 15:56:15 [42mnormalization[0m >             _airbyte_emitted_at desc
2022-04-18 15:56:15 [42mnormalization[0m >       ) as _airbyte_end_at,
2022-04-18 15:56:15 [42mnormalization[0m >       case when row_number() over (
2022-04-18 15:56:15 [42mnormalization[0m >         partition by id
2022-04-18 15:56:15 [42mnormalization[0m >         order by
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at is null asc,
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at desc,
2022-04-18 15:56:15 [42mnormalization[0m >             _airbyte_emitted_at desc
2022-04-18 15:56:15 [42mnormalization[0m >       ) = 1 then 1 else 0 end as _airbyte_active_row,
2022-04-18 15:56:15 [42mnormalization[0m >       _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >       _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >       _airbyte_ticket_fields_hashid
2022-04-18 15:56:15 [42mnormalization[0m >     from input_data
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > dedup_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         -- we need to ensure de-duplicated rows for merge/update queries
2022-04-18 15:56:15 [42mnormalization[0m >         -- additionally, we generate a unique key for the scd table
2022-04-18 15:56:15 [42mnormalization[0m >         row_number() over (
2022-04-18 15:56:15 [42mnormalization[0m >             partition by
2022-04-18 15:56:15 [42mnormalization[0m >                 _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >                 _airbyte_start_at,
2022-04-18 15:56:15 [42mnormalization[0m >                 _airbyte_emitted_at
2022-04-18 15:56:15 [42mnormalization[0m >             order by _airbyte_active_row desc, _airbyte_ab_id
2022-04-18 15:56:15 [42mnormalization[0m >         ) as _airbyte_row_num,
2022-04-18 15:56:15 [42mnormalization[0m >         md5(cast(coalesce(cast(_airbyte_unique_key as varchar), '') || '-' || coalesce(cast(_airbyte_start_at as varchar), '') || '-' || coalesce(cast(_airbyte_emitted_at as varchar), '') as varchar)) as _airbyte_unique_key_scd,
2022-04-18 15:56:15 [42mnormalization[0m >         scd_data.*
2022-04-18 15:56:15 [42mnormalization[0m >     from scd_data
2022-04-18 15:56:15 [42mnormalization[0m > )
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_unique_key_scd,
2022-04-18 15:56:15 [42mnormalization[0m >     id,
2022-04-18 15:56:15 [42mnormalization[0m >     "tag",
2022-04-18 15:56:15 [42mnormalization[0m >     url,
2022-04-18 15:56:15 [42mnormalization[0m >     type,
2022-04-18 15:56:15 [42mnormalization[0m >     title,
2022-04-18 15:56:15 [42mnormalization[0m >     active,
2022-04-18 15:56:15 [42mnormalization[0m >     position,
2022-04-18 15:56:15 [42mnormalization[0m >     required,
2022-04-18 15:56:15 [42mnormalization[0m >     raw_title,
2022-04-18 15:56:15 [42mnormalization[0m >     removable,
2022-04-18 15:56:15 [42mnormalization[0m >     created_at,
2022-04-18 15:56:15 [42mnormalization[0m >     updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     description,
2022-04-18 15:56:15 [42mnormalization[0m >     sub_type_id,
2022-04-18 15:56:15 [42mnormalization[0m >     raw_description,
2022-04-18 15:56:15 [42mnormalization[0m >     title_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >     agent_description,
2022-04-18 15:56:15 [42mnormalization[0m >     visible_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >     editable_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >     required_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >     raw_title_in_portal,
2022-04-18 15:56:15 [42mnormalization[0m >     collapsed_for_agents,
2022-04-18 15:56:15 [42mnormalization[0m >     custom_field_options,
2022-04-18 15:56:15 [42mnormalization[0m >     system_field_options,
2022-04-18 15:56:15 [42mnormalization[0m >     regexp_for_validation,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_start_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_end_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_active_row,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ticket_fields_hashid
2022-04-18 15:56:15 [42mnormalization[0m > from dedup_data where _airbyte_row_num = 1
2022-04-18 15:56:15 [42mnormalization[0m >   );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.914093 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.914358 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.914499 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.914627 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.914749 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.914870 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.914991 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.915140 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.915269 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.915392 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.915513 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.915634 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.915759 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.915880 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.916000 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.916119 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.916238 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.916392 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.916516 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.916663 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.916798 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.916930 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.917061 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.917203 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.917335 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.917463 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.917592 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.917721 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.917852 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.917982 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.918112 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.918243 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.918377 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.918510 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.918641 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.918773 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.918904 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.919033 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.919171 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.919300 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.919431 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.919562 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.919693 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.919821 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.919956 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.920085 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.920216 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.920376 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.920804 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.920960 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.924368 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.924522 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_stg'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_stg'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_stg'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.937310 (Thread-3): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.940518 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.940687 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.940832 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.940972 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.941115 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.941256 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.941394 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.941529 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.941672 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.941809 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.941942 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.942075 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.942207 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.942340 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.942471 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.942610 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.942743 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.942876 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.943008 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.943139 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.943271 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.943402 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.943533 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.943660 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.943790 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.943923 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.944057 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.944190 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.944353 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.944489 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.944621 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.944754 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.944886 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.945019 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.945153 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.945295 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.945429 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.945561 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.945693 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.945829 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.945963 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.946095 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.946228 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.946359 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.946491 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.946658 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.946807 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.946951 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.947093 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.947236 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.947378 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.947519 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.947660 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.947800 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.947941 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.948089 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.948231 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.948413 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.948560 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.948702 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.949082 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.949245 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.949393 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.949545 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.949690 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.949833 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.949974 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.950116 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.950256 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.950401 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.950541 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.950681 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.950825 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.950966 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.951103 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.951243 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.951382 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.951521 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.951662 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.951802 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.951966 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.952108 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.952270 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.952421 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.952564 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.956416 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:35.956583 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'tickets_stg'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'tickets_stg'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'tickets_stg'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.125451 (Thread-4): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.129800 (Thread-4): 
2022-04-18 15:56:15 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metric_events_scd":
2022-04-18 15:56:15 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:15 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:15 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:15 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.132982 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.133141 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.361374 (Thread-1): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.364496 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.364674 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.364813 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.364946 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.365085 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.365214 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.365340 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.365466 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.365590 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.365712 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.365837 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.365959 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.366080 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.366203 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.366324 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.366450 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.366574 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.366697 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.366821 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.366944 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.367065 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.367188 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.367348 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.367474 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.367596 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.367717 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.367844 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.367967 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.368089 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.368210 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.368363 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.368517 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.368654 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.368787 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.368917 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.369046 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.369181 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.369309 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.369442 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.369572 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.369701 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.369830 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.369961 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.370091 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.370221 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.370350 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.370479 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.370614 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.370743 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.370872 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.371001 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.371130 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.371258 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.371395 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.371532 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.371663 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.371794 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.371928 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.372060 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.372190 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.372348 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.372484 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.376226 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.376413 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_stg'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_stg'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_stg'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.426064 (Thread-3): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.431875 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.432068 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.432207 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.432369 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.432499 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.432625 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.432754 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.432881 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.433006 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.433129 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.433257 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.433379 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.433501 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.433623 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.433745 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.433871 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.433994 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.434118 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.434242 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.434365 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.434488 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.434616 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.434738 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.434861 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.434984 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.435105 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.435238 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.435362 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.435486 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.435607 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.435729 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.435853 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.435978 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.436100 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.436222 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.436373 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.436498 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.436619 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.436741 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.436863 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.437013 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.437148 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.437278 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.437412 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.437540 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.437669 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.437798 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.437933 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.438063 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.438192 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.438321 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.438450 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.438584 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.438719 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.438847 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.438975 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.439108 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.439237 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.439366 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.439493 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.439620 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.439749 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.439876 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.440006 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.440139 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.440291 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.440431 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.440562 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.440689 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.440817 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.440948 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.441330 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.441484 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.441619 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.441750 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.441878 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.442011 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.442138 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.442265 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.442390 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.445819 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.445982 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'tickets_stg'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'tickets_stg'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'tickets_stg'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = '_airbyte_zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.567049 (Thread-4): SQL status: SELECT in 0.43 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.570030 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_metric_events_scd"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.570515 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.570656 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       delete
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_metric_events_scd"
2022-04-18 15:56:15 [42mnormalization[0m >     where (_airbyte_unique_key_scd) in (
2022-04-18 15:56:15 [42mnormalization[0m >         select (_airbyte_unique_key_scd)
2022-04-18 15:56:15 [42mnormalization[0m >         from "ticket_metric_events_scd__dbt_tmp155532930967"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metric_events_scd" ("_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "time", "type", "metric", "ticket_id", "instance_id", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metric_events_hashid")
2022-04-18 15:56:15 [42mnormalization[0m >     (
2022-04-18 15:56:15 [42mnormalization[0m >        select "_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "time", "type", "metric", "ticket_id", "instance_id", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metric_events_hashid"
2022-04-18 15:56:15 [42mnormalization[0m >        from "ticket_metric_events_scd__dbt_tmp155532930967"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.804589 (Thread-4): SQL status: INSERT 0 6872 in 0.23 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.809903 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.810095 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.848326 (Thread-3): SQL status: SELECT in 0.40 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.851559 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.851747 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.851906 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.852057 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.852281 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.852513 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.854851 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.855043 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.855227 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.855401 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.855579 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.855746 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.855918 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.856087 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.856276 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.856468 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.856648 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.856870 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.857065 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.857258 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.857478 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.857671 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.857881 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.858063 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.858263 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.858444 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.858614 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.858784 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.858950 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.859113 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.859298 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.859472 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.859635 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.859798 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.859960 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.860122 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.860319 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.860499 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.860665 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.860827 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.860989 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.861151 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.861311 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.861471 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.861631 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.861793 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.861953 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.862118 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.862279 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.862439 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.862598 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.862757 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.862916 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.863350 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.863522 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.863695 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.863860 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.864024 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.864186 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.864403 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.864573 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.864739 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.864904 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.865067 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.865229 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.865406 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.865584 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.865748 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.865987 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.866174 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.866372 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.866544 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.866718 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.866882 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.867051 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.867214 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.867385 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.867573 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.867743 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.867917 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.868104 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.868295 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.868494 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.868672 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.868843 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.869013 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.869184 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.869371 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.869547 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.869716 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.869887 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.870054 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.870229 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.870394 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.870564 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.870732 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.870902 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.871070 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.871271 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.871450 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.871623 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.871789 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.871957 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.872124 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.872310 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.872519 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.872701 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.872879 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.873049 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.873219 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.873387 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.873554 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.873743 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.873938 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.874121 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.874293 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.874563 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.874744 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.874915 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.875085 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.875256 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.875428 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.875604 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.875771 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.875941 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.876109 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.876306 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.876502 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.876927 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.877113 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.880123 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_scd"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.880377 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.880693 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.880878 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.881038 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.881190 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.881429 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.881612 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.881778 (Thread-1): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.881976 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.889848 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.890090 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.890314 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.890753 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.891074 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.891230 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.891374 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.894360 (Thread-3): Writing injected SQL for node "model.airbyte_utils.tickets_scd"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.894706 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.902592 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:36.902763 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.179954 (Thread-4): SQL status: SELECT in 0.37 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.182609 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.182942 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.183081 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         drop view _airbyte_zendesk_intercom.ticket_metric_events_stg
2022-04-18 15:56:15 [42mnormalization[0m >       
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.192747 (Thread-4): SQL status: DROP VIEW in 0.01 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.193780 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.193933 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.194061 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.285674 (Thread-4): SQL status: COMMIT in 0.09 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.286607 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.286832 (Thread-4): On model.airbyte_utils.ticket_metric_events_scd: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.287732 (Thread-4): 15:55:37 | 8 of 33 OK created incremental model zendesk_intercom.ticket_metric_events_scd............................... [INSERT 0 6872 in 7.12s]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.288231 (Thread-4): Finished running node model.airbyte_utils.ticket_metric_events_scd
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.288540 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.289037 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.289245 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.298941 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.299583 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.300077 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.300333 (Thread-4): Began running node model.airbyte_utils.sla_policies_policy_metrics_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.300870 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.301146 (Thread-4): Compiling model.airbyte_utils.sla_policies_policy_metrics_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.310814 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.311013 (Thread-4): On model.airbyte_utils.sla_policies_policy_metrics_ab1: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.311167 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.311290 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.380476 (Thread-4): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.380792 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.380939 (Thread-4): On model.airbyte_utils.sla_policies_policy_metrics_ab1: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics_ab1"} */
2022-04-18 15:56:15 [42mnormalization[0m > with max_value as (
2022-04-18 15:56:15 [42mnormalization[0m >             select max(json_array_length(policy_metrics, true)) as max_number_of_items
2022-04-18 15:56:15 [42mnormalization[0m >             from "datalake".zendesk_intercom."sla_policies"
2022-04-18 15:56:15 [42mnormalization[0m >         )
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >             case when max_number_of_items is not null and max_number_of_items > 1
2022-04-18 15:56:15 [42mnormalization[0m >             then max_number_of_items
2022-04-18 15:56:15 [42mnormalization[0m >             else 1 end as max_number_of_items
2022-04-18 15:56:15 [42mnormalization[0m >         from max_value
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.407264 (Thread-1): SQL status: SELECT in 0.52 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.412812 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.413011 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:15 [42mnormalization[0m >     "ticket_metrics_scd__dbt_tmp155536886011"
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >       compound sortkey(_airbyte_active_row,_airbyte_unique_key_scd,_airbyte_emitted_at)
2022-04-18 15:56:15 [42mnormalization[0m >   as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: ref('ticket_metrics_stg')
2022-04-18 15:56:15 [42mnormalization[0m > with
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > new_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- retrieve incremental "new" data
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         *
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake"._airbyte_zendesk_intercom."ticket_metrics_stg"
2022-04-18 15:56:15 [42mnormalization[0m >     -- ticket_metrics from "datalake".zendesk_intercom._airbyte_raw_ticket_metrics
2022-04-18 15:56:15 [42mnormalization[0m >     where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:15 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metrics_scd"),
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     true)
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > new_data_ids as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- build a subset of _airbyte_unique_key from rows that are new
2022-04-18 15:56:15 [42mnormalization[0m >     select distinct
2022-04-18 15:56:15 [42mnormalization[0m >         md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key
2022-04-18 15:56:15 [42mnormalization[0m >     from new_data
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > empty_new_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- build an empty table to only keep the table's column types
2022-04-18 15:56:15 [42mnormalization[0m >     select * from new_data where 1 = 0
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > previous_active_scd_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- retrieve "incomplete old" data that needs to be updated with an end date because of new changes
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         this_data."_airbyte_ticket_metrics_hashid",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."url",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."time",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."type",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."metric",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."status",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."reopens",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."replies",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."solved_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."ticket_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."created_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."assigned_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."instance_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."group_stations",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."assignee_stations",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."status_updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."assignee_updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."requester_updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."initially_assigned_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."reply_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."latest_comment_added_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."on_hold_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."agent_wait_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."requester_wait_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."full_resolution_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."first_resolution_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."_airbyte_ab_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."_airbyte_emitted_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."_airbyte_normalized_at"
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_metrics_scd" as this_data
2022-04-18 15:56:15 [42mnormalization[0m >     -- make a join with new_data using primary key to filter active data that need to be updated only
2022-04-18 15:56:15 [42mnormalization[0m >     join new_data_ids on this_data._airbyte_unique_key = new_data_ids._airbyte_unique_key
2022-04-18 15:56:15 [42mnormalization[0m >     -- force left join to NULL values (we just need to transfer column types only for the star_intersect macro on schema changes)
2022-04-18 15:56:15 [42mnormalization[0m >     left join empty_new_data as inc_data on this_data._airbyte_ab_id = inc_data._airbyte_ab_id
2022-04-18 15:56:15 [42mnormalization[0m >     where _airbyte_active_row = 1
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > input_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     select "_airbyte_ticket_metrics_hashid",
2022-04-18 15:56:15 [42mnormalization[0m >   "id",
2022-04-18 15:56:15 [42mnormalization[0m >   "url",
2022-04-18 15:56:15 [42mnormalization[0m >   "time",
2022-04-18 15:56:15 [42mnormalization[0m >   "type",
2022-04-18 15:56:15 [42mnormalization[0m >   "metric",
2022-04-18 15:56:15 [42mnormalization[0m >   "status",
2022-04-18 15:56:15 [42mnormalization[0m >   "reopens",
2022-04-18 15:56:15 [42mnormalization[0m >   "replies",
2022-04-18 15:56:15 [42mnormalization[0m >   "solved_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "ticket_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "created_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "assigned_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "instance_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "group_stations",
2022-04-18 15:56:15 [42mnormalization[0m >   "assignee_stations",
2022-04-18 15:56:15 [42mnormalization[0m >   "status_updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "assignee_updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "requester_updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "initially_assigned_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "reply_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "latest_comment_added_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "on_hold_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "agent_wait_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "requester_wait_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "full_resolution_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "first_resolution_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_ab_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_emitted_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_normalized_at" from new_data
2022-04-18 15:56:15 [42mnormalization[0m >     union all
2022-04-18 15:56:15 [42mnormalization[0m >     select "_airbyte_ticket_metrics_hashid",
2022-04-18 15:56:15 [42mnormalization[0m >   "id",
2022-04-18 15:56:15 [42mnormalization[0m >   "url",
2022-04-18 15:56:15 [42mnormalization[0m >   "time",
2022-04-18 15:56:15 [42mnormalization[0m >   "type",
2022-04-18 15:56:15 [42mnormalization[0m >   "metric",
2022-04-18 15:56:15 [42mnormalization[0m >   "status",
2022-04-18 15:56:15 [42mnormalization[0m >   "reopens",
2022-04-18 15:56:15 [42mnormalization[0m >   "replies",
2022-04-18 15:56:15 [42mnormalization[0m >   "solved_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "ticket_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "created_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "assigned_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "instance_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "group_stations",
2022-04-18 15:56:15 [42mnormalization[0m >   "assignee_stations",
2022-04-18 15:56:15 [42mnormalization[0m >   "status_updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "assignee_updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "requester_updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "initially_assigned_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "reply_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "latest_comment_added_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "on_hold_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "agent_wait_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "requester_wait_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "full_resolution_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "first_resolution_time_in_minutes",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_ab_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_emitted_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_normalized_at" from previous_active_scd_data
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > scd_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- SQL model to build a Type 2 Slowly Changing Dimension (SCD) table for each record identified by their primary key
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >       id,
2022-04-18 15:56:15 [42mnormalization[0m >       url,
2022-04-18 15:56:15 [42mnormalization[0m >       "time",
2022-04-18 15:56:15 [42mnormalization[0m >       type,
2022-04-18 15:56:15 [42mnormalization[0m >       metric,
2022-04-18 15:56:15 [42mnormalization[0m >       status,
2022-04-18 15:56:15 [42mnormalization[0m >       reopens,
2022-04-18 15:56:15 [42mnormalization[0m >       replies,
2022-04-18 15:56:15 [42mnormalization[0m >       solved_at,
2022-04-18 15:56:15 [42mnormalization[0m >       ticket_id,
2022-04-18 15:56:15 [42mnormalization[0m >       created_at,
2022-04-18 15:56:15 [42mnormalization[0m >       updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >       assigned_at,
2022-04-18 15:56:15 [42mnormalization[0m >       instance_id,
2022-04-18 15:56:15 [42mnormalization[0m >       group_stations,
2022-04-18 15:56:15 [42mnormalization[0m >       assignee_stations,
2022-04-18 15:56:15 [42mnormalization[0m >       status_updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >       assignee_updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >       requester_updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >       initially_assigned_at,
2022-04-18 15:56:15 [42mnormalization[0m >       reply_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >       latest_comment_added_at,
2022-04-18 15:56:15 [42mnormalization[0m >       on_hold_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >       agent_wait_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >       requester_wait_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >       full_resolution_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >       first_resolution_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >       updated_at as _airbyte_start_at,
2022-04-18 15:56:15 [42mnormalization[0m >       lag(updated_at) over (
2022-04-18 15:56:15 [42mnormalization[0m >         partition by id
2022-04-18 15:56:15 [42mnormalization[0m >         order by
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at is null asc,
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at desc,
2022-04-18 15:56:15 [42mnormalization[0m >             _airbyte_emitted_at desc
2022-04-18 15:56:15 [42mnormalization[0m >       ) as _airbyte_end_at,
2022-04-18 15:56:15 [42mnormalization[0m >       case when row_number() over (
2022-04-18 15:56:15 [42mnormalization[0m >         partition by id
2022-04-18 15:56:15 [42mnormalization[0m >         order by
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at is null asc,
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at desc,
2022-04-18 15:56:15 [42mnormalization[0m >             _airbyte_emitted_at desc
2022-04-18 15:56:15 [42mnormalization[0m >       ) = 1 then 1 else 0 end as _airbyte_active_row,
2022-04-18 15:56:15 [42mnormalization[0m >       _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >       _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >       _airbyte_ticket_metrics_hashid
2022-04-18 15:56:15 [42mnormalization[0m >     from input_data
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > dedup_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         -- we need to ensure de-duplicated rows for merge/update queries
2022-04-18 15:56:15 [42mnormalization[0m >         -- additionally, we generate a unique key for the scd table
2022-04-18 15:56:15 [42mnormalization[0m >         row_number() over (
2022-04-18 15:56:15 [42mnormalization[0m >             partition by
2022-04-18 15:56:15 [42mnormalization[0m >                 _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >                 _airbyte_start_at,
2022-04-18 15:56:15 [42mnormalization[0m >                 _airbyte_emitted_at
2022-04-18 15:56:15 [42mnormalization[0m >             order by _airbyte_active_row desc, _airbyte_ab_id
2022-04-18 15:56:15 [42mnormalization[0m >         ) as _airbyte_row_num,
2022-04-18 15:56:15 [42mnormalization[0m >         md5(cast(coalesce(cast(_airbyte_unique_key as varchar), '') || '-' || coalesce(cast(_airbyte_start_at as varchar), '') || '-' || coalesce(cast(_airbyte_emitted_at as varchar), '') as varchar)) as _airbyte_unique_key_scd,
2022-04-18 15:56:15 [42mnormalization[0m >         scd_data.*
2022-04-18 15:56:15 [42mnormalization[0m >     from scd_data
2022-04-18 15:56:15 [42mnormalization[0m > )
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_unique_key_scd,
2022-04-18 15:56:15 [42mnormalization[0m >     id,
2022-04-18 15:56:15 [42mnormalization[0m >     url,
2022-04-18 15:56:15 [42mnormalization[0m >     "time",
2022-04-18 15:56:15 [42mnormalization[0m >     type,
2022-04-18 15:56:15 [42mnormalization[0m >     metric,
2022-04-18 15:56:15 [42mnormalization[0m >     status,
2022-04-18 15:56:15 [42mnormalization[0m >     reopens,
2022-04-18 15:56:15 [42mnormalization[0m >     replies,
2022-04-18 15:56:15 [42mnormalization[0m >     solved_at,
2022-04-18 15:56:15 [42mnormalization[0m >     ticket_id,
2022-04-18 15:56:15 [42mnormalization[0m >     created_at,
2022-04-18 15:56:15 [42mnormalization[0m >     updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     assigned_at,
2022-04-18 15:56:15 [42mnormalization[0m >     instance_id,
2022-04-18 15:56:15 [42mnormalization[0m >     group_stations,
2022-04-18 15:56:15 [42mnormalization[0m >     assignee_stations,
2022-04-18 15:56:15 [42mnormalization[0m >     status_updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     assignee_updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     requester_updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     initially_assigned_at,
2022-04-18 15:56:15 [42mnormalization[0m >     reply_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     latest_comment_added_at,
2022-04-18 15:56:15 [42mnormalization[0m >     on_hold_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     agent_wait_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     requester_wait_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     full_resolution_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     first_resolution_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_start_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_end_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_active_row,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid
2022-04-18 15:56:15 [42mnormalization[0m > from dedup_data where _airbyte_row_num = 1
2022-04-18 15:56:15 [42mnormalization[0m >   );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.417798 (Thread-3): SQL status: SELECT in 0.51 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.422728 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.422888 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:15 [42mnormalization[0m >     "tickets_scd__dbt_tmp155536899642"
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >       compound sortkey(_airbyte_active_row,_airbyte_unique_key_scd,_airbyte_emitted_at)
2022-04-18 15:56:15 [42mnormalization[0m >   as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: ref('tickets_stg')
2022-04-18 15:56:15 [42mnormalization[0m > with
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > new_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- retrieve incremental "new" data
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         *
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake"._airbyte_zendesk_intercom."tickets_stg"
2022-04-18 15:56:15 [42mnormalization[0m >     -- tickets from "datalake".zendesk_intercom._airbyte_raw_tickets
2022-04-18 15:56:15 [42mnormalization[0m >     where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:15 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > )) from "datalake".zendesk_intercom."tickets_scd"),
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     true)
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > new_data_ids as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- build a subset of _airbyte_unique_key from rows that are new
2022-04-18 15:56:15 [42mnormalization[0m >     select distinct
2022-04-18 15:56:15 [42mnormalization[0m >         md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key
2022-04-18 15:56:15 [42mnormalization[0m >     from new_data
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > empty_new_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- build an empty table to only keep the table's column types
2022-04-18 15:56:15 [42mnormalization[0m >     select * from new_data where 1 = 0
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > previous_active_scd_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- retrieve "incomplete old" data that needs to be updated with an end date because of new changes
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         this_data."_airbyte_tickets_hashid",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."url",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."via",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."tags",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."type",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."due_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."status",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."subject",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."brand_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."group_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."priority",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."is_public",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."recipient",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."created_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."problem_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."assignee_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."description",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."external_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."raw_subject",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."email_cc_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."follower_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."followup_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."requester_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."submitter_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."custom_fields",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."has_incidents",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."forum_topic_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."ticket_form_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."organization_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."collaborator_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."allow_attachments",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."allow_channelback",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."generated_timestamp",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."satisfaction_rating",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."sharing_agreement_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."_airbyte_ab_id",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."_airbyte_emitted_at",
2022-04-18 15:56:15 [42mnormalization[0m >   this_data."_airbyte_normalized_at"
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."tickets_scd" as this_data
2022-04-18 15:56:15 [42mnormalization[0m >     -- make a join with new_data using primary key to filter active data that need to be updated only
2022-04-18 15:56:15 [42mnormalization[0m >     join new_data_ids on this_data._airbyte_unique_key = new_data_ids._airbyte_unique_key
2022-04-18 15:56:15 [42mnormalization[0m >     -- force left join to NULL values (we just need to transfer column types only for the star_intersect macro on schema changes)
2022-04-18 15:56:15 [42mnormalization[0m >     left join empty_new_data as inc_data on this_data._airbyte_ab_id = inc_data._airbyte_ab_id
2022-04-18 15:56:15 [42mnormalization[0m >     where _airbyte_active_row = 1
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > input_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     select "_airbyte_tickets_hashid",
2022-04-18 15:56:15 [42mnormalization[0m >   "id",
2022-04-18 15:56:15 [42mnormalization[0m >   "url",
2022-04-18 15:56:15 [42mnormalization[0m >   "via",
2022-04-18 15:56:15 [42mnormalization[0m >   "tags",
2022-04-18 15:56:15 [42mnormalization[0m >   "type",
2022-04-18 15:56:15 [42mnormalization[0m >   "due_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "status",
2022-04-18 15:56:15 [42mnormalization[0m >   "subject",
2022-04-18 15:56:15 [42mnormalization[0m >   "brand_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "group_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "priority",
2022-04-18 15:56:15 [42mnormalization[0m >   "is_public",
2022-04-18 15:56:15 [42mnormalization[0m >   "recipient",
2022-04-18 15:56:15 [42mnormalization[0m >   "created_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "problem_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "assignee_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "description",
2022-04-18 15:56:15 [42mnormalization[0m >   "external_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "raw_subject",
2022-04-18 15:56:15 [42mnormalization[0m >   "email_cc_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "follower_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "followup_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "requester_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "submitter_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "custom_fields",
2022-04-18 15:56:15 [42mnormalization[0m >   "has_incidents",
2022-04-18 15:56:15 [42mnormalization[0m >   "forum_topic_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "ticket_form_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "organization_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "collaborator_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "allow_attachments",
2022-04-18 15:56:15 [42mnormalization[0m >   "allow_channelback",
2022-04-18 15:56:15 [42mnormalization[0m >   "generated_timestamp",
2022-04-18 15:56:15 [42mnormalization[0m >   "satisfaction_rating",
2022-04-18 15:56:15 [42mnormalization[0m >   "sharing_agreement_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_ab_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_emitted_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_normalized_at" from new_data
2022-04-18 15:56:15 [42mnormalization[0m >     union all
2022-04-18 15:56:15 [42mnormalization[0m >     select "_airbyte_tickets_hashid",
2022-04-18 15:56:15 [42mnormalization[0m >   "id",
2022-04-18 15:56:15 [42mnormalization[0m >   "url",
2022-04-18 15:56:15 [42mnormalization[0m >   "via",
2022-04-18 15:56:15 [42mnormalization[0m >   "tags",
2022-04-18 15:56:15 [42mnormalization[0m >   "type",
2022-04-18 15:56:15 [42mnormalization[0m >   "due_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "status",
2022-04-18 15:56:15 [42mnormalization[0m >   "subject",
2022-04-18 15:56:15 [42mnormalization[0m >   "brand_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "group_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "priority",
2022-04-18 15:56:15 [42mnormalization[0m >   "is_public",
2022-04-18 15:56:15 [42mnormalization[0m >   "recipient",
2022-04-18 15:56:15 [42mnormalization[0m >   "created_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "problem_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "updated_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "assignee_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "description",
2022-04-18 15:56:15 [42mnormalization[0m >   "external_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "raw_subject",
2022-04-18 15:56:15 [42mnormalization[0m >   "email_cc_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "follower_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "followup_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "requester_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "submitter_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "custom_fields",
2022-04-18 15:56:15 [42mnormalization[0m >   "has_incidents",
2022-04-18 15:56:15 [42mnormalization[0m >   "forum_topic_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "ticket_form_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "organization_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "collaborator_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "allow_attachments",
2022-04-18 15:56:15 [42mnormalization[0m >   "allow_channelback",
2022-04-18 15:56:15 [42mnormalization[0m >   "generated_timestamp",
2022-04-18 15:56:15 [42mnormalization[0m >   "satisfaction_rating",
2022-04-18 15:56:15 [42mnormalization[0m >   "sharing_agreement_ids",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_ab_id",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_emitted_at",
2022-04-18 15:56:15 [42mnormalization[0m >   "_airbyte_normalized_at" from previous_active_scd_data
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > scd_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     -- SQL model to build a Type 2 Slowly Changing Dimension (SCD) table for each record identified by their primary key
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >       id,
2022-04-18 15:56:15 [42mnormalization[0m >       url,
2022-04-18 15:56:15 [42mnormalization[0m >       via,
2022-04-18 15:56:15 [42mnormalization[0m >       tags,
2022-04-18 15:56:15 [42mnormalization[0m >       type,
2022-04-18 15:56:15 [42mnormalization[0m >       due_at,
2022-04-18 15:56:15 [42mnormalization[0m >       status,
2022-04-18 15:56:15 [42mnormalization[0m >       subject,
2022-04-18 15:56:15 [42mnormalization[0m >       brand_id,
2022-04-18 15:56:15 [42mnormalization[0m >       group_id,
2022-04-18 15:56:15 [42mnormalization[0m >       priority,
2022-04-18 15:56:15 [42mnormalization[0m >       is_public,
2022-04-18 15:56:15 [42mnormalization[0m >       recipient,
2022-04-18 15:56:15 [42mnormalization[0m >       created_at,
2022-04-18 15:56:15 [42mnormalization[0m >       problem_id,
2022-04-18 15:56:15 [42mnormalization[0m >       updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >       assignee_id,
2022-04-18 15:56:15 [42mnormalization[0m >       description,
2022-04-18 15:56:15 [42mnormalization[0m >       external_id,
2022-04-18 15:56:15 [42mnormalization[0m >       raw_subject,
2022-04-18 15:56:15 [42mnormalization[0m >       email_cc_ids,
2022-04-18 15:56:15 [42mnormalization[0m >       follower_ids,
2022-04-18 15:56:15 [42mnormalization[0m >       followup_ids,
2022-04-18 15:56:15 [42mnormalization[0m >       requester_id,
2022-04-18 15:56:15 [42mnormalization[0m >       submitter_id,
2022-04-18 15:56:15 [42mnormalization[0m >       custom_fields,
2022-04-18 15:56:15 [42mnormalization[0m >       has_incidents,
2022-04-18 15:56:15 [42mnormalization[0m >       forum_topic_id,
2022-04-18 15:56:15 [42mnormalization[0m >       ticket_form_id,
2022-04-18 15:56:15 [42mnormalization[0m >       organization_id,
2022-04-18 15:56:15 [42mnormalization[0m >       collaborator_ids,
2022-04-18 15:56:15 [42mnormalization[0m >       allow_attachments,
2022-04-18 15:56:15 [42mnormalization[0m >       allow_channelback,
2022-04-18 15:56:15 [42mnormalization[0m >       generated_timestamp,
2022-04-18 15:56:15 [42mnormalization[0m >       satisfaction_rating,
2022-04-18 15:56:15 [42mnormalization[0m >       sharing_agreement_ids,
2022-04-18 15:56:15 [42mnormalization[0m >       updated_at as _airbyte_start_at,
2022-04-18 15:56:15 [42mnormalization[0m >       lag(updated_at) over (
2022-04-18 15:56:15 [42mnormalization[0m >         partition by id
2022-04-18 15:56:15 [42mnormalization[0m >         order by
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at is null asc,
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at desc,
2022-04-18 15:56:15 [42mnormalization[0m >             _airbyte_emitted_at desc
2022-04-18 15:56:15 [42mnormalization[0m >       ) as _airbyte_end_at,
2022-04-18 15:56:15 [42mnormalization[0m >       case when row_number() over (
2022-04-18 15:56:15 [42mnormalization[0m >         partition by id
2022-04-18 15:56:15 [42mnormalization[0m >         order by
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at is null asc,
2022-04-18 15:56:15 [42mnormalization[0m >             updated_at desc,
2022-04-18 15:56:15 [42mnormalization[0m >             _airbyte_emitted_at desc
2022-04-18 15:56:15 [42mnormalization[0m >       ) = 1 then 1 else 0 end as _airbyte_active_row,
2022-04-18 15:56:15 [42mnormalization[0m >       _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >       _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >       _airbyte_tickets_hashid
2022-04-18 15:56:15 [42mnormalization[0m >     from input_data
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > dedup_data as (
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         -- we need to ensure de-duplicated rows for merge/update queries
2022-04-18 15:56:15 [42mnormalization[0m >         -- additionally, we generate a unique key for the scd table
2022-04-18 15:56:15 [42mnormalization[0m >         row_number() over (
2022-04-18 15:56:15 [42mnormalization[0m >             partition by
2022-04-18 15:56:15 [42mnormalization[0m >                 _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >                 _airbyte_start_at,
2022-04-18 15:56:15 [42mnormalization[0m >                 _airbyte_emitted_at
2022-04-18 15:56:15 [42mnormalization[0m >             order by _airbyte_active_row desc, _airbyte_ab_id
2022-04-18 15:56:15 [42mnormalization[0m >         ) as _airbyte_row_num,
2022-04-18 15:56:15 [42mnormalization[0m >         md5(cast(coalesce(cast(_airbyte_unique_key as varchar), '') || '-' || coalesce(cast(_airbyte_start_at as varchar), '') || '-' || coalesce(cast(_airbyte_emitted_at as varchar), '') as varchar)) as _airbyte_unique_key_scd,
2022-04-18 15:56:15 [42mnormalization[0m >         scd_data.*
2022-04-18 15:56:15 [42mnormalization[0m >     from scd_data
2022-04-18 15:56:15 [42mnormalization[0m > )
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_unique_key_scd,
2022-04-18 15:56:15 [42mnormalization[0m >     id,
2022-04-18 15:56:15 [42mnormalization[0m >     url,
2022-04-18 15:56:15 [42mnormalization[0m >     via,
2022-04-18 15:56:15 [42mnormalization[0m >     tags,
2022-04-18 15:56:15 [42mnormalization[0m >     type,
2022-04-18 15:56:15 [42mnormalization[0m >     due_at,
2022-04-18 15:56:15 [42mnormalization[0m >     status,
2022-04-18 15:56:15 [42mnormalization[0m >     subject,
2022-04-18 15:56:15 [42mnormalization[0m >     brand_id,
2022-04-18 15:56:15 [42mnormalization[0m >     group_id,
2022-04-18 15:56:15 [42mnormalization[0m >     priority,
2022-04-18 15:56:15 [42mnormalization[0m >     is_public,
2022-04-18 15:56:15 [42mnormalization[0m >     recipient,
2022-04-18 15:56:15 [42mnormalization[0m >     created_at,
2022-04-18 15:56:15 [42mnormalization[0m >     problem_id,
2022-04-18 15:56:15 [42mnormalization[0m >     updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     assignee_id,
2022-04-18 15:56:15 [42mnormalization[0m >     description,
2022-04-18 15:56:15 [42mnormalization[0m >     external_id,
2022-04-18 15:56:15 [42mnormalization[0m >     raw_subject,
2022-04-18 15:56:15 [42mnormalization[0m >     email_cc_ids,
2022-04-18 15:56:15 [42mnormalization[0m >     follower_ids,
2022-04-18 15:56:15 [42mnormalization[0m >     followup_ids,
2022-04-18 15:56:15 [42mnormalization[0m >     requester_id,
2022-04-18 15:56:15 [42mnormalization[0m >     submitter_id,
2022-04-18 15:56:15 [42mnormalization[0m >     custom_fields,
2022-04-18 15:56:15 [42mnormalization[0m >     has_incidents,
2022-04-18 15:56:15 [42mnormalization[0m >     forum_topic_id,
2022-04-18 15:56:15 [42mnormalization[0m >     ticket_form_id,
2022-04-18 15:56:15 [42mnormalization[0m >     organization_id,
2022-04-18 15:56:15 [42mnormalization[0m >     collaborator_ids,
2022-04-18 15:56:15 [42mnormalization[0m >     allow_attachments,
2022-04-18 15:56:15 [42mnormalization[0m >     allow_channelback,
2022-04-18 15:56:15 [42mnormalization[0m >     generated_timestamp,
2022-04-18 15:56:15 [42mnormalization[0m >     satisfaction_rating,
2022-04-18 15:56:15 [42mnormalization[0m >     sharing_agreement_ids,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_start_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_end_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_active_row,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_tickets_hashid
2022-04-18 15:56:15 [42mnormalization[0m > from dedup_data where _airbyte_row_num = 1
2022-04-18 15:56:15 [42mnormalization[0m >   );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.457224 (Thread-4): SQL status: SELECT in 0.08 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.477270 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_policy_metrics_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.477734 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.477956 (Thread-4): On model.airbyte_utils.sla_policies_policy_metrics_ab1: ROLLBACK
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.481559 (Thread-4): On model.airbyte_utils.sla_policies_policy_metrics_ab1: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.482258 (Thread-4): Finished running node model.airbyte_utils.sla_policies_policy_metrics_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.482490 (Thread-4): Began running node model.airbyte_utils.ticket_metric_events
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.483046 (Thread-4): 15:55:37 | 12 of 33 START incremental model zendesk_intercom.ticket_metric_events....................................... [RUN]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.483638 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.483819 (Thread-4): Compiling model.airbyte_utils.ticket_metric_events
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.496803 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.496997 (Thread-4): On model.airbyte_utils.ticket_metric_events: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.497154 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.497286 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.565771 (Thread-4): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.566092 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:37.566226 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:38.288510 (Thread-4): SQL status: SELECT in 0.72 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:38.291451 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:38.291826 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:38.300109 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:38.300338 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:38.821457 (Thread-4): SQL status: SELECT in 0.52 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:38.825950 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:38.826117 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:15 [42mnormalization[0m >     "ticket_metric_events__dbt_tmp155538296866"
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >       compound sortkey(_airbyte_unique_key,_airbyte_emitted_at)
2022-04-18 15:56:15 [42mnormalization[0m >   as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > -- Final base SQL model
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_metric_events_scd"
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >     id,
2022-04-18 15:56:15 [42mnormalization[0m >     "time",
2022-04-18 15:56:15 [42mnormalization[0m >     type,
2022-04-18 15:56:15 [42mnormalization[0m >     metric,
2022-04-18 15:56:15 [42mnormalization[0m >     ticket_id,
2022-04-18 15:56:15 [42mnormalization[0m >     instance_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ticket_metric_events_hashid
2022-04-18 15:56:15 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_metric_events_scd"
2022-04-18 15:56:15 [42mnormalization[0m > -- ticket_metric_events from "datalake".zendesk_intercom._airbyte_raw_ticket_metric_events
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > and _airbyte_active_row = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:15 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metric_events"),
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     true)
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:39.453063 (Thread-4): SQL status: SELECT in 0.63 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:39.457728 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:39.457891 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events__dbt_tmp155538296866'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events__dbt_tmp155538296866'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events__dbt_tmp155538296866'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:39.754037 (Thread-4): SQL status: SELECT in 0.30 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:39.759223 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:39.759551 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:40.058924 (Thread-4): SQL status: SELECT in 0.30 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:40.064327 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:40.064541 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events__dbt_tmp155538296866'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events__dbt_tmp155538296866'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events__dbt_tmp155538296866'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:40.541772 (Thread-4): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:40.546870 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:40.547032 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:40.701737 (Thread-3): SQL status: SELECT in 3.28 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:40.708899 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:40.709113 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'tickets_scd__dbt_tmp155536899642'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'tickets_scd__dbt_tmp155536899642'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'tickets_scd__dbt_tmp155536899642'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.058810 (Thread-4): SQL status: SELECT in 0.51 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.062385 (Thread-4): 
2022-04-18 15:56:15 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metric_events":
2022-04-18 15:56:15 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:15 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:15 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:15 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.065680 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.065842 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.151929 (Thread-3): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.158138 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.158354 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.484040 (Thread-3): SQL status: SELECT in 0.33 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.490662 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.490833 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'tickets_scd__dbt_tmp155536899642'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'tickets_scd__dbt_tmp155536899642'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'tickets_scd__dbt_tmp155536899642'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.501823 (Thread-4): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.504025 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_metric_events"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.504516 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.504700 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       delete
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_metric_events"
2022-04-18 15:56:15 [42mnormalization[0m >     where (_airbyte_unique_key) in (
2022-04-18 15:56:15 [42mnormalization[0m >         select (_airbyte_unique_key)
2022-04-18 15:56:15 [42mnormalization[0m >         from "ticket_metric_events__dbt_tmp155538296866"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metric_events" ("id", "time", "type", "metric", "ticket_id", "instance_id", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metric_events_hashid", "_airbyte_unique_key")
2022-04-18 15:56:15 [42mnormalization[0m >     (
2022-04-18 15:56:15 [42mnormalization[0m >        select "id", "time", "type", "metric", "ticket_id", "instance_id", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metric_events_hashid", "_airbyte_unique_key"
2022-04-18 15:56:15 [42mnormalization[0m >        from "ticket_metric_events__dbt_tmp155538296866"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.779491 (Thread-4): SQL status: INSERT 0 3486 in 0.27 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.784725 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.784914 (Thread-4): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metric_events'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.830730 (Thread-3): SQL status: SELECT in 0.34 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.836737 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:41.836923 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'tickets_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.292554 (Thread-4): SQL status: SELECT in 0.51 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.295079 (Thread-4): On model.airbyte_utils.ticket_metric_events: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.295245 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.295376 (Thread-4): On model.airbyte_utils.ticket_metric_events: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.365265 (Thread-3): SQL status: SELECT in 0.53 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.378816 (Thread-3): 
2022-04-18 15:56:15 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."tickets_scd":
2022-04-18 15:56:15 [42mnormalization[0m >         Schema changed: True
2022-04-18 15:56:15 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:15 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:15 [42mnormalization[0m >         New column types: [{'column_name': '_airbyte_start_at', 'new_type': 'timestamp with time zone'}, {'column_name': '_airbyte_end_at', 'new_type': 'timestamp with time zone'}]
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.395545 (Thread-3): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.395733 (Thread-3): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     alter table "datalake"."zendesk_intercom"."tickets_scd" add column "_airbyte_start_at__dbt_alter" timestamp with time zone;
2022-04-18 15:56:15 [42mnormalization[0m >     update "datalake"."zendesk_intercom"."tickets_scd" set "_airbyte_start_at__dbt_alter" = "_airbyte_start_at";
2022-04-18 15:56:15 [42mnormalization[0m >     alter table "datalake"."zendesk_intercom"."tickets_scd" drop column "_airbyte_start_at" cascade;
2022-04-18 15:56:15 [42mnormalization[0m >     alter table "datalake"."zendesk_intercom"."tickets_scd" rename column "_airbyte_start_at__dbt_alter" to "_airbyte_start_at"
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.433368 (Thread-4): SQL status: COMMIT in 0.14 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.434264 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.434482 (Thread-4): On model.airbyte_utils.ticket_metric_events: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.435302 (Thread-4): 15:55:42 | 12 of 33 OK created incremental model zendesk_intercom.ticket_metric_events.................................. [INSERT 0 3486 in 4.95s]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.436156 (Thread-4): Finished running node model.airbyte_utils.ticket_metric_events
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.436433 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.436978 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.437157 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.451213 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.451579 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.451985 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.452200 (Thread-4): Began running node model.airbyte_utils.sla_policies_policy_metrics_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.452824 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.452993 (Thread-4): Compiling model.airbyte_utils.sla_policies_policy_metrics_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.467465 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_policy_metrics_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.467772 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.468211 (Thread-4): Finished running node model.airbyte_utils.sla_policies_policy_metrics_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.468411 (Thread-3): Postgres error: column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-18 15:56:15 [42mnormalization[0m > HINT:  You will need to rewrite or cast the expression.
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.468806 (Thread-3): On model.airbyte_utils.tickets_scd: ROLLBACK
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.468614 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.469383 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.469548 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.478088 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.478290 (Thread-3): On model.airbyte_utils.tickets_scd: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.478809 (Thread-3): Database Error in model tickets_scd (models/generated/airbyte_incremental/scd/zendesk_intercom/tickets_scd.sql)
2022-04-18 15:56:15 [42mnormalization[0m >   column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-18 15:56:15 [42mnormalization[0m >   HINT:  You will need to rewrite or cast the expression.
2022-04-18 15:56:15 [42mnormalization[0m > Traceback (most recent call last):
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/postgres/connections.py", line 56, in exception_handler
2022-04-18 15:56:15 [42mnormalization[0m >     yield
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/sql/connections.py", line 80, in add_query
2022-04-18 15:56:15 [42mnormalization[0m >     cursor.execute(sql, bindings)
2022-04-18 15:56:15 [42mnormalization[0m > psycopg2.errors.DatatypeMismatch: column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-18 15:56:15 [42mnormalization[0m > HINT:  You will need to rewrite or cast the expression.
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > The above exception was the direct cause of the following exception:
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > Traceback (most recent call last):
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/task/base.py", line 348, in safe_run
2022-04-18 15:56:15 [42mnormalization[0m >     result = self.compile_and_execute(manifest, ctx)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/task/base.py", line 291, in compile_and_execute
2022-04-18 15:56:15 [42mnormalization[0m >     result = self.run(ctx.node, manifest)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/task/base.py", line 393, in run
2022-04-18 15:56:15 [42mnormalization[0m >     return self.execute(compiled_node, manifest)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/task/run.py", line 249, in execute
2022-04-18 15:56:15 [42mnormalization[0m >     result = MacroGenerator(materialization_macro, context)()
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self.call_macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-18 15:56:15 [42mnormalization[0m >     return macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self._invoke(arguments, autoescape)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-18 15:56:15 [42mnormalization[0m >     rv = self._func(*arguments)
2022-04-18 15:56:15 [42mnormalization[0m >   File "<template>", line 148, in macro
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __context.call(__obj, *args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __obj(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self.call_macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-18 15:56:15 [42mnormalization[0m >     return macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self._invoke(arguments, autoescape)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-18 15:56:15 [42mnormalization[0m >     rv = self._func(*arguments)
2022-04-18 15:56:15 [42mnormalization[0m >   File "<template>", line 66, in macro
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __context.call(__obj, *args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __obj(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self.call_macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-18 15:56:15 [42mnormalization[0m >     return macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self._invoke(arguments, autoescape)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-18 15:56:15 [42mnormalization[0m >     rv = self._func(*arguments)
2022-04-18 15:56:15 [42mnormalization[0m >   File "<template>", line 63, in macro
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __context.call(__obj, *args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __obj(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self.call_macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-18 15:56:15 [42mnormalization[0m >     return macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self._invoke(arguments, autoescape)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-18 15:56:15 [42mnormalization[0m >     rv = self._func(*arguments)
2022-04-18 15:56:15 [42mnormalization[0m >   File "<template>", line 24, in macro
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __context.call(__obj, *args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __obj(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self.call_macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-18 15:56:15 [42mnormalization[0m >     return macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self._invoke(arguments, autoescape)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-18 15:56:15 [42mnormalization[0m >     rv = self._func(*arguments)
2022-04-18 15:56:15 [42mnormalization[0m >   File "<template>", line 55, in macro
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __context.call(__obj, *args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __obj(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self.call_macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-18 15:56:15 [42mnormalization[0m >     return macro(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-18 15:56:15 [42mnormalization[0m >     return self._invoke(arguments, autoescape)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-18 15:56:15 [42mnormalization[0m >     rv = self._func(*arguments)
2022-04-18 15:56:15 [42mnormalization[0m >   File "<template>", line 41, in macro
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __context.call(__obj, *args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-18 15:56:15 [42mnormalization[0m >     return __obj(*args, **kwargs)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/base/impl.py", line 226, in execute
2022-04-18 15:56:15 [42mnormalization[0m >     return self.connections.execute(
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/sql/connections.py", line 131, in execute
2022-04-18 15:56:15 [42mnormalization[0m >     _, cursor = self.add_query(sql, auto_begin)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/sql/connections.py", line 87, in add_query
2022-04-18 15:56:15 [42mnormalization[0m >     return connection, cursor
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/contextlib.py", line 131, in __exit__
2022-04-18 15:56:15 [42mnormalization[0m >     self.gen.throw(type, value, traceback)
2022-04-18 15:56:15 [42mnormalization[0m >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/postgres/connections.py", line 67, in exception_handler
2022-04-18 15:56:15 [42mnormalization[0m >     raise dbt.exceptions.DatabaseException(str(e).strip()) from e
2022-04-18 15:56:15 [42mnormalization[0m > dbt.exceptions.DatabaseException: Database Error in model tickets_scd (models/generated/airbyte_incremental/scd/zendesk_intercom/tickets_scd.sql)
2022-04-18 15:56:15 [42mnormalization[0m >   column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-18 15:56:15 [42mnormalization[0m >   HINT:  You will need to rewrite or cast the expression.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.481100 (Thread-3): 15:55:42 | 11 of 33 ERROR creating incremental model zendesk_intercom.tickets_scd....................................... [ERROR in 8.72s]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.481922 (Thread-3): Finished running node model.airbyte_utils.tickets_scd
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.482143 (Thread-3): Began running node model.airbyte_utils.sla_policies_policy_metrics_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.482953 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.483124 (Thread-3): Compiling model.airbyte_utils.sla_policies_policy_metrics_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.502737 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.503188 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.506811 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.507310 (Thread-4): Began running node model.airbyte_utils.tickets
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.512506 (Thread-4): 15:55:42 | 13 of 33 SKIP relation zendesk_intercom.tickets.............................................................. [SKIP]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.513350 (Thread-4): Finished running node model.airbyte_utils.tickets
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.513564 (Thread-4): Began running node model.airbyte_utils.tickets_custom_fields_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.513853 (Thread-4): Finished running node model.airbyte_utils.tickets_custom_fields_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.514037 (Thread-4): Began running node model.airbyte_utils.tickets_satisfaction_rating_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.514401 (Thread-4): Finished running node model.airbyte_utils.tickets_satisfaction_rating_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.514589 (Thread-4): Began running node model.airbyte_utils.tickets_via_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.514922 (Thread-4): Finished running node model.airbyte_utils.tickets_via_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.515107 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.515570 (Thread-4): 15:55:42 | 14 of 33 START incremental model zendesk_intercom.sla_policies_filter........................................ [RUN]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.516502 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.516685 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.512293 (Thread-3): Writing injected SQL for node "model.airbyte_utils.sla_policies_policy_metrics_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.523681 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.524140 (Thread-3): Finished running node model.airbyte_utils.sla_policies_policy_metrics_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.524375 (Thread-3): Began running node model.airbyte_utils.tickets_custom_fields_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.524558 (Thread-3): Finished running node model.airbyte_utils.tickets_custom_fields_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.524717 (Thread-3): Began running node model.airbyte_utils.tickets_satisfaction_rating_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.524878 (Thread-3): Finished running node model.airbyte_utils.tickets_satisfaction_rating_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.525030 (Thread-3): Began running node model.airbyte_utils.tickets_via_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.525188 (Thread-3): Finished running node model.airbyte_utils.tickets_via_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.525802 (Thread-3): Began running node model.airbyte_utils.tickets_custom_fields_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.525987 (Thread-3): Finished running node model.airbyte_utils.tickets_custom_fields_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.526164 (Thread-3): Began running node model.airbyte_utils.tickets_satisfaction_rating_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.526488 (Thread-3): Finished running node model.airbyte_utils.tickets_satisfaction_rating_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.526673 (Thread-3): Began running node model.airbyte_utils.tickets_via_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.527003 (Thread-3): Finished running node model.airbyte_utils.tickets_via_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.527185 (Thread-3): Began running node model.airbyte_utils.sla_policies_policy_metrics
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.527678 (Thread-3): 15:55:42 | 15 of 33 START incremental model zendesk_intercom.sla_policies_policy_metrics................................ [RUN]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.528827 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.529014 (Thread-3): Compiling model.airbyte_utils.sla_policies_policy_metrics
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.542586 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.542807 (Thread-4): On model.airbyte_utils.sla_policies_filter: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.542953 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.543072 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.547237 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.547407 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.547548 (Thread-3): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.547669 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.594352 (Thread-4): SQL status: BEGIN in 0.05 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.594658 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.594793 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.600806 (Thread-3): SQL status: BEGIN in 0.05 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.600977 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:42.601103 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.158578 (Thread-3): SQL status: SELECT in 0.56 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.169847 (Thread-3): Writing injected SQL for node "model.airbyte_utils.sla_policies_policy_metrics"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.170249 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.177215 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.177381 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.184346 (Thread-4): SQL status: SELECT in 0.59 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.194306 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.194640 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.202951 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.203118 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.623399 (Thread-3): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.627950 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.628114 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:15 [42mnormalization[0m >     "sla_policies_policy_metrics__dbt_tmp155543174209"
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:15 [42mnormalization[0m >   as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > with __dbt__cte__sla_policies_policy_metrics_ab1 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."sla_policies"
2022-04-18 15:56:15 [42mnormalization[0m > with numbers as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     with p as (
2022-04-18 15:56:15 [42mnormalization[0m >         select 0 as generated_number union all select 1
2022-04-18 15:56:15 [42mnormalization[0m >     ), unioned as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p0.generated_number * power(2, 0)
2022-04-18 15:56:15 [42mnormalization[0m >      + 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p1.generated_number * power(2, 1)
2022-04-18 15:56:15 [42mnormalization[0m >      + 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p2.generated_number * power(2, 2)
2022-04-18 15:56:15 [42mnormalization[0m >      + 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p3.generated_number * power(2, 3)
2022-04-18 15:56:15 [42mnormalization[0m >      + 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p4.generated_number * power(2, 4)
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     + 1
2022-04-18 15:56:15 [42mnormalization[0m >     as generated_number
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p as p0
2022-04-18 15:56:15 [42mnormalization[0m >      cross join 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p as p1
2022-04-18 15:56:15 [42mnormalization[0m >      cross join 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p as p2
2022-04-18 15:56:15 [42mnormalization[0m >      cross join 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p as p3
2022-04-18 15:56:15 [42mnormalization[0m >      cross join 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p as p4
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select *
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     where generated_number <= 24
2022-04-18 15:56:15 [42mnormalization[0m >     order by generated_number
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > joined as (
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         _airbyte_sla_policies_hashid as _airbyte_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >         json_extract_array_element_text(policy_metrics, numbers.generated_number::int - 1, true) as _airbyte_nested_data
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."sla_policies"
2022-04-18 15:56:15 [42mnormalization[0m >     cross join numbers
2022-04-18 15:56:15 [42mnormalization[0m >     -- only generate the number of records in the cross join that corresponds
2022-04-18 15:56:15 [42mnormalization[0m >     -- to the number of items in "datalake".zendesk_intercom."sla_policies".policy_metrics
2022-04-18 15:56:15 [42mnormalization[0m >     where numbers.generated_number <= json_array_length(policy_metrics, true)
2022-04-18 15:56:15 [42mnormalization[0m > )
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_sla_policies_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'metric', true) != '' then json_extract_path_text(_airbyte_nested_data, 'metric', true) end as metric,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'target', true) != '' then json_extract_path_text(_airbyte_nested_data, 'target', true) end as target,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'priority', true) != '' then json_extract_path_text(_airbyte_nested_data, 'priority', true) end as priority,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'business_hours', true) != '' then json_extract_path_text(_airbyte_nested_data, 'business_hours', true) end as business_hours,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:15 [42mnormalization[0m > from "datalake".zendesk_intercom."sla_policies" as table_alias
2022-04-18 15:56:15 [42mnormalization[0m > -- policy_metrics at sla_policies/policy_metrics
2022-04-18 15:56:15 [42mnormalization[0m > left join joined on _airbyte_sla_policies_hashid = joined._airbyte_hashid
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > and policy_metrics is not null
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),  __dbt__cte__sla_policies_policy_metrics_ab2 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_policy_metrics_ab1
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_sla_policies_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(metric as varchar) as metric,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(target as 
2022-04-18 15:56:15 [42mnormalization[0m >     bigint
2022-04-18 15:56:15 [42mnormalization[0m > ) as target,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(priority as varchar) as priority,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(decode(business_hours, 'true', '1', 'false', '0')::integer as boolean) as business_hours,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_policy_metrics_ab1
2022-04-18 15:56:15 [42mnormalization[0m > -- policy_metrics at sla_policies/policy_metrics
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),  __dbt__cte__sla_policies_policy_metrics_ab3 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_policy_metrics_ab2
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_sla_policies_hashid as varchar), '') || '-' || coalesce(cast(metric as varchar), '') || '-' || coalesce(cast(target as varchar), '') || '-' || coalesce(cast(priority as varchar), '') || '-' || coalesce(cast(case when business_hours then 'true' else 'false' end as varchar), '') as varchar)) as _airbyte_policy_metrics_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_policy_metrics_ab2 tmp
2022-04-18 15:56:15 [42mnormalization[0m > -- policy_metrics at sla_policies/policy_metrics
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_policy_metrics_ab3
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_sla_policies_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     metric,
2022-04-18 15:56:15 [42mnormalization[0m >     target,
2022-04-18 15:56:15 [42mnormalization[0m >     priority,
2022-04-18 15:56:15 [42mnormalization[0m >     business_hours,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_policy_metrics_hashid
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_policy_metrics_ab3
2022-04-18 15:56:15 [42mnormalization[0m > -- policy_metrics at sla_policies/policy_metrics from "datalake".zendesk_intercom."sla_policies"
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:15 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > )) from "datalake".zendesk_intercom."sla_policies_policy_metrics"),
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     true)
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.654936 (Thread-4): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.659211 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.659392 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:15 [42mnormalization[0m >     "sla_policies_filter__dbt_tmp155543198154"
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:15 [42mnormalization[0m >   as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > with __dbt__cte__sla_policies_filter_ab1 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."sla_policies"
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_sla_policies_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     json_extract_path_text(filter, 'all', true) as "all",
2022-04-18 15:56:15 [42mnormalization[0m >     json_extract_path_text(filter, 'any', true) as "any",
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:15 [42mnormalization[0m > from "datalake".zendesk_intercom."sla_policies" as table_alias
2022-04-18 15:56:15 [42mnormalization[0m > -- filter at sla_policies/filter
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > and filter is not null
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),  __dbt__cte__sla_policies_filter_ab2 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_filter_ab1
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_sla_policies_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     "all",
2022-04-18 15:56:15 [42mnormalization[0m >     "any",
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_filter_ab1
2022-04-18 15:56:15 [42mnormalization[0m > -- filter at sla_policies/filter
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),  __dbt__cte__sla_policies_filter_ab3 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_filter_ab2
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_sla_policies_hashid as varchar), '') || '-' || coalesce(cast("all" as varchar), '') || '-' || coalesce(cast("any" as varchar), '') as varchar)) as _airbyte_filter_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_filter_ab2 tmp
2022-04-18 15:56:15 [42mnormalization[0m > -- filter at sla_policies/filter
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_filter_ab3
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_sla_policies_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     "all",
2022-04-18 15:56:15 [42mnormalization[0m >     "any",
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_filter_hashid
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_filter_ab3
2022-04-18 15:56:15 [42mnormalization[0m > -- filter at sla_policies/filter from "datalake".zendesk_intercom."sla_policies"
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:15 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > )) from "datalake".zendesk_intercom."sla_policies_filter"),
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     true)
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.830878 (Thread-4): SQL status: SELECT in 0.17 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.835737 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:43.835916 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter__dbt_tmp155543198154'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter__dbt_tmp155543198154'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter__dbt_tmp155543198154'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.298018 (Thread-4): SQL status: SELECT in 0.46 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.303340 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.303517 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.493051 (Thread-3): SQL status: SELECT in 0.86 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.497570 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.497738 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_policy_metrics__dbt_tmp155543174209'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_policy_metrics__dbt_tmp155543174209'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_policy_metrics__dbt_tmp155543174209'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.790476 (Thread-4): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.795606 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.795833 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter__dbt_tmp155543198154'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter__dbt_tmp155543198154'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter__dbt_tmp155543198154'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.947657 (Thread-3): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.955055 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:44.955249 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.285511 (Thread-4): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.290561 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.290728 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.482577 (Thread-3): SQL status: SELECT in 0.53 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.489972 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.490151 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_policy_metrics__dbt_tmp155543174209'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_policy_metrics__dbt_tmp155543174209'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_policy_metrics__dbt_tmp155543174209'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.791731 (Thread-4): SQL status: SELECT in 0.50 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.794819 (Thread-4): 
2022-04-18 15:56:15 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."sla_policies_filter":
2022-04-18 15:56:15 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:15 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:15 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:15 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.798074 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:45.798232 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.021218 (Thread-3): SQL status: SELECT in 0.53 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.026930 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.027103 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.370760 (Thread-4): SQL status: SELECT in 0.57 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.373237 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.373656 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.373800 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."sla_policies_filter" ("_airbyte_sla_policies_hashid", "all", "any", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_filter_hashid")
2022-04-18 15:56:15 [42mnormalization[0m >     (
2022-04-18 15:56:15 [42mnormalization[0m >        select "_airbyte_sla_policies_hashid", "all", "any", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_filter_hashid"
2022-04-18 15:56:15 [42mnormalization[0m >        from "sla_policies_filter__dbt_tmp155543198154"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.450656 (Thread-4): SQL status: INSERT 0 12 in 0.08 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.455726 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.455896 (Thread-4): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.584152 (Thread-3): SQL status: SELECT in 0.56 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.587658 (Thread-3): 
2022-04-18 15:56:15 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."sla_policies_policy_metrics":
2022-04-18 15:56:15 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:15 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:15 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:15 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.590734 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.590888 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.748569 (Thread-1): SQL status: SELECT in 9.34 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.753228 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:46.753390 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd__dbt_tmp155536886011'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd__dbt_tmp155536886011'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd__dbt_tmp155536886011'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.055188 (Thread-4): SQL status: SELECT in 0.60 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.057649 (Thread-4): On model.airbyte_utils.sla_policies_filter: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.057817 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.057947 (Thread-4): On model.airbyte_utils.sla_policies_filter: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.168573 (Thread-3): SQL status: SELECT in 0.58 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.171116 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.sla_policies_policy_metrics"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.171519 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.171662 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."sla_policies_policy_metrics" ("_airbyte_sla_policies_hashid", "metric", "target", "priority", "business_hours", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_policy_metrics_hashid")
2022-04-18 15:56:15 [42mnormalization[0m >     (
2022-04-18 15:56:15 [42mnormalization[0m >        select "_airbyte_sla_policies_hashid", "metric", "target", "priority", "business_hours", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_policy_metrics_hashid"
2022-04-18 15:56:15 [42mnormalization[0m >        from "sla_policies_policy_metrics__dbt_tmp155543174209"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.268475 (Thread-4): SQL status: COMMIT in 0.21 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.269404 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.269621 (Thread-4): On model.airbyte_utils.sla_policies_filter: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.270442 (Thread-4): 15:55:47 | 14 of 33 OK created incremental model zendesk_intercom.sla_policies_filter................................... [INSERT 0 12 in 4.75s]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.271362 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.271628 (Thread-4): Began running node model.airbyte_utils.tickets_custom_fields
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.272140 (Thread-4): 15:55:47 | 16 of 33 SKIP relation zendesk_intercom.tickets_custom_fields................................................ [SKIP]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.272767 (Thread-4): Finished running node model.airbyte_utils.tickets_custom_fields
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.273016 (Thread-4): Began running node model.airbyte_utils.tickets_satisfaction_rating
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.273302 (Thread-4): 15:55:47 | 17 of 33 SKIP relation zendesk_intercom.tickets_satisfaction_rating.......................................... [SKIP]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.273898 (Thread-4): Finished running node model.airbyte_utils.tickets_satisfaction_rating
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.274138 (Thread-4): Began running node model.airbyte_utils.tickets_via
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.274405 (Thread-4): 15:55:47 | 18 of 33 SKIP relation zendesk_intercom.tickets_via.......................................................... [SKIP]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.275001 (Thread-4): Finished running node model.airbyte_utils.tickets_via
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.275246 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_all_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.275768 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.275948 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_all_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.290075 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.290249 (Thread-4): On model.airbyte_utils.sla_policies_filter_all_ab1: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.290393 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.290510 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.301011 (Thread-1): SQL status: SELECT in 0.55 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.306835 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.306999 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.371888 (Thread-4): SQL status: BEGIN in 0.08 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.372205 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.372373 (Thread-4): On model.airbyte_utils.sla_policies_filter_all_ab1: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all_ab1"} */
2022-04-18 15:56:15 [42mnormalization[0m > with max_value as (
2022-04-18 15:56:15 [42mnormalization[0m >             select max(json_array_length("all", true)) as max_number_of_items
2022-04-18 15:56:15 [42mnormalization[0m >             from "datalake".zendesk_intercom."sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m >         )
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >             case when max_number_of_items is not null and max_number_of_items > 1
2022-04-18 15:56:15 [42mnormalization[0m >             then max_number_of_items
2022-04-18 15:56:15 [42mnormalization[0m >             else 1 end as max_number_of_items
2022-04-18 15:56:15 [42mnormalization[0m >         from max_value
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.478845 (Thread-4): SQL status: SELECT in 0.11 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.485067 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_all_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.485441 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.485649 (Thread-4): On model.airbyte_utils.sla_policies_filter_all_ab1: ROLLBACK
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.489349 (Thread-4): On model.airbyte_utils.sla_policies_filter_all_ab1: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.489955 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_all_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.490182 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_any_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.490800 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.490971 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_any_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.501333 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_any_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.501493 (Thread-4): On model.airbyte_utils.sla_policies_filter_any_ab1: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.501633 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.501746 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.571214 (Thread-4): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.571520 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_any_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.571653 (Thread-4): On model.airbyte_utils.sla_policies_filter_any_ab1: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any_ab1"} */
2022-04-18 15:56:15 [42mnormalization[0m > with max_value as (
2022-04-18 15:56:15 [42mnormalization[0m >             select max(json_array_length("any", true)) as max_number_of_items
2022-04-18 15:56:15 [42mnormalization[0m >             from "datalake".zendesk_intercom."sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m >         )
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >             case when max_number_of_items is not null and max_number_of_items > 1
2022-04-18 15:56:15 [42mnormalization[0m >             then max_number_of_items
2022-04-18 15:56:15 [42mnormalization[0m >             else 1 end as max_number_of_items
2022-04-18 15:56:15 [42mnormalization[0m >         from max_value
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.658170 (Thread-4): SQL status: SELECT in 0.09 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.664174 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_any_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.664602 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.664811 (Thread-4): On model.airbyte_utils.sla_policies_filter_any_ab1: ROLLBACK
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.670489 (Thread-4): On model.airbyte_utils.sla_policies_filter_any_ab1: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.671168 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_any_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.671440 (Thread-4): Began running node model.airbyte_utils.tickets_via_source_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.671899 (Thread-4): Finished running node model.airbyte_utils.tickets_via_source_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.672087 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_all_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.672621 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.672807 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_all_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.686318 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_all_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.686679 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.687081 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_all_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.687283 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_any_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.687805 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.687973 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_any_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.700345 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_any_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.700656 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.701045 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_any_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.701246 (Thread-4): Began running node model.airbyte_utils.tickets_via_source_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.701601 (Thread-4): Finished running node model.airbyte_utils.tickets_via_source_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.701786 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_all_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.702278 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.702441 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_all_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.718724 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_all_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.719045 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.719445 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_all_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.719646 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_any_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.720166 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.720366 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_any_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.735129 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_any_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.735467 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.735880 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_any_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.736087 (Thread-4): Began running node model.airbyte_utils.tickets_via_source_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.736508 (Thread-4): Finished running node model.airbyte_utils.tickets_via_source_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.736702 (Thread-4): Began running node model.airbyte_utils.sla_policies_filter_all
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.737126 (Thread-4): 15:55:47 | 19 of 33 START incremental model zendesk_intercom.sla_policies_filter_all.................................... [RUN]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.738221 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.738390 (Thread-4): Compiling model.airbyte_utils.sla_policies_filter_all
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.748380 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.748574 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.748716 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.748832 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.798091 (Thread-4): SQL status: BEGIN in 0.05 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.798401 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.798540 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.924147 (Thread-1): SQL status: SELECT in 0.62 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.930362 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:47.930528 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd__dbt_tmp155536886011'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd__dbt_tmp155536886011'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd__dbt_tmp155536886011'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:48.457863 (Thread-1): SQL status: SELECT in 0.53 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:48.463611 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:48.463783 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:48.471940 (Thread-4): SQL status: SELECT in 0.67 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:48.482627 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_all"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:48.483029 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:48.556545 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:48.556805 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.023747 (Thread-1): SQL status: SELECT in 0.56 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.033555 (Thread-1): 
2022-04-18 15:56:15 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metrics_scd":
2022-04-18 15:56:15 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:15 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:15 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:15 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.036935 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.037110 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.169812 (Thread-4): SQL status: SELECT in 0.61 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.174161 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.174323 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:15 [42mnormalization[0m >     "sla_policies_filter_all__dbt_tmp155548488956"
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:15 [42mnormalization[0m >   as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > with __dbt__cte__sla_policies_filter_all_ab1 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m > with numbers as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     with p as (
2022-04-18 15:56:15 [42mnormalization[0m >         select 0 as generated_number union all select 1
2022-04-18 15:56:15 [42mnormalization[0m >     ), unioned as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p0.generated_number * power(2, 0)
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     + 1
2022-04-18 15:56:15 [42mnormalization[0m >     as generated_number
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p as p0
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select *
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     where generated_number <= 1
2022-04-18 15:56:15 [42mnormalization[0m >     order by generated_number
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > joined as (
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         _airbyte_filter_hashid as _airbyte_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >         json_extract_array_element_text("all", numbers.generated_number::int - 1, true) as _airbyte_nested_data
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m >     cross join numbers
2022-04-18 15:56:15 [42mnormalization[0m >     -- only generate the number of records in the cross join that corresponds
2022-04-18 15:56:15 [42mnormalization[0m >     -- to the number of items in "datalake".zendesk_intercom."sla_policies_filter"."all"
2022-04-18 15:56:15 [42mnormalization[0m >     where numbers.generated_number <= json_array_length("all", true)
2022-04-18 15:56:15 [42mnormalization[0m > )
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_filter_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'field', true) != '' then json_extract_path_text(_airbyte_nested_data, 'field', true) end as field,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'value', true) != '' then json_extract_path_text(_airbyte_nested_data, 'value', true) end as value,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'operator', true) != '' then json_extract_path_text(_airbyte_nested_data, 'operator', true) end as operator,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:15 [42mnormalization[0m > from "datalake".zendesk_intercom."sla_policies_filter" as table_alias
2022-04-18 15:56:15 [42mnormalization[0m > -- all at sla_policies/filter/all
2022-04-18 15:56:15 [42mnormalization[0m > left join joined on _airbyte_filter_hashid = joined._airbyte_hashid
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > and "all" is not null
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),  __dbt__cte__sla_policies_filter_all_ab2 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_filter_all_ab1
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_filter_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(field as varchar) as field,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(value as varchar) as value,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(operator as varchar) as operator,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_filter_all_ab1
2022-04-18 15:56:15 [42mnormalization[0m > -- all at sla_policies/filter/all
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),  __dbt__cte__sla_policies_filter_all_ab3 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_filter_all_ab2
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_filter_hashid as varchar), '') || '-' || coalesce(cast(field as varchar), '') || '-' || coalesce(cast(value as varchar), '') || '-' || coalesce(cast(operator as varchar), '') as varchar)) as _airbyte_all_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_filter_all_ab2 tmp
2022-04-18 15:56:15 [42mnormalization[0m > -- all at sla_policies/filter/all
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_filter_all_ab3
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_filter_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     field,
2022-04-18 15:56:15 [42mnormalization[0m >     value,
2022-04-18 15:56:15 [42mnormalization[0m >     operator,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_all_hashid
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_filter_all_ab3
2022-04-18 15:56:15 [42mnormalization[0m > -- all at sla_policies/filter/all from "datalake".zendesk_intercom."sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:15 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > )) from "datalake".zendesk_intercom."sla_policies_filter_all"),
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     true)
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.606448 (Thread-1): SQL status: SELECT in 0.57 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.610253 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_scd"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.610678 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.610822 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       delete
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:15 [42mnormalization[0m >     where (_airbyte_unique_key_scd) in (
2022-04-18 15:56:15 [42mnormalization[0m >         select (_airbyte_unique_key_scd)
2022-04-18 15:56:15 [42mnormalization[0m >         from "ticket_metrics_scd__dbt_tmp155536886011"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metrics_scd" ("_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "url", "time", "type", "metric", "status", "reopens", "replies", "solved_at", "ticket_id", "created_at", "updated_at", "assigned_at", "instance_id", "group_stations", "assignee_stations", "status_updated_at", "assignee_updated_at", "requester_updated_at", "initially_assigned_at", "reply_time_in_minutes", "latest_comment_added_at", "on_hold_time_in_minutes", "agent_wait_time_in_minutes", "requester_wait_time_in_minutes", "full_resolution_time_in_minutes", "first_resolution_time_in_minutes", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metrics_hashid")
2022-04-18 15:56:15 [42mnormalization[0m >     (
2022-04-18 15:56:15 [42mnormalization[0m >        select "_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "url", "time", "type", "metric", "status", "reopens", "replies", "solved_at", "ticket_id", "created_at", "updated_at", "assigned_at", "instance_id", "group_stations", "assignee_stations", "status_updated_at", "assignee_updated_at", "requester_updated_at", "initially_assigned_at", "reply_time_in_minutes", "latest_comment_added_at", "on_hold_time_in_minutes", "agent_wait_time_in_minutes", "requester_wait_time_in_minutes", "full_resolution_time_in_minutes", "first_resolution_time_in_minutes", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metrics_hashid"
2022-04-18 15:56:15 [42mnormalization[0m >        from "ticket_metrics_scd__dbt_tmp155536886011"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.682403 (Thread-4): SQL status: SELECT in 0.51 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.687158 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:49.687336 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_all__dbt_tmp155548488956'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_all__dbt_tmp155548488956'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_all__dbt_tmp155548488956'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.066489 (Thread-4): SQL status: SELECT in 0.38 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.071512 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.071697 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.262912 (Thread-1): SQL status: INSERT 0 29547 in 0.65 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.267967 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.268140 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.436555 (Thread-4): SQL status: SELECT in 0.36 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.442087 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.442278 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_all__dbt_tmp155548488956'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_all__dbt_tmp155548488956'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_all__dbt_tmp155548488956'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.747680 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.751098 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.751481 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.751632 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         drop view _airbyte_zendesk_intercom.ticket_metrics_stg
2022-04-18 15:56:15 [42mnormalization[0m >       
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.765012 (Thread-1): SQL status: DROP VIEW in 0.01 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.766234 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.766404 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.766633 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.882585 (Thread-1): SQL status: COMMIT in 0.12 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.883520 (Thread-1): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.883739 (Thread-1): On model.airbyte_utils.ticket_metrics_scd: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.884630 (Thread-1): 15:55:50 | 10 of 33 OK created incremental model zendesk_intercom.ticket_metrics_scd.................................... [INSERT 0 29547 in 17.20s]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.884839 (Thread-1): Finished running node model.airbyte_utils.ticket_metrics_scd
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.885292 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter_any
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.885645 (Thread-1): 15:55:50 | 20 of 33 START incremental model zendesk_intercom.sla_policies_filter_any.................................... [RUN]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.886136 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.886347 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter_any
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.899666 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.899960 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.900117 (Thread-1): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.900235 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.925822 (Thread-4): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.931092 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.931301 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.969238 (Thread-1): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.969555 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:50.969688 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.414124 (Thread-4): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.417465 (Thread-4): 
2022-04-18 15:56:15 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."sla_policies_filter_all":
2022-04-18 15:56:15 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:15 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:15 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:15 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.421003 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.421170 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.553315 (Thread-1): SQL status: SELECT in 0.58 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.563685 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_any"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.564137 (Thread-1): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.571310 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.571483 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.761317 (Thread-2): SQL status: SELECT in 15.85 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.767852 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.768032 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_fields_scd__dbt_tmp155535468764'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_fields_scd__dbt_tmp155535468764'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_fields_scd__dbt_tmp155535468764'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.830801 (Thread-4): SQL status: SELECT in 0.41 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.833531 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.sla_policies_filter_all"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.833945 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.834085 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."sla_policies_filter_all" ("_airbyte_filter_hashid", "field", "value", "operator", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_all_hashid")
2022-04-18 15:56:15 [42mnormalization[0m >     (
2022-04-18 15:56:15 [42mnormalization[0m >        select "_airbyte_filter_hashid", "field", "value", "operator", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_all_hashid"
2022-04-18 15:56:15 [42mnormalization[0m >        from "sla_policies_filter_all__dbt_tmp155548488956"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.949812 (Thread-4): SQL status: INSERT 0 29 in 0.12 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.954921 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:51.955105 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_all'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.050202 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.054819 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.054996 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:15 [42mnormalization[0m >     "sla_policies_filter_any__dbt_tmp155551568198"
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:15 [42mnormalization[0m >   as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > with __dbt__cte__sla_policies_filter_any_ab1 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m > with numbers as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     with p as (
2022-04-18 15:56:15 [42mnormalization[0m >         select 0 as generated_number union all select 1
2022-04-18 15:56:15 [42mnormalization[0m >     ), unioned as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p0.generated_number * power(2, 0)
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     + 1
2022-04-18 15:56:15 [42mnormalization[0m >     as generated_number
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     p as p0
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select *
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     where generated_number <= 1
2022-04-18 15:56:15 [42mnormalization[0m >     order by generated_number
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),
2022-04-18 15:56:15 [42mnormalization[0m > joined as (
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >         _airbyte_filter_hashid as _airbyte_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >         json_extract_array_element_text("any", numbers.generated_number::int - 1, true) as _airbyte_nested_data
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m >     cross join numbers
2022-04-18 15:56:15 [42mnormalization[0m >     -- only generate the number of records in the cross join that corresponds
2022-04-18 15:56:15 [42mnormalization[0m >     -- to the number of items in "datalake".zendesk_intercom."sla_policies_filter"."any"
2022-04-18 15:56:15 [42mnormalization[0m >     where numbers.generated_number <= json_array_length("any", true)
2022-04-18 15:56:15 [42mnormalization[0m > )
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_filter_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'field', true) != '' then json_extract_path_text(_airbyte_nested_data, 'field', true) end as field,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'value', true) != '' then json_extract_path_text(_airbyte_nested_data, 'value', true) end as value,
2022-04-18 15:56:15 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'operator', true) != '' then json_extract_path_text(_airbyte_nested_data, 'operator', true) end as operator,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:15 [42mnormalization[0m > from "datalake".zendesk_intercom."sla_policies_filter" as table_alias
2022-04-18 15:56:15 [42mnormalization[0m > -- any at sla_policies/filter/any
2022-04-18 15:56:15 [42mnormalization[0m > left join joined on _airbyte_filter_hashid = joined._airbyte_hashid
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > and "any" is not null
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),  __dbt__cte__sla_policies_filter_any_ab2 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_filter_any_ab1
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_filter_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(field as varchar) as field,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(value as varchar) as value,
2022-04-18 15:56:15 [42mnormalization[0m >     cast(operator as varchar) as operator,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_filter_any_ab1
2022-04-18 15:56:15 [42mnormalization[0m > -- any at sla_policies/filter/any
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > ),  __dbt__cte__sla_policies_filter_any_ab3 as (
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_filter_any_ab2
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_filter_hashid as varchar), '') || '-' || coalesce(cast(field as varchar), '') || '-' || coalesce(cast(value as varchar), '') || '-' || coalesce(cast(operator as varchar), '') as varchar)) as _airbyte_any_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_filter_any_ab2 tmp
2022-04-18 15:56:15 [42mnormalization[0m > -- any at sla_policies/filter/any
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: __dbt__cte__sla_policies_filter_any_ab3
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_filter_hashid,
2022-04-18 15:56:15 [42mnormalization[0m >     field,
2022-04-18 15:56:15 [42mnormalization[0m >     value,
2022-04-18 15:56:15 [42mnormalization[0m >     operator,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_any_hashid
2022-04-18 15:56:15 [42mnormalization[0m > from __dbt__cte__sla_policies_filter_any_ab3
2022-04-18 15:56:15 [42mnormalization[0m > -- any at sla_policies/filter/any from "datalake".zendesk_intercom."sla_policies_filter"
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:15 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > )) from "datalake".zendesk_intercom."sla_policies_filter_any"),
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     true)
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.222473 (Thread-2): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.228641 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.228828 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.414247 (Thread-1): SQL status: SELECT in 0.36 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.418635 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.418794 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_any__dbt_tmp155551568198'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_any__dbt_tmp155551568198'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_any__dbt_tmp155551568198'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.475567 (Thread-4): SQL status: SELECT in 0.52 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.478157 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.478342 (Thread-4): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.478473 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.556009 (Thread-4): SQL status: COMMIT in 0.08 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.557024 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.557254 (Thread-4): On model.airbyte_utils.sla_policies_filter_all: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.558169 (Thread-4): 15:55:52 | 19 of 33 OK created incremental model zendesk_intercom.sla_policies_filter_all............................... [INSERT 0 29 in 4.82s]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.558943 (Thread-4): Finished running node model.airbyte_utils.sla_policies_filter_all
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.559225 (Thread-4): Began running node model.airbyte_utils.tickets_via_source
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.559665 (Thread-4): 15:55:52 | 21 of 33 SKIP relation zendesk_intercom.tickets_via_source................................................... [SKIP]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.559866 (Thread-4): Finished running node model.airbyte_utils.tickets_via_source
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.560050 (Thread-4): Began running node model.airbyte_utils.ticket_metrics
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.560347 (Thread-4): 15:55:52 | 22 of 33 START incremental model zendesk_intercom.ticket_metrics............................................. [RUN]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.560977 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.561249 (Thread-4): Compiling model.airbyte_utils.ticket_metrics
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.572344 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.572632 (Thread-4): On model.airbyte_utils.ticket_metrics: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.572784 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.572906 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.648411 (Thread-4): SQL status: BEGIN in 0.08 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.648717 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.648852 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.699334 (Thread-2): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.707249 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.707417 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_fields_scd__dbt_tmp155535468764'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_fields_scd__dbt_tmp155535468764'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_fields_scd__dbt_tmp155535468764'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.935838 (Thread-1): SQL status: SELECT in 0.52 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.940904 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:52.941075 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.190304 (Thread-2): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.196036 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.196206 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.273995 (Thread-4): SQL status: SELECT in 0.62 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.277643 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.278032 (Thread-4): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.285468 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.285641 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.444220 (Thread-1): SQL status: SELECT in 0.50 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.449543 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.449709 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_any__dbt_tmp155551568198'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_any__dbt_tmp155551568198'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_any__dbt_tmp155551568198'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.737827 (Thread-2): SQL status: SELECT in 0.54 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.747028 (Thread-2): 
2022-04-18 15:56:15 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_fields_scd":
2022-04-18 15:56:15 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:15 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:15 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:15 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.750150 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.750307 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_fields_scd'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.803945 (Thread-4): SQL status: SELECT in 0.52 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.810947 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.811120 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:15 [42mnormalization[0m >     "ticket_metrics__dbt_tmp155553282222"
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >       compound sortkey(_airbyte_unique_key,_airbyte_emitted_at)
2022-04-18 15:56:15 [42mnormalization[0m >   as (
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m > -- Final base SQL model
2022-04-18 15:56:15 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:15 [42mnormalization[0m > select
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_unique_key,
2022-04-18 15:56:15 [42mnormalization[0m >     id,
2022-04-18 15:56:15 [42mnormalization[0m >     url,
2022-04-18 15:56:15 [42mnormalization[0m >     "time",
2022-04-18 15:56:15 [42mnormalization[0m >     type,
2022-04-18 15:56:15 [42mnormalization[0m >     metric,
2022-04-18 15:56:15 [42mnormalization[0m >     status,
2022-04-18 15:56:15 [42mnormalization[0m >     reopens,
2022-04-18 15:56:15 [42mnormalization[0m >     replies,
2022-04-18 15:56:15 [42mnormalization[0m >     solved_at,
2022-04-18 15:56:15 [42mnormalization[0m >     ticket_id,
2022-04-18 15:56:15 [42mnormalization[0m >     created_at,
2022-04-18 15:56:15 [42mnormalization[0m >     updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     assigned_at,
2022-04-18 15:56:15 [42mnormalization[0m >     instance_id,
2022-04-18 15:56:15 [42mnormalization[0m >     group_stations,
2022-04-18 15:56:15 [42mnormalization[0m >     assignee_stations,
2022-04-18 15:56:15 [42mnormalization[0m >     status_updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     assignee_updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     requester_updated_at,
2022-04-18 15:56:15 [42mnormalization[0m >     initially_assigned_at,
2022-04-18 15:56:15 [42mnormalization[0m >     reply_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     latest_comment_added_at,
2022-04-18 15:56:15 [42mnormalization[0m >     on_hold_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     agent_wait_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     requester_wait_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     full_resolution_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     first_resolution_time_in_minutes,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:15 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:15 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid
2022-04-18 15:56:15 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:15 [42mnormalization[0m > -- ticket_metrics from "datalake".zendesk_intercom._airbyte_raw_ticket_metrics
2022-04-18 15:56:15 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:15 [42mnormalization[0m > and _airbyte_active_row = 1
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:15 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:15 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:15 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metrics"),
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     true)
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >   );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.885458 (Thread-1): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.890869 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:53.891045 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.271928 (Thread-3): SQL status: INSERT 0 576 in 7.10 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.276970 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.277130 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_policy_metrics'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.319951 (Thread-2): SQL status: SELECT in 0.57 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.323432 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.ticket_fields_scd"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.323844 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.323982 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       delete
2022-04-18 15:56:15 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_fields_scd"
2022-04-18 15:56:15 [42mnormalization[0m >     where (_airbyte_unique_key_scd) in (
2022-04-18 15:56:15 [42mnormalization[0m >         select (_airbyte_unique_key_scd)
2022-04-18 15:56:15 [42mnormalization[0m >         from "ticket_fields_scd__dbt_tmp155535468764"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_fields_scd" ("_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "tag", "url", "type", "title", "active", "position", "required", "raw_title", "removable", "created_at", "updated_at", "description", "sub_type_id", "raw_description", "title_in_portal", "agent_description", "visible_in_portal", "editable_in_portal", "required_in_portal", "raw_title_in_portal", "collapsed_for_agents", "custom_field_options", "system_field_options", "regexp_for_validation", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_fields_hashid")
2022-04-18 15:56:15 [42mnormalization[0m >     (
2022-04-18 15:56:15 [42mnormalization[0m >        select "_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "tag", "url", "type", "title", "active", "position", "required", "raw_title", "removable", "created_at", "updated_at", "description", "sub_type_id", "raw_description", "title_in_portal", "agent_description", "visible_in_portal", "editable_in_portal", "required_in_portal", "raw_title_in_portal", "collapsed_for_agents", "custom_field_options", "system_field_options", "regexp_for_validation", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_fields_hashid"
2022-04-18 15:56:15 [42mnormalization[0m >        from "ticket_fields_scd__dbt_tmp155535468764"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.397780 (Thread-1): SQL status: SELECT in 0.51 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.401080 (Thread-1): 
2022-04-18 15:56:15 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."sla_policies_filter_any":
2022-04-18 15:56:15 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:15 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:15 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:15 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.404455 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.404626 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.782118 (Thread-3): SQL status: SELECT in 0.50 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.784806 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.784966 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.785090 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.823422 (Thread-1): SQL status: SELECT in 0.42 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.825713 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.sla_policies_filter_any"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.826120 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.826257 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."sla_policies_filter_any" ("_airbyte_filter_hashid", "field", "value", "operator", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_any_hashid")
2022-04-18 15:56:15 [42mnormalization[0m >     (
2022-04-18 15:56:15 [42mnormalization[0m >        select "_airbyte_filter_hashid", "field", "value", "operator", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_any_hashid"
2022-04-18 15:56:15 [42mnormalization[0m >        from "sla_policies_filter_any__dbt_tmp155551568198"
2022-04-18 15:56:15 [42mnormalization[0m >     );
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.878123 (Thread-3): SQL status: COMMIT in 0.09 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.878995 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.879212 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: Close
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.880023 (Thread-3): 15:55:54 | 15 of 33 OK created incremental model zendesk_intercom.sla_policies_policy_metrics........................... [INSERT 0 576 in 12.35s]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.880236 (Thread-3): Finished running node model.airbyte_utils.sla_policies_policy_metrics
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.880859 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.881478 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.881668 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.890398 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.890763 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.891149 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.891348 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.891861 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.892035 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.899871 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.900225 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.900647 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.900845 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.901353 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.901618 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.911377 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.911676 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.912053 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.912272 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.912765 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.913081 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.920873 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.921180 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.921565 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.921767 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.922257 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.922526 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.930216 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.930510 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.930888 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.931086 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.931596 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.931865 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.939447 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.939744 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.940153 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.940379 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_status_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.940866 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab1".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.941038 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_status_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.949205 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_status_ab1"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.949515 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.949898 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_status_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.950111 (Thread-3): Began running node model.airbyte_utils.tickets_via_source_from_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.950597 (Thread-3): Finished running node model.airbyte_utils.tickets_via_source_from_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.950850 (Thread-3): Began running node model.airbyte_utils.tickets_via_source_to_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.951151 (Thread-3): Finished running node model.airbyte_utils.tickets_via_source_to_ab1
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.951430 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.951809 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.951976 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.964008 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.964461 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.964897 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.965102 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.965610 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.965887 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.979489 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.979842 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.980282 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.980494 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.980997 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.981175 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.992964 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.993339 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.993746 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.993948 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.994368 (Thread-1): SQL status: INSERT 0 13 in 0.17 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.994805 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.999477 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.999733 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:54.999923 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'sla_policies_filter_any'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.011950 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.012366 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.012828 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.013038 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.013566 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.013857 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.021242 (Thread-4): SQL status: SELECT in 1.21 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.026066 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.030048 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.030385 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics__dbt_tmp155553282222'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics__dbt_tmp155553282222'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics__dbt_tmp155553282222'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.031055 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.031552 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.031786 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.032151 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.032563 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.046202 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.046672 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.047136 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.047346 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_status_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.047874 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab2".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.048167 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_status_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.060597 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_status_ab2"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.061060 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.061550 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_status_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.061763 (Thread-3): Began running node model.airbyte_utils.tickets_via_source_from_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.062145 (Thread-3): Finished running node model.airbyte_utils.tickets_via_source_from_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.062337 (Thread-3): Began running node model.airbyte_utils.tickets_via_source_to_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.062773 (Thread-3): Finished running node model.airbyte_utils.tickets_via_source_to_ab2
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.063068 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.063470 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.063999 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.079340 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.080584 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.081092 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.081315 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.081861 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.082178 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.096846 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.097307 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.097790 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.098015 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.098576 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.098881 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.113410 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.113842 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.114323 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.114543 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.115094 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.115408 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.132659 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.133095 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.133557 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.133773 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.134320 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.134628 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.148764 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.149168 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.149616 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.149831 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.150358 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.150664 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.164739 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.165109 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.165540 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.165750 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_status_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.166259 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab3".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.166445 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_status_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.180873 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_status_ab3"
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.181303 (Thread-3): finished collecting timing info
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.181743 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_status_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.181979 (Thread-3): Began running node model.airbyte_utils.tickets_via_source_from_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.182317 (Thread-3): Finished running node model.airbyte_utils.tickets_via_source_from_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.182604 (Thread-3): Began running node model.airbyte_utils.tickets_via_source_to_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.182845 (Thread-3): Finished running node model.airbyte_utils.tickets_via_source_to_ab3
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.183149 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.183614 (Thread-3): 15:55:55 | 23 of 33 START incremental model zendesk_intercom.ticket_metrics_agent_wait_time_in_minutes.................. [RUN]
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.184151 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.184451 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.193767 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.193936 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: BEGIN
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.194092 (Thread-3): Opening a new connection, currently in state closed
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.194214 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.239754 (Thread-3): SQL status: BEGIN in 0.05 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.240108 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.240269 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:15 [42mnormalization[0m >         col_name,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:15 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else col_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:15 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:15 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:15 [42mnormalization[0m >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:15 [42mnormalization[0m >       select
2022-04-18 15:56:15 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:15 [42mnormalization[0m >         columnname,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:15 [42mnormalization[0m >           else external_type
2022-04-18 15:56:15 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >         case
2022-04-18 15:56:15 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:15 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:15 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:15 [42mnormalization[0m >             '')::int
2022-04-18 15:56:15 [42mnormalization[0m >           else null
2022-04-18 15:56:15 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m >       from
2022-04-18 15:56:15 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:15 [42mnormalization[0m >       where
2022-04-18 15:56:15 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:15 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:15 [42mnormalization[0m >       union all
2022-04-18 15:56:15 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:15 [42mnormalization[0m >     )
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     select
2022-04-18 15:56:15 [42mnormalization[0m >       column_name,
2022-04-18 15:56:15 [42mnormalization[0m >       data_type,
2022-04-18 15:56:15 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     from unioned
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:15 [42mnormalization[0m >     
2022-04-18 15:56:15 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:15 [42mnormalization[0m >   
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.260211 (Thread-1): SQL status: SELECT in 0.25 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.263313 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.263505 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.263673 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: COMMIT
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.311881 (Thread-4): SQL status: SELECT in 0.28 seconds
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.319292 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:15 [42mnormalization[0m > 2022-04-18 15:55:55.319463 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:15 [42mnormalization[0m >         select
2022-04-18 15:56:15 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:15 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:15 [42mnormalization[0m >           column_name,
2022-04-18 15:56:15 [42mnormalization[0m >           data_type,
2022-04-18 15:56:15 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:15 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:15 [42mnormalization[0m >         where table_name = 'ticket_metrics'
2022-04-18 15:56:15 [42mnormalization[0m >     ),
2022-04-18 15:56:15 [42mnormalization[0m > 
2022-04-18 15:56:15 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.538600 (Thread-3): SQL status: SELECT in 0.30 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.549061 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.549454 (Thread-3): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.556198 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.556400 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.556622 (Thread-4): SQL status: SELECT in 0.24 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.562178 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.562353 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics__dbt_tmp155553282222'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics__dbt_tmp155553282222'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics__dbt_tmp155553282222'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.562554 (Thread-1): SQL status: COMMIT in 0.30 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.563231 (Thread-1): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.563421 (Thread-1): On model.airbyte_utils.sla_policies_filter_any: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.564172 (Thread-1): 15:55:55 | 20 of 33 OK created incremental model zendesk_intercom.sla_policies_filter_any............................... [INSERT 0 13 in 4.68s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.564410 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter_any
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.565044 (Thread-1): Began running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.565464 (Thread-1): 15:55:55 | 24 of 33 START incremental model zendesk_intercom.ticket_metrics_first_resolution_time_in_minutes............ [RUN]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.565996 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.566170 (Thread-1): Compiling model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.575369 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.575555 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.575731 (Thread-2): SQL status: INSERT 0 28 in 1.25 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.575941 (Thread-1): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.581559 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.581732 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.581913 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields_scd'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields_scd'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields_scd'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.647943 (Thread-1): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.648281 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:55.648426 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.026355 (Thread-3): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.030708 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.030884 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:16 [42mnormalization[0m >     "ticket_metrics_agent_wait_time_in_minutes__dbt_tmp155555553188"
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:16 [42mnormalization[0m >   as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > with __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab1 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(agent_wait_time_in_minutes, 'business', true) != '' then json_extract_path_text(agent_wait_time_in_minutes, 'business', true) end as business,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(agent_wait_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(agent_wait_time_in_minutes, 'calendar', true) end as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_metrics_scd" as table_alias
2022-04-18 15:56:16 [42mnormalization[0m > -- agent_wait_time_in_minutes at ticket_metrics/agent_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > and agent_wait_time_in_minutes is not null
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab2 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(business as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as business,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(calendar as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > -- agent_wait_time_in_minutes at ticket_metrics/agent_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab3 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab2
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_agent_wait_time_in_minutes_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab2 tmp
2022-04-18 15:56:16 [42mnormalization[0m > -- agent_wait_time_in_minutes at ticket_metrics/agent_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     business,
2022-04-18 15:56:16 [42mnormalization[0m >     calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_agent_wait_time_in_minutes_hashid
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > -- agent_wait_time_in_minutes at ticket_metrics/agent_wait_time_in_minutes from "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:16 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metrics_agent_wait_time_in_minutes"),
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     true)
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.044360 (Thread-4): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.049742 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.049900 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.120487 (Thread-2): SQL status: SELECT in 0.54 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.123976 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.124352 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.124508 (Thread-2): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         drop view _airbyte_zendesk_intercom.ticket_fields_stg
2022-04-18 15:56:16 [42mnormalization[0m >       
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.140541 (Thread-2): SQL status: DROP VIEW in 0.02 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.141592 (Thread-2): On model.airbyte_utils.ticket_fields_scd: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.141749 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.141882 (Thread-2): On model.airbyte_utils.ticket_fields_scd: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.174567 (Thread-1): SQL status: SELECT in 0.53 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.186347 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.187274 (Thread-1): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.194206 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.194371 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.329001 (Thread-2): SQL status: COMMIT in 0.19 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.329925 (Thread-2): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.330138 (Thread-2): On model.airbyte_utils.ticket_fields_scd: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.330999 (Thread-2): 15:55:56 | 9 of 33 OK created incremental model zendesk_intercom.ticket_fields_scd...................................... [INSERT 0 28 in 23.54s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.331403 (Thread-2): Finished running node model.airbyte_utils.ticket_fields_scd
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.331712 (Thread-2): Began running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.332226 (Thread-2): 15:55:56 | 25 of 33 START incremental model zendesk_intercom.ticket_metrics_full_resolution_time_in_minutes............. [RUN]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.332867 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.333120 (Thread-2): Compiling model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.342916 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.343087 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.343229 (Thread-2): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.343343 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.418969 (Thread-2): SQL status: BEGIN in 0.08 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.419298 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.419432 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.486896 (Thread-4): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.496044 (Thread-4): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metrics":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.500870 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.501029 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.625443 (Thread-1): SQL status: SELECT in 0.43 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.629918 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.695880 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:16 [42mnormalization[0m >     "ticket_metrics_first_resolution_time_in_mi__dbt_tmp155556191325"
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:16 [42mnormalization[0m >   as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > with __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab1 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(first_resolution_time_in_minutes, 'business', true) != '' then json_extract_path_text(first_resolution_time_in_minutes, 'business', true) end as business,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(first_resolution_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(first_resolution_time_in_minutes, 'calendar', true) end as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_metrics_scd" as table_alias
2022-04-18 15:56:16 [42mnormalization[0m > -- first_resolution_time_in_minutes at ticket_metrics/first_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > and first_resolution_time_in_minutes is not null
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab2 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(business as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as business,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(calendar as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > -- first_resolution_time_in_minutes at ticket_metrics/first_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab3 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab2
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_first_resolution_time_in_minutes_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab2 tmp
2022-04-18 15:56:16 [42mnormalization[0m > -- first_resolution_time_in_minutes at ticket_metrics/first_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     business,
2022-04-18 15:56:16 [42mnormalization[0m >     calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_first_resolution_time_in_minutes_hashid
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > -- first_resolution_time_in_minutes at ticket_metrics/first_resolution_time_in_minutes from "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:16 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metrics_first_resolution_time_in_minutes"),
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     true)
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.700073 (Thread-3): SQL status: SELECT in 0.67 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.703977 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.704140 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp155555553188'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp155555553188'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp155555553188'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.874737 (Thread-2): SQL status: SELECT in 0.46 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.885018 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.885415 (Thread-2): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.892126 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.892430 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.996125 (Thread-4): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.999494 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:56.999940 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.000088 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       delete
2022-04-18 15:56:16 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_metrics"
2022-04-18 15:56:16 [42mnormalization[0m >     where (_airbyte_unique_key) in (
2022-04-18 15:56:16 [42mnormalization[0m >         select (_airbyte_unique_key)
2022-04-18 15:56:16 [42mnormalization[0m >         from "ticket_metrics__dbt_tmp155553282222"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metrics" ("id", "url", "time", "type", "metric", "status", "reopens", "replies", "solved_at", "ticket_id", "created_at", "updated_at", "assigned_at", "instance_id", "group_stations", "assignee_stations", "status_updated_at", "assignee_updated_at", "requester_updated_at", "initially_assigned_at", "reply_time_in_minutes", "latest_comment_added_at", "on_hold_time_in_minutes", "agent_wait_time_in_minutes", "requester_wait_time_in_minutes", "full_resolution_time_in_minutes", "first_resolution_time_in_minutes", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metrics_hashid", "_airbyte_unique_key")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "id", "url", "time", "type", "metric", "status", "reopens", "replies", "solved_at", "ticket_id", "created_at", "updated_at", "assigned_at", "instance_id", "group_stations", "assignee_stations", "status_updated_at", "assignee_updated_at", "requester_updated_at", "initially_assigned_at", "reply_time_in_minutes", "latest_comment_added_at", "on_hold_time_in_minutes", "agent_wait_time_in_minutes", "requester_wait_time_in_minutes", "full_resolution_time_in_minutes", "first_resolution_time_in_minutes", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metrics_hashid", "_airbyte_unique_key"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_metrics__dbt_tmp155553282222"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.068556 (Thread-1): SQL status: SELECT in 0.37 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.073054 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.073223 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp155556191325'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp155556191325'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp155556191325'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.174456 (Thread-3): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.179330 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.179494 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.242280 (Thread-2): SQL status: SELECT in 0.35 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.246912 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.247076 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:16 [42mnormalization[0m >     "ticket_metrics_full_resolution_time_in_min__dbt_tmp155556889237"
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:16 [42mnormalization[0m >   as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > with __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab1 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(full_resolution_time_in_minutes, 'business', true) != '' then json_extract_path_text(full_resolution_time_in_minutes, 'business', true) end as business,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(full_resolution_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(full_resolution_time_in_minutes, 'calendar', true) end as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_metrics_scd" as table_alias
2022-04-18 15:56:16 [42mnormalization[0m > -- full_resolution_time_in_minutes at ticket_metrics/full_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > and full_resolution_time_in_minutes is not null
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab2 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(business as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as business,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(calendar as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > -- full_resolution_time_in_minutes at ticket_metrics/full_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab3 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab2
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_full_resolution_time_in_minutes_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab2 tmp
2022-04-18 15:56:16 [42mnormalization[0m > -- full_resolution_time_in_minutes at ticket_metrics/full_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     business,
2022-04-18 15:56:16 [42mnormalization[0m >     calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_full_resolution_time_in_minutes_hashid
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > -- full_resolution_time_in_minutes at ticket_metrics/full_resolution_time_in_minutes from "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:16 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metrics_full_resolution_time_in_minutes"),
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     true)
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.408469 (Thread-1): SQL status: SELECT in 0.33 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.413514 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.413688 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.518511 (Thread-3): SQL status: SELECT in 0.34 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.525675 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.525849 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp155555553188'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp155555553188'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp155555553188'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.628057 (Thread-1): SQL status: SELECT in 0.21 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.633335 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.633500 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp155556191325'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp155556191325'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp155556191325'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.646563 (Thread-4): SQL status: INSERT 0 14776 in 0.65 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.650707 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.650860 (Thread-4): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.758445 (Thread-3): SQL status: SELECT in 0.23 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.763467 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:57.763621 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.005007 (Thread-2): SQL status: SELECT in 0.76 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.009546 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.009721 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp155556889237'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp155556889237'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp155556889237'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.030522 (Thread-1): SQL status: SELECT in 0.40 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.034903 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.035056 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.136934 (Thread-4): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.140092 (Thread-4): On model.airbyte_utils.ticket_metrics: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.140275 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.140418 (Thread-4): On model.airbyte_utils.ticket_metrics: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.215159 (Thread-4): SQL status: COMMIT in 0.07 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.216071 (Thread-4): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.216320 (Thread-4): On model.airbyte_utils.ticket_metrics: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.217191 (Thread-4): 15:55:58 | 22 of 33 OK created incremental model zendesk_intercom.ticket_metrics........................................ [INSERT 0 14776 in 5.66s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.217601 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.217938 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.218505 (Thread-4): 15:55:58 | 26 of 33 START incremental model zendesk_intercom.ticket_metrics_on_hold_time_in_minutes..................... [RUN]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.219131 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.219324 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.231703 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.231881 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.232039 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.232163 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.294013 (Thread-4): SQL status: BEGIN in 0.06 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.294329 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.294464 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.305449 (Thread-3): SQL status: SELECT in 0.54 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.308479 (Thread-3): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metrics_agent_wait_time_in_minutes":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.311693 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.311852 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.456539 (Thread-2): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.461727 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.461916 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.468798 (Thread-1): SQL status: SELECT in 0.43 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.471686 (Thread-1): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metrics_first_resolution_time_in_minutes":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.478521 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.478684 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.761647 (Thread-3): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.764191 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.764680 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.764832 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metrics_agent_wait_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_agent_wait_time_in_minutes_hashid")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_agent_wait_time_in_minutes_hashid"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_metrics_agent_wait_time_in_minutes__dbt_tmp155555553188"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.866122 (Thread-4): SQL status: SELECT in 0.57 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.880961 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.881157 (Thread-3): SQL status: INSERT 0 14778 in 0.12 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.893696 (Thread-4): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.906478 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.906681 (Thread-2): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.913684 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.906871 (Thread-1): SQL status: SELECT in 0.43 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.915771 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.909019 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.916131 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.909236 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.913852 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp155556889237'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp155556889237'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp155556889237'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.924483 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:58.924639 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metrics_first_resolution_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_first_resolution_time_in_minutes_hashid")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_first_resolution_time_in_minutes_hashid"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_metrics_first_resolution_time_in_mi__dbt_tmp155556191325"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.102983 (Thread-1): SQL status: INSERT 0 14778 in 0.18 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.108052 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.108229 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.323609 (Thread-4): SQL status: SELECT in 0.41 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.327948 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.328117 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:16 [42mnormalization[0m >     "ticket_metrics_on_hold_time_in_minutes__dbt_tmp155558899317"
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:16 [42mnormalization[0m >   as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > with __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab1 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(on_hold_time_in_minutes, 'business', true) != '' then json_extract_path_text(on_hold_time_in_minutes, 'business', true) end as business,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(on_hold_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(on_hold_time_in_minutes, 'calendar', true) end as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_metrics_scd" as table_alias
2022-04-18 15:56:16 [42mnormalization[0m > -- on_hold_time_in_minutes at ticket_metrics/on_hold_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > and on_hold_time_in_minutes is not null
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab2 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(business as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as business,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(calendar as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > -- on_hold_time_in_minutes at ticket_metrics/on_hold_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab3 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab2
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_on_hold_time_in_minutes_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab2 tmp
2022-04-18 15:56:16 [42mnormalization[0m > -- on_hold_time_in_minutes at ticket_metrics/on_hold_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     business,
2022-04-18 15:56:16 [42mnormalization[0m >     calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_on_hold_time_in_minutes_hashid
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > -- on_hold_time_in_minutes at ticket_metrics/on_hold_time_in_minutes from "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:16 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metrics_on_hold_time_in_minutes"),
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     true)
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.329338 (Thread-3): SQL status: SELECT in 0.41 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.331271 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.331424 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.331547 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.389680 (Thread-2): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.395024 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.395196 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.422255 (Thread-3): SQL status: COMMIT in 0.09 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.423064 (Thread-3): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.423274 (Thread-3): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.424076 (Thread-3): 15:55:59 | 23 of 33 OK created incremental model zendesk_intercom.ticket_metrics_agent_wait_time_in_minutes............. [INSERT 0 14778 in 4.24s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.424315 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.425011 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.425415 (Thread-3): 15:55:59 | 27 of 33 START incremental model zendesk_intercom.ticket_metrics_reply_time_in_minutes....................... [RUN]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.426068 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.426281 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_reply_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.443005 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.443180 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.443323 (Thread-3): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.443441 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.504929 (Thread-3): SQL status: BEGIN in 0.06 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.505230 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.505367 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.530868 (Thread-1): SQL status: SELECT in 0.42 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.533183 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.533346 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.533474 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.614802 (Thread-1): SQL status: COMMIT in 0.08 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.615712 (Thread-1): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.615928 (Thread-1): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.616802 (Thread-1): 15:55:59 | 24 of 33 OK created incremental model zendesk_intercom.ticket_metrics_first_resolution_time_in_minutes....... [INSERT 0 14778 in 4.05s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.617391 (Thread-1): Finished running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.617778 (Thread-1): Began running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.618259 (Thread-1): 15:55:59 | 28 of 33 START incremental model zendesk_intercom.ticket_metrics_requester_wait_time_in_minutes.............. [RUN]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.618926 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.619108 (Thread-1): Compiling model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.641813 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.642075 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.642225 (Thread-1): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.642343 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.712137 (Thread-1): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.712475 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.712609 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.758642 (Thread-4): SQL status: SELECT in 0.43 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.763231 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.763407 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp155558899317'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp155558899317'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp155558899317'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.870084 (Thread-2): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.873282 (Thread-2): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metrics_full_resolution_time_in_minutes":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.876363 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.876523 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.882613 (Thread-3): SQL status: SELECT in 0.38 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.900779 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.901260 (Thread-3): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.913773 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:55:59.913945 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.262199 (Thread-4): SQL status: SELECT in 0.50 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.268948 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.269127 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.319945 (Thread-1): SQL status: SELECT in 0.61 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.330450 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.330889 (Thread-1): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.356434 (Thread-2): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.351777 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.358720 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.360948 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.360505 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.361105 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metrics_full_resolution_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_full_resolution_time_in_minutes_hashid")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_full_resolution_time_in_minutes_hashid"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_metrics_full_resolution_time_in_min__dbt_tmp155556889237"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.360259 (Thread-3): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.368917 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.369076 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:16 [42mnormalization[0m >     "ticket_metrics_reply_time_in_minutes__dbt_tmp155559910209"
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:16 [42mnormalization[0m >   as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > with __dbt__cte__ticket_metrics_reply_time_in_minutes_ab1 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(reply_time_in_minutes, 'business', true) != '' then json_extract_path_text(reply_time_in_minutes, 'business', true) end as business,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(reply_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(reply_time_in_minutes, 'calendar', true) end as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_metrics_scd" as table_alias
2022-04-18 15:56:16 [42mnormalization[0m > -- reply_time_in_minutes at ticket_metrics/reply_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > and reply_time_in_minutes is not null
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_reply_time_in_minutes_ab2 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_reply_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(business as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as business,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(calendar as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_reply_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > -- reply_time_in_minutes at ticket_metrics/reply_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_reply_time_in_minutes_ab3 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_reply_time_in_minutes_ab2
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_reply_time_in_minutes_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_reply_time_in_minutes_ab2 tmp
2022-04-18 15:56:16 [42mnormalization[0m > -- reply_time_in_minutes at ticket_metrics/reply_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_reply_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     business,
2022-04-18 15:56:16 [42mnormalization[0m >     calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_reply_time_in_minutes_hashid
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_reply_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > -- reply_time_in_minutes at ticket_metrics/reply_time_in_minutes from "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:16 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metrics_reply_time_in_minutes"),
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     true)
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.474366 (Thread-2): SQL status: INSERT 0 14778 in 0.11 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.483564 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.483746 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.646193 (Thread-4): SQL status: SELECT in 0.38 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.651574 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.651753 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp155558899317'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp155558899317'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp155558899317'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.745257 (Thread-3): SQL status: SELECT in 0.38 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.749970 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.750173 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_reply_time_in_minutes__dbt_tmp155559910209'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_reply_time_in_minutes__dbt_tmp155559910209'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_reply_time_in_minutes__dbt_tmp155559910209'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.794253 (Thread-1): SQL status: SELECT in 0.43 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.809072 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.809332 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:16 [42mnormalization[0m >     "ticket_metrics_requester_wait_time_in_minu__dbt_tmp155600348621"
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:16 [42mnormalization[0m >   as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > with __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab1 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(requester_wait_time_in_minutes, 'business', true) != '' then json_extract_path_text(requester_wait_time_in_minutes, 'business', true) end as business,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(requester_wait_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(requester_wait_time_in_minutes, 'calendar', true) end as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_metrics_scd" as table_alias
2022-04-18 15:56:16 [42mnormalization[0m > -- requester_wait_time_in_minutes at ticket_metrics/requester_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > and requester_wait_time_in_minutes is not null
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab2 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(business as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as business,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(calendar as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-18 15:56:16 [42mnormalization[0m > -- requester_wait_time_in_minutes at ticket_metrics/requester_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab3 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab2
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_requester_wait_time_in_minutes_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab2 tmp
2022-04-18 15:56:16 [42mnormalization[0m > -- requester_wait_time_in_minutes at ticket_metrics/requester_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     business,
2022-04-18 15:56:16 [42mnormalization[0m >     calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_requester_wait_time_in_minutes_hashid
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-18 15:56:16 [42mnormalization[0m > -- requester_wait_time_in_minutes at ticket_metrics/requester_wait_time_in_minutes from "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:16 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metrics_requester_wait_time_in_minutes"),
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     true)
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.959348 (Thread-2): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.961847 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.962024 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:00.962156 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.042197 (Thread-4): SQL status: SELECT in 0.39 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.047295 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.047511 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.071407 (Thread-3): SQL status: SELECT in 0.32 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.076776 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.076971 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.208514 (Thread-2): SQL status: COMMIT in 0.25 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.209451 (Thread-2): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.209669 (Thread-2): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.210511 (Thread-2): 15:56:01 | 25 of 33 OK created incremental model zendesk_intercom.ticket_metrics_full_resolution_time_in_minutes........ [INSERT 0 14778 in 4.88s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.211381 (Thread-2): Finished running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.211614 (Thread-2): Began running node model.airbyte_utils.ticket_metrics_status
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.212010 (Thread-2): 15:56:01 | 29 of 33 START incremental model zendesk_intercom.ticket_metrics_status...................................... [RUN]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.212810 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.212994 (Thread-2): Compiling model.airbyte_utils.ticket_metrics_status
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.222909 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.223077 (Thread-2): On model.airbyte_utils.ticket_metrics_status: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.223220 (Thread-2): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.223341 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.286044 (Thread-2): SQL status: BEGIN in 0.06 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.286378 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.286519 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.358481 (Thread-1): SQL status: SELECT in 0.55 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.362967 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.363136 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp155600348621'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp155600348621'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp155600348621'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.403128 (Thread-4): SQL status: SELECT in 0.36 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.406167 (Thread-4): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metrics_on_hold_time_in_minutes":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.409387 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.420022 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.438803 (Thread-3): SQL status: SELECT in 0.36 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.450201 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.450440 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_reply_time_in_minutes__dbt_tmp155559910209'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_reply_time_in_minutes__dbt_tmp155559910209'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_reply_time_in_minutes__dbt_tmp155559910209'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.817391 (Thread-1): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.822343 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.822553 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.829904 (Thread-4): SQL status: SELECT in 0.41 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.832431 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.832861 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.833001 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metrics_on_hold_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_on_hold_time_in_minutes_hashid")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_on_hold_time_in_minutes_hashid"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_metrics_on_hold_time_in_minutes__dbt_tmp155558899317"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.906123 (Thread-2): SQL status: SELECT in 0.62 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.916419 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_status"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.916853 (Thread-2): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.924788 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.924969 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.928616 (Thread-3): SQL status: SELECT in 0.48 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.933128 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.933293 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.956395 (Thread-4): SQL status: INSERT 0 14778 in 0.12 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.961390 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:01.961588 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.336133 (Thread-1): SQL status: SELECT in 0.51 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.343298 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.343531 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp155600348621'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp155600348621'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp155600348621'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.344591 (Thread-2): SQL status: SELECT in 0.42 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.348847 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.349022 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:16 [42mnormalization[0m >     "ticket_metrics_status__dbt_tmp155601921233"
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:16 [42mnormalization[0m >   as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > with __dbt__cte__ticket_metrics_status_ab1 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(status, 'business', true) != '' then json_extract_path_text(status, 'business', true) end as business,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(status, 'calendar', true) != '' then json_extract_path_text(status, 'calendar', true) end as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_metrics_scd" as table_alias
2022-04-18 15:56:16 [42mnormalization[0m > -- status at ticket_metrics/status
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > and status is not null
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_status_ab2 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_status_ab1
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(business as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as business,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(calendar as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_status_ab1
2022-04-18 15:56:16 [42mnormalization[0m > -- status at ticket_metrics/status
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_metrics_status_ab3 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_status_ab2
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_status_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_status_ab2 tmp
2022-04-18 15:56:16 [42mnormalization[0m > -- status at ticket_metrics/status
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_metrics_status_ab3
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_metrics_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     business,
2022-04-18 15:56:16 [42mnormalization[0m >     calendar,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_status_hashid
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_metrics_status_ab3
2022-04-18 15:56:16 [42mnormalization[0m > -- status at ticket_metrics/status from "datalake".zendesk_intercom."ticket_metrics_scd"
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:16 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_metrics_status"),
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     true)
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.416216 (Thread-4): SQL status: SELECT in 0.45 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.418685 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.418854 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.418986 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.425802 (Thread-3): SQL status: SELECT in 0.49 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.428737 (Thread-3): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metrics_reply_time_in_minutes":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.431958 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.432122 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.626318 (Thread-4): SQL status: COMMIT in 0.21 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.627247 (Thread-4): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.627470 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.628373 (Thread-4): 15:56:02 | 26 of 33 OK created incremental model zendesk_intercom.ticket_metrics_on_hold_time_in_minutes................ [INSERT 0 14778 in 4.41s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.629381 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.629615 (Thread-4): Began running node model.airbyte_utils.tickets_via_source_from
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.629935 (Thread-4): 15:56:02 | 30 of 33 SKIP relation zendesk_intercom.tickets_via_source_from.............................................. [SKIP]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.630526 (Thread-4): Finished running node model.airbyte_utils.tickets_via_source_from
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.630750 (Thread-4): Began running node model.airbyte_utils.tickets_via_source_to
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.631010 (Thread-4): 15:56:02 | 31 of 33 SKIP relation zendesk_intercom.tickets_via_source_to................................................ [SKIP]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.631561 (Thread-4): Finished running node model.airbyte_utils.tickets_via_source_to
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.631803 (Thread-4): Began running node model.airbyte_utils.ticket_fields
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.632143 (Thread-4): 15:56:02 | 32 of 33 START incremental model zendesk_intercom.ticket_fields.............................................. [RUN]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.632922 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.633108 (Thread-4): Compiling model.airbyte_utils.ticket_fields
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.644818 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.645030 (Thread-4): On model.airbyte_utils.ticket_fields: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.645177 (Thread-4): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.645295 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.665978 (Thread-2): SQL status: SELECT in 0.32 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.670687 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.670866 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_status__dbt_tmp155601921233'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_status__dbt_tmp155601921233'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_status__dbt_tmp155601921233'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.728761 (Thread-1): SQL status: SELECT in 0.39 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.733891 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.734133 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.734395 (Thread-4): SQL status: BEGIN in 0.09 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.734543 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.734665 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.744387 (Thread-3): SQL status: SELECT in 0.31 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.746327 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.746720 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.746859 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metrics_reply_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_reply_time_in_minutes_hashid")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_reply_time_in_minutes_hashid"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_metrics_reply_time_in_minutes__dbt_tmp155559910209"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.864783 (Thread-3): SQL status: INSERT 0 14778 in 0.12 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.871352 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:02.871519 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.129144 (Thread-2): SQL status: SELECT in 0.46 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.134228 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.134396 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.207862 (Thread-1): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.210861 (Thread-1): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metrics_requester_wait_time_in_minutes":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.213942 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.214096 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.365558 (Thread-4): SQL status: SELECT in 0.63 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.369488 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_fields"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.369900 (Thread-4): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.384890 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.385199 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.410782 (Thread-3): SQL status: SELECT in 0.54 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.413183 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.413346 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.413470 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.575194 (Thread-2): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.580687 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.580870 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_status__dbt_tmp155601921233'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_status__dbt_tmp155601921233'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_status__dbt_tmp155601921233'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.586446 (Thread-1): SQL status: SELECT in 0.37 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.588523 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.588919 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.589057 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metrics_requester_wait_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_requester_wait_time_in_minutes_hashid")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_requester_wait_time_in_minutes_hashid"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_metrics_requester_wait_time_in_minu__dbt_tmp155600348621"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.597639 (Thread-3): SQL status: COMMIT in 0.18 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.598357 (Thread-3): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.598571 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.599400 (Thread-3): 15:56:03 | 27 of 33 OK created incremental model zendesk_intercom.ticket_metrics_reply_time_in_minutes.................. [INSERT 0 14778 in 4.17s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.600398 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.600634 (Thread-3): Began running node model.airbyte_utils.ticket_fields_custom_field_options_ab1
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.601133 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab1".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.601313 (Thread-3): Compiling model.airbyte_utils.ticket_fields_custom_field_options_ab1
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.612150 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab1".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.612353 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options_ab1: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.612511 (Thread-3): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.612630 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.682447 (Thread-3): SQL status: BEGIN in 0.07 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.682763 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab1".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.682897 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options_ab1: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options_ab1"} */
2022-04-18 15:56:16 [42mnormalization[0m > with max_value as (
2022-04-18 15:56:16 [42mnormalization[0m >             select max(json_array_length(custom_field_options, true)) as max_number_of_items
2022-04-18 15:56:16 [42mnormalization[0m >             from "datalake".zendesk_intercom."ticket_fields_scd"
2022-04-18 15:56:16 [42mnormalization[0m >         )
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >             case when max_number_of_items is not null and max_number_of_items > 1
2022-04-18 15:56:16 [42mnormalization[0m >             then max_number_of_items
2022-04-18 15:56:16 [42mnormalization[0m >             else 1 end as max_number_of_items
2022-04-18 15:56:16 [42mnormalization[0m >         from max_value
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.700117 (Thread-1): SQL status: INSERT 0 14778 in 0.11 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.707025 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.707211 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.770279 (Thread-3): SQL status: SELECT in 0.09 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.778084 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_fields_custom_field_options_ab1"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.778527 (Thread-3): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.778738 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options_ab1: ROLLBACK
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.782463 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options_ab1: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.783244 (Thread-3): Finished running node model.airbyte_utils.ticket_fields_custom_field_options_ab1
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.783826 (Thread-3): Began running node model.airbyte_utils.ticket_fields_custom_field_options_ab2
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.784794 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab2".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.785003 (Thread-3): Compiling model.airbyte_utils.ticket_fields_custom_field_options_ab2
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.801830 (Thread-4): SQL status: SELECT in 0.42 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.803707 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_fields_custom_field_options_ab2"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.809021 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.809414 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:16 [42mnormalization[0m >     "ticket_fields__dbt_tmp155603373710"
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >       compound sortkey(_airbyte_unique_key,_airbyte_emitted_at)
2022-04-18 15:56:16 [42mnormalization[0m >   as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > -- Final base SQL model
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_fields_scd"
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_unique_key,
2022-04-18 15:56:16 [42mnormalization[0m >     id,
2022-04-18 15:56:16 [42mnormalization[0m >     "tag",
2022-04-18 15:56:16 [42mnormalization[0m >     url,
2022-04-18 15:56:16 [42mnormalization[0m >     type,
2022-04-18 15:56:16 [42mnormalization[0m >     title,
2022-04-18 15:56:16 [42mnormalization[0m >     active,
2022-04-18 15:56:16 [42mnormalization[0m >     position,
2022-04-18 15:56:16 [42mnormalization[0m >     required,
2022-04-18 15:56:16 [42mnormalization[0m >     raw_title,
2022-04-18 15:56:16 [42mnormalization[0m >     removable,
2022-04-18 15:56:16 [42mnormalization[0m >     created_at,
2022-04-18 15:56:16 [42mnormalization[0m >     updated_at,
2022-04-18 15:56:16 [42mnormalization[0m >     description,
2022-04-18 15:56:16 [42mnormalization[0m >     sub_type_id,
2022-04-18 15:56:16 [42mnormalization[0m >     raw_description,
2022-04-18 15:56:16 [42mnormalization[0m >     title_in_portal,
2022-04-18 15:56:16 [42mnormalization[0m >     agent_description,
2022-04-18 15:56:16 [42mnormalization[0m >     visible_in_portal,
2022-04-18 15:56:16 [42mnormalization[0m >     editable_in_portal,
2022-04-18 15:56:16 [42mnormalization[0m >     required_in_portal,
2022-04-18 15:56:16 [42mnormalization[0m >     raw_title_in_portal,
2022-04-18 15:56:16 [42mnormalization[0m >     collapsed_for_agents,
2022-04-18 15:56:16 [42mnormalization[0m >     custom_field_options,
2022-04-18 15:56:16 [42mnormalization[0m >     system_field_options,
2022-04-18 15:56:16 [42mnormalization[0m >     regexp_for_validation,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_fields_hashid
2022-04-18 15:56:16 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_fields_scd"
2022-04-18 15:56:16 [42mnormalization[0m > -- ticket_fields from "datalake".zendesk_intercom._airbyte_raw_ticket_fields
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > and _airbyte_active_row = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:16 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_fields"),
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     true)
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.809739 (Thread-3): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.810254 (Thread-3): Finished running node model.airbyte_utils.ticket_fields_custom_field_options_ab2
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.810813 (Thread-3): Began running node model.airbyte_utils.ticket_fields_custom_field_options_ab3
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.811197 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab3".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.811372 (Thread-3): Compiling model.airbyte_utils.ticket_fields_custom_field_options_ab3
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.831620 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_fields_custom_field_options_ab3"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.832830 (Thread-3): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.833300 (Thread-3): Finished running node model.airbyte_utils.ticket_fields_custom_field_options_ab3
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.833799 (Thread-3): Began running node model.airbyte_utils.ticket_fields_custom_field_options
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.834103 (Thread-3): 15:56:03 | 33 of 33 START incremental model zendesk_intercom.ticket_fields_custom_field_options......................... [RUN]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.834808 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.835017 (Thread-3): Compiling model.airbyte_utils.ticket_fields_custom_field_options
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.845343 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.845519 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.845668 (Thread-3): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.845789 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.898264 (Thread-3): SQL status: BEGIN in 0.05 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.898578 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:03.898711 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.087455 (Thread-2): SQL status: SELECT in 0.51 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.175702 (Thread-1): SQL status: SELECT in 0.47 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.177418 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.179532 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.179759 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.179970 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.180219 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.335696 (Thread-3): SQL status: SELECT in 0.44 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.347049 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_fields_custom_field_options"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.347509 (Thread-3): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.354711 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.354893 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [32mINFO[m i.a.w.t.TemporalAttemptExecution(lambda$getWorkerThread$2):158 - Completing future exceptionally...
io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:60) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at java.lang.Thread.run(Thread.java:833) [?:?]
Caused by: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:57) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 3 more
	Suppressed: io.airbyte.workers.WorkerException: Normalization process wasn't successful
		at io.airbyte.workers.normalization.DefaultNormalizationRunner.close(DefaultNormalizationRunner.java:159) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:45) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at java.lang.Thread.run(Thread.java:833) [?:?]
2022-04-18 15:56:16 [32mINFO[m i.a.w.t.TemporalAttemptExecution(get):131 - Stopping cancellation check scheduling...
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [32mINFO[m i.a.w.t.TemporalUtils(withBackgroundHeartbeat):234 - Stopping temporal heartbeating...
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.368233 (Thread-1): SQL status: COMMIT in 0.19 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.368971 (Thread-1): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.369176 (Thread-1): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.369973 (Thread-1): 15:56:04 | 28 of 33 OK created incremental model zendesk_intercom.ticket_metrics_requester_wait_time_in_minutes......... [INSERT 0 14778 in 4.75s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.370220 (Thread-1): Finished running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.420693 (Thread-4): SQL status: SELECT in 0.61 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.426159 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.426340 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields__dbt_tmp155603373710'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields__dbt_tmp155603373710'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields__dbt_tmp155603373710'
2022-04-18 15:56:16 [33mWARN[m i.t.i.s.POJOActivityTaskHandler(activityFailureToResult):363 - Activity failure. ActivityId=08ad36ef-ebe1-3fee-94ed-471c998785b1, activityType=Normalize, attempt=1
java.lang.RuntimeException: io.temporal.serviceclient.CheckedExceptionWrapper: java.util.concurrent.ExecutionException: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.temporal.TemporalUtils.withBackgroundHeartbeat(TemporalUtils.java:232) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.sync.NormalizationActivityImpl.normalize(NormalizationActivityImpl.java:71) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) ~[?:?]
	at jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77) ~[?:?]
	at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) ~[?:?]
	at java.lang.reflect.Method.invoke(Method.java:568) ~[?:?]
	at io.temporal.internal.sync.POJOActivityTaskHandler$POJOActivityInboundCallsInterceptor.execute(POJOActivityTaskHandler.java:286) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.sync.POJOActivityTaskHandler$POJOActivityImplementation.execute(POJOActivityTaskHandler.java:252) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.sync.POJOActivityTaskHandler.handle(POJOActivityTaskHandler.java:209) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.handle(ActivityWorker.java:193) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.handle(ActivityWorker.java:151) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.worker.PollTaskExecutor.lambda$process$0(PollTaskExecutor.java:73) ~[temporal-sdk-1.6.0.jar:?]
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) [?:?]
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) [?:?]
	at java.lang.Thread.run(Thread.java:833) [?:?]
Caused by: io.temporal.serviceclient.CheckedExceptionWrapper: java.util.concurrent.ExecutionException: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.temporal.serviceclient.CheckedExceptionWrapper.wrap(CheckedExceptionWrapper.java:56) ~[temporal-serviceclient-1.6.0.jar:?]
	at io.temporal.internal.sync.WorkflowInternal.wrap(WorkflowInternal.java:412) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.activity.Activity.wrap(Activity.java:51) ~[temporal-sdk-1.6.0.jar:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.get(TemporalAttemptExecution.java:135) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.sync.NormalizationActivityImpl.lambda$normalize$1(NormalizationActivityImpl.java:97) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalUtils.withBackgroundHeartbeat(TemporalUtils.java:227) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 14 more
Caused by: java.util.concurrent.ExecutionException: io.airbyte.workers.WorkerException: Normalization Failed.
	at java.util.concurrent.CompletableFuture.reportGet(CompletableFuture.java:396) ~[?:?]
	at java.util.concurrent.CompletableFuture.get(CompletableFuture.java:2073) ~[?:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.get(TemporalAttemptExecution.java:129) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.sync.NormalizationActivityImpl.lambda$normalize$1(NormalizationActivityImpl.java:97) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalUtils.withBackgroundHeartbeat(TemporalUtils.java:227) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 14 more
Caused by: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:60) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 1 more
Caused by: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:57) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 1 more
	Suppressed: io.airbyte.workers.WorkerException: Normalization process wasn't successful
		at io.airbyte.workers.normalization.DefaultNormalizationRunner.close(DefaultNormalizationRunner.java:159) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:45) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at java.lang.Thread.run(Thread.java:833) [?:?]
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.442477 (Thread-2): SQL status: SELECT in 0.26 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.445332 (Thread-2): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_metrics_status":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.448688 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.448847 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.721298 (Thread-3): SQL status: SELECT in 0.37 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.725844 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.726021 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   create temporary table
2022-04-18 15:56:16 [42mnormalization[0m >     "ticket_fields_custom_field_options__dbt_tmp155604351390"
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >       compound sortkey(_airbyte_emitted_at)
2022-04-18 15:56:16 [42mnormalization[0m >   as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > with __dbt__cte__ticket_fields_custom_field_options_ab1 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: "datalake".zendesk_intercom."ticket_fields_scd"
2022-04-18 15:56:16 [42mnormalization[0m > with numbers as (
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     with p as (
2022-04-18 15:56:16 [42mnormalization[0m >         select 0 as generated_number union all select 1
2022-04-18 15:56:16 [42mnormalization[0m >     ), unioned as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     p0.generated_number * power(2, 0)
2022-04-18 15:56:16 [42mnormalization[0m >      + 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     p1.generated_number * power(2, 1)
2022-04-18 15:56:16 [42mnormalization[0m >      + 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     p2.generated_number * power(2, 2)
2022-04-18 15:56:16 [42mnormalization[0m >      + 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     p3.generated_number * power(2, 3)
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     + 1
2022-04-18 15:56:16 [42mnormalization[0m >     as generated_number
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     p as p0
2022-04-18 15:56:16 [42mnormalization[0m >      cross join 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     p as p1
2022-04-18 15:56:16 [42mnormalization[0m >      cross join 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     p as p2
2022-04-18 15:56:16 [42mnormalization[0m >      cross join 
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     p as p3
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select *
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     where generated_number <= 16
2022-04-18 15:56:16 [42mnormalization[0m >     order by generated_number
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),
2022-04-18 15:56:16 [42mnormalization[0m > joined as (
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >         _airbyte_ticket_fields_hashid as _airbyte_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >         json_extract_array_element_text(custom_field_options, numbers.generated_number::int - 1, true) as _airbyte_nested_data
2022-04-18 15:56:16 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_fields_scd"
2022-04-18 15:56:16 [42mnormalization[0m >     cross join numbers
2022-04-18 15:56:16 [42mnormalization[0m >     -- only generate the number of records in the cross join that corresponds
2022-04-18 15:56:16 [42mnormalization[0m >     -- to the number of items in "datalake".zendesk_intercom."ticket_fields_scd".custom_field_options
2022-04-18 15:56:16 [42mnormalization[0m >     where numbers.generated_number <= json_array_length(custom_field_options, true)
2022-04-18 15:56:16 [42mnormalization[0m > )
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_fields_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'id', true) != '' then json_extract_path_text(_airbyte_nested_data, 'id', true) end as id,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'name', true) != '' then json_extract_path_text(_airbyte_nested_data, 'name', true) end as name,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'value', true) != '' then json_extract_path_text(_airbyte_nested_data, 'value', true) end as value,
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'default', true) != '' then json_extract_path_text(_airbyte_nested_data, 'default', true) end as "default",
2022-04-18 15:56:16 [42mnormalization[0m >     case when json_extract_path_text(_airbyte_nested_data, 'raw_name', true) != '' then json_extract_path_text(_airbyte_nested_data, 'raw_name', true) end as raw_name,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from "datalake".zendesk_intercom."ticket_fields_scd" as table_alias
2022-04-18 15:56:16 [42mnormalization[0m > -- custom_field_options at ticket_fields/custom_field_options
2022-04-18 15:56:16 [42mnormalization[0m > left join joined on _airbyte_ticket_fields_hashid = joined._airbyte_hashid
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > and custom_field_options is not null
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_fields_custom_field_options_ab2 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_fields_custom_field_options_ab1
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_fields_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(id as 
2022-04-18 15:56:16 [42mnormalization[0m >     bigint
2022-04-18 15:56:16 [42mnormalization[0m > ) as id,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(name as varchar) as name,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(value as varchar) as value,
2022-04-18 15:56:16 [42mnormalization[0m >     cast(decode("default", 'true', '1', 'false', '0')::integer as boolean) as "default",
2022-04-18 15:56:16 [42mnormalization[0m >     cast(raw_name as varchar) as raw_name,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_fields_custom_field_options_ab1
2022-04-18 15:56:16 [42mnormalization[0m > -- custom_field_options at ticket_fields/custom_field_options
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > ),  __dbt__cte__ticket_fields_custom_field_options_ab3 as (
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > -- SQL model to build a hash column based on the values of this record
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_fields_custom_field_options_ab2
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     md5(cast(coalesce(cast(_airbyte_ticket_fields_hashid as varchar), '') || '-' || coalesce(cast(id as varchar), '') || '-' || coalesce(cast(name as varchar), '') || '-' || coalesce(cast(value as varchar), '') || '-' || coalesce(cast(case when "default" then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(raw_name as varchar), '') as varchar)) as _airbyte_custom_field_options_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     tmp.*
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_fields_custom_field_options_ab2 tmp
2022-04-18 15:56:16 [42mnormalization[0m > -- custom_field_options at ticket_fields/custom_field_options
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > )-- Final base SQL model
2022-04-18 15:56:16 [42mnormalization[0m > -- depends_on: __dbt__cte__ticket_fields_custom_field_options_ab3
2022-04-18 15:56:16 [42mnormalization[0m > select
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ticket_fields_hashid,
2022-04-18 15:56:16 [42mnormalization[0m >     id,
2022-04-18 15:56:16 [42mnormalization[0m >     name,
2022-04-18 15:56:16 [42mnormalization[0m >     value,
2022-04-18 15:56:16 [42mnormalization[0m >     "default",
2022-04-18 15:56:16 [42mnormalization[0m >     raw_name,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_ab_id,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_emitted_at,
2022-04-18 15:56:16 [42mnormalization[0m >     getdate() as _airbyte_normalized_at,
2022-04-18 15:56:16 [42mnormalization[0m >     _airbyte_custom_field_options_hashid
2022-04-18 15:56:16 [42mnormalization[0m > from __dbt__cte__ticket_fields_custom_field_options_ab3
2022-04-18 15:56:16 [42mnormalization[0m > -- custom_field_options at ticket_fields/custom_field_options from "datalake".zendesk_intercom."ticket_fields_scd"
2022-04-18 15:56:16 [42mnormalization[0m > where 1 = 1
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m > and coalesce(
2022-04-18 15:56:16 [42mnormalization[0m >     cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-18 15:56:16 [42mnormalization[0m >     timestamp with time zone
2022-04-18 15:56:16 [42mnormalization[0m > )) from "datalake".zendesk_intercom."ticket_fields_custom_field_options"),
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     true)
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >   );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.758499 (Thread-4): SQL status: SELECT in 0.33 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.764243 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.764434 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.841456 (Thread-2): SQL status: SELECT in 0.39 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.843984 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_status"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.844456 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.844605 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_metrics_status" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_status_hashid")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_status_hashid"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_metrics_status__dbt_tmp155601921233"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.931854 (Thread-2): SQL status: INSERT 0 0 in 0.09 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.939252 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:04.939481 (Thread-2): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_metrics_status'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.157357 (Thread-4): SQL status: SELECT in 0.39 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.163352 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.163527 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields__dbt_tmp155603373710'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields__dbt_tmp155603373710'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields__dbt_tmp155603373710'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.349556 (Thread-2): SQL status: SELECT in 0.41 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.351989 (Thread-2): On model.airbyte_utils.ticket_metrics_status: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.352154 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.352311 (Thread-2): On model.airbyte_utils.ticket_metrics_status: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.429838 (Thread-2): SQL status: COMMIT in 0.08 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.430756 (Thread-2): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.430972 (Thread-2): On model.airbyte_utils.ticket_metrics_status: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.431811 (Thread-2): 15:56:05 | 29 of 33 OK created incremental model zendesk_intercom.ticket_metrics_status................................. [INSERT 0 0 in 4.22s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.432219 (Thread-2): Finished running node model.airbyte_utils.ticket_metrics_status
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.520761 (Thread-4): SQL status: SELECT in 0.36 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.526707 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.526871 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.766536 (Thread-4): SQL status: SELECT in 0.24 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.774487 (Thread-4): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_fields":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.777580 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:05.777740 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.026120 (Thread-4): SQL status: SELECT in 0.25 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.029620 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_fields"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.030068 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.030209 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       delete
2022-04-18 15:56:16 [42mnormalization[0m >     from "datalake".zendesk_intercom."ticket_fields"
2022-04-18 15:56:16 [42mnormalization[0m >     where (_airbyte_unique_key) in (
2022-04-18 15:56:16 [42mnormalization[0m >         select (_airbyte_unique_key)
2022-04-18 15:56:16 [42mnormalization[0m >         from "ticket_fields__dbt_tmp155603373710"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_fields" ("id", "tag", "url", "type", "title", "active", "position", "required", "raw_title", "removable", "created_at", "updated_at", "description", "sub_type_id", "raw_description", "title_in_portal", "agent_description", "visible_in_portal", "editable_in_portal", "required_in_portal", "raw_title_in_portal", "collapsed_for_agents", "custom_field_options", "system_field_options", "regexp_for_validation", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_fields_hashid", "_airbyte_unique_key")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "id", "tag", "url", "type", "title", "active", "position", "required", "raw_title", "removable", "created_at", "updated_at", "description", "sub_type_id", "raw_description", "title_in_portal", "agent_description", "visible_in_portal", "editable_in_portal", "required_in_portal", "raw_title_in_portal", "collapsed_for_agents", "custom_field_options", "system_field_options", "regexp_for_validation", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_fields_hashid", "_airbyte_unique_key"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_fields__dbt_tmp155603373710"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.627606 (Thread-4): SQL status: INSERT 0 14 in 0.60 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.632562 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.632730 (Thread-4): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.883332 (Thread-4): SQL status: SELECT in 0.25 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.886707 (Thread-4): On model.airbyte_utils.ticket_fields: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.886882 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.887021 (Thread-4): On model.airbyte_utils.ticket_fields: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.963439 (Thread-4): SQL status: COMMIT in 0.08 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.964368 (Thread-4): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.964594 (Thread-4): On model.airbyte_utils.ticket_fields: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.965470 (Thread-4): 15:56:06 | 32 of 33 OK created incremental model zendesk_intercom.ticket_fields......................................... [INSERT 0 14 in 4.33s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:06.965777 (Thread-4): Finished running node model.airbyte_utils.ticket_fields
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:09.859516 (Thread-3): SQL status: SELECT in 5.13 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:09.864109 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:09.864333 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields_custom_field_options__dbt_tmp155604351390'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields_custom_field_options__dbt_tmp155604351390'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields_custom_field_options__dbt_tmp155604351390'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:09.998066 (Thread-3): SQL status: SELECT in 0.13 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.003269 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.003475 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.139032 (Thread-3): SQL status: SELECT in 0.14 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.146958 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.147123 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields_custom_field_options__dbt_tmp155604351390'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields_custom_field_options__dbt_tmp155604351390'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'None'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields_custom_field_options__dbt_tmp155604351390'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.281598 (Thread-3): SQL status: SELECT in 0.13 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.286687 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.286848 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.429777 (Thread-3): SQL status: SELECT in 0.14 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.433373 (Thread-3): 
2022-04-18 15:56:16 [42mnormalization[0m >     In "datalake"."zendesk_intercom"."ticket_fields_custom_field_options":
2022-04-18 15:56:16 [42mnormalization[0m >         Schema changed: False
2022-04-18 15:56:16 [42mnormalization[0m >         Source columns not in target: []
2022-04-18 15:56:16 [42mnormalization[0m >         Target columns not in source: []
2022-04-18 15:56:16 [42mnormalization[0m >         New column types: []
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.436726 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.436898 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.579677 (Thread-3): SQL status: SELECT in 0.14 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.582431 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.ticket_fields_custom_field_options"
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.582854 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.582994 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     insert into "datalake".zendesk_intercom."ticket_fields_custom_field_options" ("_airbyte_ticket_fields_hashid", "id", "name", "value", "default", "raw_name", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_custom_field_options_hashid")
2022-04-18 15:56:16 [42mnormalization[0m >     (
2022-04-18 15:56:16 [42mnormalization[0m >        select "_airbyte_ticket_fields_hashid", "id", "name", "value", "default", "raw_name", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_custom_field_options_hashid"
2022-04-18 15:56:16 [42mnormalization[0m >        from "ticket_fields_custom_field_options__dbt_tmp155604351390"
2022-04-18 15:56:16 [42mnormalization[0m >     );
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.762875 (Thread-3): SQL status: INSERT 0 109 in 0.18 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.767777 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.767954 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       with bound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >         select
2022-04-18 15:56:16 [42mnormalization[0m >           ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >           table_schema,
2022-04-18 15:56:16 [42mnormalization[0m >           column_name,
2022-04-18 15:56:16 [42mnormalization[0m >           data_type,
2022-04-18 15:56:16 [42mnormalization[0m >           character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >           numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >         from information_schema."columns"
2022-04-18 15:56:16 [42mnormalization[0m >         where table_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unbound_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         ordinal_position,
2022-04-18 15:56:16 [42mnormalization[0m >         view_schema,
2022-04-18 15:56:16 [42mnormalization[0m >         col_name,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'character varying%' then
2022-04-18 15:56:16 [42mnormalization[0m >             'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else col_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as col_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'character%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when col_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >       from pg_get_late_binding_view_cols()
2022-04-18 15:56:16 [42mnormalization[0m >       cols(view_schema name, view_name name, col_name name,
2022-04-18 15:56:16 [42mnormalization[0m >            col_type varchar, ordinal_position int)
2022-04-18 15:56:16 [42mnormalization[0m >       where view_name = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     external_views as (
2022-04-18 15:56:16 [42mnormalization[0m >       select
2022-04-18 15:56:16 [42mnormalization[0m >         columnnum,
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname,
2022-04-18 15:56:16 [42mnormalization[0m >         columnname,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then 'character varying'
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type ilike 'numeric%' then 'numeric'
2022-04-18 15:56:16 [42mnormalization[0m >           else external_type
2022-04-18 15:56:16 [42mnormalization[0m >         end as external_type,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >         case
2022-04-18 15:56:16 [42mnormalization[0m >           when external_type like 'numeric%'
2022-04-18 15:56:16 [42mnormalization[0m >           then nullif(
2022-04-18 15:56:16 [42mnormalization[0m >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-18 15:56:16 [42mnormalization[0m >             '')::int
2022-04-18 15:56:16 [42mnormalization[0m >           else null
2022-04-18 15:56:16 [42mnormalization[0m >         end as numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m >       from
2022-04-18 15:56:16 [42mnormalization[0m >         pg_catalog.svv_external_columns
2022-04-18 15:56:16 [42mnormalization[0m >       where
2022-04-18 15:56:16 [42mnormalization[0m >         schemaname = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >         and tablename = 'ticket_fields_custom_field_options'
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     ),
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     unioned as (
2022-04-18 15:56:16 [42mnormalization[0m >       select * from bound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from unbound_views
2022-04-18 15:56:16 [42mnormalization[0m >       union all
2022-04-18 15:56:16 [42mnormalization[0m >       select * from external_views
2022-04-18 15:56:16 [42mnormalization[0m >     )
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     select
2022-04-18 15:56:16 [42mnormalization[0m >       column_name,
2022-04-18 15:56:16 [42mnormalization[0m >       data_type,
2022-04-18 15:56:16 [42mnormalization[0m >       character_maximum_length,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_precision,
2022-04-18 15:56:16 [42mnormalization[0m >       numeric_scale
2022-04-18 15:56:16 [42mnormalization[0m > 
2022-04-18 15:56:16 [42mnormalization[0m >     from unioned
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     where table_schema = 'zendesk_intercom'
2022-04-18 15:56:16 [42mnormalization[0m >     
2022-04-18 15:56:16 [42mnormalization[0m >     order by ordinal_position
2022-04-18 15:56:16 [42mnormalization[0m >   
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.905762 (Thread-3): SQL status: SELECT in 0.14 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.908490 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.908656 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:10.908785 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.035674 (Thread-3): SQL status: COMMIT in 0.13 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.036625 (Thread-3): finished collecting timing info
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.036850 (Thread-3): On model.airbyte_utils.ticket_fields_custom_field_options: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.037715 (Thread-3): 15:56:11 | 33 of 33 OK created incremental model zendesk_intercom.ticket_fields_custom_field_options.................... [INSERT 0 109 in 7.20s]
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.038162 (Thread-3): Finished running node model.airbyte_utils.ticket_fields_custom_field_options
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.040190 (MainThread): Acquiring new redshift connection "master".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.040474 (MainThread): Using redshift connection "master".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.040606 (MainThread): On master: BEGIN
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.040740 (MainThread): Opening a new connection, currently in state closed
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.040854 (MainThread): Connecting to Redshift using 'database' credentials
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.074628 (MainThread): SQL status: BEGIN in 0.03 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.074847 (MainThread): On master: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.074980 (MainThread): Using redshift connection "master".
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.075102 (MainThread): On master: COMMIT
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.077696 (MainThread): SQL status: COMMIT in 0.00 seconds
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.077869 (MainThread): On master: Close
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.078561 (MainThread): 15:56:11 | 
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.078859 (MainThread): 15:56:11 | Finished running 4 view models, 29 incremental models in 43.47s.
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.079091 (MainThread): Connection 'master' was properly closed.
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.079252 (MainThread): Connection 'model.airbyte_utils.ticket_metrics_status' was properly closed.
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.079446 (MainThread): Connection 'model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes' was properly closed.
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.079603 (MainThread): Connection 'model.airbyte_utils.ticket_fields_custom_field_options' was properly closed.
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.079711 (MainThread): Connection 'model.airbyte_utils.ticket_fields' was properly closed.
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.105418 (MainThread): 
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.105687 (MainThread): Completed with 1 error and 0 warnings:
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.105854 (MainThread): 
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.106059 (MainThread): Database Error in model tickets_scd (models/generated/airbyte_incremental/scd/zendesk_intercom/tickets_scd.sql)
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.106224 (MainThread):   column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.106359 (MainThread):   HINT:  You will need to rewrite or cast the expression.
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.106552 (MainThread): 
2022-04-18 15:56:16 [42mnormalization[0m > Done. PASS=25 WARN=0 ERROR=1 SKIP=7 TOTAL=33
2022-04-18 15:56:16 [42mnormalization[0m > 2022-04-18 15:56:11.106900 (MainThread): Flushing usage events