You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Ranju (Jira)" <ji...@apache.org> on 2021/02/08 11:27:00 UTC

[jira] [Updated] (SPARK-34389) Spark job on Kubernetes scheduled For Zero or less than minimum number of executors and Wait indefinitely under resource starvation

     [ https://issues.apache.org/jira/browse/SPARK-34389?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ranju updated SPARK-34389:
--------------------------
    Attachment: Steps to reproduce.docx

> 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: 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 availableĀ 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.



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org