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https://issues.apache.org/jira/browse/SPARK-45182?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot reassigned SPARK-45182:
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Assignee: Apache Spark
> Ignore task completion from old stage after retrying indeterminate stages
> -------------------------------------------------------------------------
>
> Key: SPARK-45182
> URL: https://issues.apache.org/jira/browse/SPARK-45182
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 3.3.2
> Reporter: Mayur Bhosale
> Assignee: Apache Spark
> Priority: Minor
> Labels: pull-request-available
>
> SPARK-25342 Added a support for rolling back shuffle map stage so that all
> tasks of the stage can be retried when the stage output is indeterminate.
> This is done by clearing all map outputs at the time of stage submission.
> This approach workouts well except for this case:
> Assume both Shuffle 1 and 2 are indeterminate
> ShuffleMapStage1 –{-}–{-}> Shuffle 1 ---–> ShuffleMapStage2 ----> Shuffle 2
> ----> ResultStage
> * ShuffleMapStage1 is complete
> * A task from ShuffleMapStage2 fails with FetchFailed. Other tasks are still
> running
> * Both ShuffleMapStage1 and ShuffleMapStage2 are retried
> * ShuffleMapStage1 is retried and completes
> * ShuffleMapStage2 reattempt is scheduled for execution
> * Before all tasks of ShuffleMapStage2 reattempt could finish, one/more
> laggard tasks from the original attempt of ShuffleMapStage2 finish and
> ShuffleMapStage2 also gets marked as complete
> * Result Stage gets scheduled and finishes
> Internally within Uber, we have been using the stage rollback functionality
> even for deterministic stages from Spark 2.4.3 to add fault tolerance from
> server going down in [remote shuffle service
> |https://github.com/uber/RemoteShuffleService]and have faced this scenario
> quite often
> Ideally, such laggard tasks should not be considered towards the partition
> completion.
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