practo/k8s-worker-pod-autoscaler

Do we consider the readiness time of the pod while scaling up?

jotish opened this issue · 5 comments

Do we consider the readiness time of the pod while scaling up?

@jotish can you please elaborate more in the description. Not very clear. Lost the context.

I think I have also experienced this.
when a pod taking a lot of time until ready, the auto-scaler "thinks" that the pod already consuming jobs, although its not the case.
this causes wrong scaling problems.
I've reported an issue here:
#101

Currently the scaling happens based on available replicas. f00ef3f in #105 changes this behavior so scaling happens based on spec replicas which includes both available and not ready replicas so that it doesn't scale up beyond the target.

Closed #102 as #105 fixes this problem

Pods no longer scale up on every tick(default 20seconds). It all depends on how many workers are needed to consumer the backlog(available)+reserved(inprocess) messages and the queue RPM. Closing this issue, as it solved now. @jotish