AIrbyte version: 0.40.6
Deployed on: K8s
I am benchmarking airbyte w.r.t resources for achieving 50 parallel syncs. For that I have run some test cases to achieve 50parallel syncs. Based on that we can successfully run 50 parallel syncs this with 8vCPU and 16GB RAM.
But the problem arises when we see the resource limits/requests applied on the sync pods which are spun up for syncing a connection. For one connection sync, we get these pods:
By default, each of these pods contains 4 containers( 1. relay-stdout, 2. relay-stderr, 2. main, 3. init) and each container has the following resources limit/request quota configured. [ These are the default values]
At various points in the airbyte documentation, it is mentioned that we can tweak the sync pod size by changing the following environment variables in the deployment.
# Docker Resource Limits
My question is quite straightforward, on what basis we can infer their values, as we do not have visibility on how much data we will be transferring over one connection sync (as it depends on the user, in our use case)?
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