Benchmark sync job resource limits and requests

Q&A
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1

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:

  • SOURCE-CHECK
  • SOURCE-READ
  • DESTINATION-CHECK
  • DESTINATION-WRITE

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]

   resources:
      limits:
        cpu: 200m
        memory: 50Mi
      requests:
        cpu: 100m
        memory: 25Mi

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
JOB_MAIN_CONTAINER_CPU_REQUEST=
JOB_MAIN_CONTAINER_CPU_LIMIT=
JOB_MAIN_CONTAINER_MEMORY_REQUEST=
JOB_MAIN_CONTAINER_MEMORY_LIMIT=

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)?

2

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