Change:
epmetrics.DroppedRequests.WithContext(ctx).WithLabelValues(epmetrics.ReadOnlyKind).Inc() } epmetrics.RecordRequestTermination(r, requestInfo, epmetrics.APIServerComponent, http.StatusTooManyRequests) <del> if isWatchRequest { close(resultCh) } </del> tooManyRequests(r, w) } <del> // For watch requests, from the APF point of view the request is already // finished at this point. However, that doesn't mean it is already finished // from the non-APF point of view. So we need to wait here until the request is: // 1) finished being processed or // 2) rejected if isWatchRequest { if forgetWatch != nil { forgetWatch() } err := <-resultCh if err != nil { panic(err) } } </del> }) }

Discussion:
As visible in this graph: ! pod-startup time in 5k-node tests increased around beginning of June (only 99% though). It seems to be connected to "run-to-watch" phase. We should investigate and see what we can do with this. /sig scalability /kind bug
The label(s) cannot be applied, because the repository doesn't have them. <detailsIn response to : As visible in this graph: ! /kind bugs Instructions for interacting with me using PR comments are available . If you have questions or suggestions related to my behavior, please file an issue against the repository. </details
/triage accepted Once PR that merged around that time is P&F support for watch-initialitation: As a first step, we may want to confirm/reject if it's this PR and see if we aren't somehow misconfigured here.
I've briefly checked test-infra/perf-tests to see if we had any relevant changes around that time, but I don't see any. Most likely it's related to k/k changes.
/assign
Good news is that we can also see it regression in kubemark (although not as bad as in regular 5k tests): http://perf- But this can help us with finding root cause faster/cheaper. ! I've picked two runs, one before this change, one after: 26th May: Timeline: 12th June: Timeline: First run vs second run I can see significantly higher spike on waiting time in P&F, but according to timeline it seems like it was during huge service and not pod startup latency: 26th May: ! 12th June: ! I can see that number of apiserver goroutines significantly increased (from ~500k to ~900k). I would expect to have 1 more goroutine per watch, we have around 200k watches). ! vs ! Worst 100 pods with slowest latency runtowatch: I've picked the slowest pod "test-2xppkn-33/latency-deployment-60--p7nwz" to analyse.

This pod was bound to node gce-scale-cluster-minion-group-4-txww: Kubelet log for this pod: CL2 logs for this pod: In apiserver logs we can see: So it looks like updating pod status was delayed by P&F by ~500 ms for this pod. Timeline for this pod: 18:37:46. scheduled 18:37:48. container started 18:37:49. kubelet gets pod (1.3s latency with 610 ms delay from P&F) 18:37:50. patch status of pod (545ms latency with 500ms delay from P&F) 18:37:51. observed by CL2 as running To compare with previous run we have this timeline for pod "test-rcdjgd-26/latency-deployment-69--6s95x 2.724031741s": 18:57:08. scheduled on node gce-scale-cluster-minion-group-3-l21s 18:57:10. container started 18:57:10. kubelet gets pod (364 ms latency with 345 ms delay from P&F) 18:57:11. patched status od pod (360 ms latency with 254 ms delay from P&F) So to compare, for these two pods major difference was "get pod" + "patch status" in kubelet: 1.8s vs 700ms. 1s of 1.8s of latency was due to P&F and 0.6s of 0.7s was due to P&F.
In both runs I don't see significant waiting time (99th percentile) on any of Priority levels during pod startup latency phase: 26th May: ! 12th June: !
1) Do we have data about 100% waiting time in P&F? With 99% it my not be enough to to see that data. 2) If really waiting time is small, then the question is what exactly. (a) it might be related to the number of goroutines (b) it might be locking (c) it might be something else... I have just opened the WIP PR: that should help with first two (at least to some extent). Will you be able to test if that helps?

Max waiting time in P&F for pod startup latency phase: 26th May: ! 12th June: ! Buckets are ..., 0.2, 0.5, ... so I don't think it's significant. will test it.
This is definitely non-negligible regression that we should address in 1.22 timeframe. I'm actually have a promissing results for it. /milestone v1.22 /priority critical-urgent
OK - so we proved that addresses the problem: it reduces back to previous value the number of goroutines ! it brings back pod startup time back to less than 3.4s on 99th percentile it brings back API call latencies I'm going to work with reviewers on merging this to fix the regression. - FYI