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https://issues.apache.org/jira/browse/FLINK-34451?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17819795#comment-17819795
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Gyula Fora edited comment on FLINK-34451 at 2/22/24 8:12 PM:
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I did not mean to turn off HA but only to reduce the JM replicas to 1
was (Author: gyfora):
I did not mean to turn off HA but only to reduce the replicas to 1
> [Kubernetes Operator] Job with restarting TaskManagers uses wrong/misleading
> fallback approach
> ----------------------------------------------------------------------------------------------
>
> Key: FLINK-34451
> URL: https://issues.apache.org/jira/browse/FLINK-34451
> Project: Flink
> Issue Type: Bug
> Components: Kubernetes Operator
> Affects Versions: kubernetes-operator-1.6.1
> Environment: Operator version: 1.6.1
> Flink version 1.18.0
> HA JobManagers
> Adaptive scheduler mode using the operator's autoscaler
> Checkpointing at an interval of 60s
> Upgrade mode savepoint
> Reporter: Alex Hoffer
> Priority: Major
>
>
> We had a situation where TaskManagers were constantly restarting from OOM.
> We're using the Adaptive scheduler with the Kubernetes Operator, and a
> restart strategy of exponential backoff, and so the JobManagers remained
> alive. We're also using savepoint upgrade mode.
> When we tried to remedy the situation by raising the direct memory allocation
> to the pods, we were surprised that Flink used the last savepoint taken,
> rather than the checkpoint. This was unfortunate for us because we are on
> adaptive scheduler and the job hasn't changed in some time, so this last
> savepoint was 6 days old! Meanwhile, checkpoints were taken every minute up
> until failure. I can confirm the HA metadata existed in the configmaps, and
> the corresponding checkpoints existed in remote storage for it to access.
> Plus, no Flink version changes were in the deployment.
> The Operator logs reported that it was using last-state recovery in this
> situation:
> {code:java}
> 2024-02-15 19:38:38,252 o.a.f.k.o.l.AuditUtils [INFO ][job-name] >>>
> Event | Info | SPECCHANGED | UPGRADE change(s) detected (Diff:
> FlinkDeploymentSpec[image : image:0a7c41b -> image:ebebc53, restartNonce :
> null -> 100]), starting reconciliation.
> 2024-02-15 19:38:38,252 o.a.f.k.o.r.d.AbstractJobReconciler [INFO ][job-name]
> Upgrading/Restarting running job, suspending first...
> 2024-02-15 19:38:38,260 o.a.f.k.o.r.d.ApplicationReconciler [INFO ][job-name]
> Job is not running but HA metadata is available for last state restore, ready
> for upgrade
> 2024-02-15 19:38:38,270 o.a.f.k.o.l.AuditUtils [INFO ][job-name] >>>
> Event | Info | SUSPENDED | Suspending existing deployment.
> 2024-02-15 19:38:38,270 o.a.f.k.o.s.NativeFlinkService [INFO ][job-name]
> Deleting JobManager deployment while preserving HA metadata.
> 2024-02-15 19:38:40,431 o.a.f.k.o.l.AuditUtils [INFO ][job-name] >>>
> Status | Info | UPGRADING | The resource is being upgraded
> 2024-02-15 19:38:40,532 o.a.f.k.o.l.AuditUtils [INFO ][job-name] >>>
> Event | Info | SUBMIT | Starting deployment
> 2024-02-15 19:38:40,532 o.a.f.k.o.s.AbstractFlinkService [INFO ][job-name]
> Deploying application cluster requiring last-state from HA metadata
> 2024-02-15 19:38:40,538 o.a.f.k.o.u.FlinkUtils [INFO ][job-name] Job
> graph in ConfigMap job-name-cluster-config-map is deleted {code}
> But when the job booted up, it reported restoring from savepoint:
> {code:java}
> 2024-02-15 19:39:03,887 INFO
> org.apache.flink.runtime.checkpoint.CheckpointCoordinator [] - Restoring
> job 522b3c363499d81ed7922aa30b13e237 from Savepoint 20207 @ 0 for
> 522b3c363499d81ed7922aa30b13e237 located at
> abfss://[email protected]/job-name/savepoint-522b3c-8836a1edc709.
> {code}
> Our expectation was that the Operator logs were true, and that it would be
> restoring from checkpoint. We had to scramble and manually restore from the
> checkpoint to restore function.
>
>
> It's also worth noting I can recreate this issue in a testing environment.
> The process for doing so is:
> - Boot up HA JobManagers with checkpoints on and savepoint upgrade mode,
> using adaptive scheduler
> - Make a dummy change to trigger a savepoint.
> - Allow the TaskManagers to process some data and hit the checkpoint interval.
> - Cause the TaskManagers to crash. In our case, we could use up a bunch of
> memory in the pods and cause it to crash.
> - Observe the Operator logs saying it is restoring from last-state, but watch
> as the pods instead use the last savepoint.
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