- 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
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.
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.
- Anyway to configure the system in the best possible way for full utilization of the machine ?
- What are the best practices for scaling Airbyte docker deployment to its full potential?
Hello there! You are receiving this message because none of your fellow community members has stepped in to respond to your topic post. (If you are a community member and you are reading this response, feel free to jump in if you have the answer!) As a result, the Community Assistance Team has been made aware of this topic and will be investigating and responding as quickly as possible.
Some important considerations that will help your to get your issue solved faster:
- It is best to use our topic creation template; if you haven’t yet, we recommend posting a followup with the requested information. With that information the team will be able to more quickly search for similar issues with connectors and the platform and troubleshoot more quickly your specific question or problem.
- Make sure to upload the complete log file; a common investigation roadblock is that sometimes the error for the issue happens well before the problem is surfaced to the user, and so having the tail of the log is less useful than having the whole log to scan through.
- Be as descriptive and specific as possible; when investigating it is extremely valuable to know what steps were taken to encounter the issue, what version of connector / platform / Java / Python / docker / k8s was used, etc. The more context supplied, the quicker the investigation can start on your topic and the faster we can drive towards an answer.
- We in the Community Assistance Team are glad you’ve made yourself part of our community, and we’ll do our best to answer your questions and resolve the problems as quickly as possible. Expect to hear from a specific team member as soon as possible.
Thank you for your time and attention.
The Community Assistance Team
Hey @AroraShreshth, have you seen this doc on scaling?
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!