S3 / CSV Incremental strategy

  • Is this your first time deploying Airbyte?: No
  • OS Version / Instance: Ubuntu
  • Memory / Disk: 4gb
  • Deployment: Elastic Beanstalk /w Docker
  • Airbyte Version: 40.1
  • Source name/version: S3/latest
  • Destination name/version: Postgres/latest
  • Step: Sync

I’m using the S3 connector with CSV files. This works perfectly when using the full refresh | overwrite strategy with the one problem that it wipes out any views I might have created (I assume because the refresh drops the table that the view is dependent on).

However, if I want to use one of the incremental strategies, it seems I’m unable to select the cursor field (it defaults to _ab_source_file_last_modified).

I have a created_at field which I think would be more suitable for the cursor but I’m also aware that might be misunderstanding something about the way the S3/CSV system works.

When I’ve used the incremental strategy with the _ab_source_file_last_modified I get a lot of duplicates, I suspect that this is due to me replacing the CSV on a periodic basis. Should I instead be saving increments of the dump and relying on the wildcard selector to pick these up?

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Thanks, looking forward to some feedback about how best to implement the S3/CSV pattern as it’s not clear from the documentation about how you are supposed to structure the files.

Bumping this. Some high level guidance around how we should be architecting the S3 / CSV connector would be welcome. Should we be thinking about incremental data dumps? How do others handle the need to retroactively change data without getting duplicates entries? Can we use a primary key in the CSV file?

Samuel today Airbyte will guarantees at-least-one record, which can cause duplicates. Some destination can use dbt to run the sync mode incremental mode + dedup history, but this is not possible to S3.