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https://issues.apache.org/jira/browse/SPARK-34389?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17281645#comment-17281645
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Attila Zsolt Piros commented on SPARK-34389:
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> is it possible to get the available resources of the cluster and match it
> with the required executor resources and if it satisfies then submits the job
[~ranju] This is out of the scope of Spark. A quick search gave me this:
https://github.com/kubernetes/kubernetes/issues/17512. You can try out the
suggestions and if you find one good enough for your case you can use it in a
script which could start Spark when the resources are available.
> Spark job on Kubernetes scheduled For Zero or less than minimum number of
> executors and Wait indefinitely under resource starvation
> -----------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-34389
> URL: https://issues.apache.org/jira/browse/SPARK-34389
> Project: Spark
> Issue Type: Bug
> Components: Kubernetes
> Affects Versions: 3.0.1
> Reporter: Ranju
> Priority: Major
> Attachments: DriverLogs_ExecutorLaunchedLessThanMinExecutor.txt,
> Steps to reproduce.docx
>
>
> In case Cluster does not have sufficient resource (CPU/ Memory ) for minimum
> number of executors , the executors goes in Pending State for indefinite time
> until the resource gets free.
> Suppose, Cluster Configurations are:
> total Memory=204Gi
> used Memory=200Gi
> free memory= 4Gi
> SPARK.EXECUTOR.MEMORY=10G
> SPARK.DYNAMICALLOCTION.MINEXECUTORS=4
> SPARK.DYNAMICALLOCATION.MAXEXECUTORS=8
> Rather, the job should be cancelled if requested number of minimum executors
> are not available at that point of time because of resource unavailability.
> Currently it is doing partial scheduling or no scheduling and waiting
> indefinitely. And the job got stuck.
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