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https://issues.apache.org/jira/browse/YUNIKORN-2521?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17833750#comment-17833750
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Shravan Achar edited comment on YUNIKORN-2521 at 4/3/24 9:48 PM:
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Also seeing this in our deployment. Updated with Goroutine dumps. State dumps
are unavailable due to deadlock. Though attached goroutine dump while the state
dump API call was being blocked
was (Author: JIRAUSER303965):
Also seeing this in our deployment. Updated with Goroutine dumps. State dumps
are unavailable due to deadlock.
> Scheduler deadlock
> ------------------
>
> Key: YUNIKORN-2521
> URL: https://issues.apache.org/jira/browse/YUNIKORN-2521
> Project: Apache YuniKorn
> Issue Type: Bug
> Affects Versions: 1.5.0
> Environment: Yunikorn: 1.5
> AWS EKS: v1.28.6-eks-508b6b3
> Reporter: Noah Yoshida
> Priority: Major
> Attachments: goroutine-4-3-1.out, goroutine-4-3-2.out,
> goroutine-4-3-3.out, goroutine-4-3.out, goroutine-dump.txt,
> goroutine-while-blocking-2.out, goroutine-while-blocking.out, profile012.gif,
> profile013.gif
>
>
> Discussion on Yunikorn slack:
> [https://yunikornworkspace.slack.com/archives/CLNUW68MU/p1711048995187179]
> Occasionally, Yunikorn will deadlock and prevent any new pods from starting.
> All pods stay in Pending. There are no error logs inside of the Yunikorn
> scheduler indicating any issue.
> Additionally, the pods all have the correct annotations / labels from the
> admission service, so they are at least getting put into k8s correctly.
> The issue was seen intermittently on Yunikorn version 1.5 in EKS, using
> version `v1.28.6-eks-508b6b3`.
> At least for me, we run about 25-50 nodes and 200-400 pods. Pods and nodes
> are added and removed pretty frequently as we do ML workloads.
> Attached is the goroutine dump. We were not able to get a statedump as the
> endpoint kept timing out.
> You can fix it by restarting the Yunikorn scheduler pod. Sometimes you also
> have to delete any "Pending" pods that got stuck while the scheduler was
> deadlocked as well, for them to get picked up by the new scheduler pod.
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