Scaling Issues while syncing BigQuery to Clickhouse

Platform, Deploy & Infra Issues
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  • Is this your first time deploying Airbyte?: Yes
  • OS Version / Instance: Ubuntu
  • Memory / Disk: 32Gb / 100Gb
  • Deployment: Docker
  • Airbyte Version: 0.40.28
  • Source name/version: BigQuery
  • Destination name/version: Clickhouse
  • Step: Sync Step
  • Description:

We are having scaling Issues while syncing BigQuery to Clickhouse. We are using Airbyte in Docker Containers on 1 x c2d-standard-32 GCP Machine (8vCPU , 32GB Memory) and have made multiple attempts to tune the parameters for faster BigQuery to Clickhouse loads but even after increasing config Variables there seems to be no change in the system.

JOB_MAIN_CONTAINER_MEMORY_REQUEST=12g
JOB_MAIN_CONTAINER_MEMORY_LIMIT= 12g
JOB_MAIN_CONTAINER_CPU_REQUEST= 0.75
JOB_MAIN_CONTAINER_CPU_LIMIT= 0.80

MAX_SYNC_WORKERS=20
MAX_SPEC_WORKERS=20
MAX_CHECK_WORKERS=20
MAX_DISCOVER_WORKERS=20
MAX_NOTIFY_WORKERS=20

Similar Configs for Normalisation Container Variables are also in place. We have run multiple syncs of multiple sizes but haven’t been able to increase the ETA of sync and is constant. Usage of the machine from airbyte to isn’t more than 40% of CPU and 4GBs of RAM (out of 32GB).
With current test sync speeds Airbyte would take days to sync data for current need of BigQuery to Clickhouse.

  1. Anyway to configure the system in the best possible way for full utilization of the machine ?
  2. What are the best practices for scaling Airbyte docker deployment to its full potential?
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Hey @AroraShreshth, have you seen this doc on scaling?
https://docs.airbyte.com/operator-guides/scaling-airbyte

I know that one of our focuses this quarter is speed, I will look into what else you can do - but if you follow the doc and are not able to improve the performance by much, it might be what it is for now until we implement improvements!