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https://issues.apache.org/jira/browse/FLINK-26351?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Flink Jira Bot updated FLINK-26351:
-----------------------------------
Labels: pull-request-available stale-major (was: pull-request-available)
I am the [Flink Jira Bot|https://github.com/apache/flink-jira-bot/] and I help
the community manage its development. I see this issues has been marked as
Major but is unassigned and neither itself nor its Sub-Tasks have been updated
for 60 days. I have gone ahead and added a "stale-major" to the issue". If this
ticket is a Major, please either assign yourself or give an update. Afterwards,
please remove the label or in 7 days the issue will be deprioritized.
> After scaling a flink task running on k8s, the flink web ui graph always
> shows the parallelism of the first deployment.
> -----------------------------------------------------------------------------------------------------------------------
>
> Key: FLINK-26351
> URL: https://issues.apache.org/jira/browse/FLINK-26351
> Project: Flink
> Issue Type: Bug
> Components: API / Core
> Affects Versions: 1.15.0
> Reporter: qiunan
> Priority: Major
> Labels: pull-request-available, stale-major
>
> In the code，flink web ui graph data from under method.
> AdaptiveScheduler.requestJob()
> {code:java}
> @Override
> public ExecutionGraphInfo requestJob() {
> return new ExecutionGraphInfo(state.getJob(),
> exceptionHistory.toArrayList());
> } {code}
> This executionGraphInfo is task restart build and restore to state.
> You can see the code, the parallelism recalculate and copy jobGraph to reset.
> AdaptiveScheduler.createExecutionGraphWithAvailableResourcesAsync().
> {code:java}
> vertexParallelism = determineParallelism(slotAllocator);
> JobGraph adjustedJobGraph = jobInformation.copyJobGraph();
> for (JobVertex vertex : adjustedJobGraph.getVertices()) {
> JobVertexID id = vertex.getID();
> // use the determined "available parallelism" to use
> // the resources we have access to
> vertex.setParallelism(vertexParallelism.getParallelism(id));
> }{code}
> But in the restoreState copy jobGraph again, so the jobGraph parallelism
> always deployed for the first time.
> AdaptiveScheduler.createExecutionGraphAndRestoreState(VertexParallelismStore
> adjustedParallelismStore)
> {code:java}
> private ExecutionGraph createExecutionGraphAndRestoreState(
> VertexParallelismStore adjustedParallelismStore) throws Exception {
> return executionGraphFactory.createAndRestoreExecutionGraph(
> jobInformation.copyJobGraph(),
> completedCheckpointStore,
> checkpointsCleaner,
> checkpointIdCounter,
>
> TaskDeploymentDescriptorFactory.PartitionLocationConstraint.MUST_BE_KNOWN,
> initializationTimestamp,
> vertexAttemptNumberStore,
> adjustedParallelismStore,
> deploymentTimeMetrics,
> LOG);
> } {code}
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