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https://issues.apache.org/jira/browse/SPARK-23053?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Imran Rashid resolved SPARK-23053.
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Resolution: Fixed
> taskBinarySerialization and task partitions calculate in
> DagScheduler.submitMissingTasks should keep the same RDD checkpoint status
> -----------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-23053
> URL: https://issues.apache.org/jira/browse/SPARK-23053
> Project: Spark
> Issue Type: Bug
> Components: Scheduler, Spark Core
> Affects Versions: 2.1.0
> Reporter: huangtengfei
> Assignee: huangtengfei
> Priority: Major
> Fix For: 2.2.2, 2.3.1, 2.4.0
>
>
> When we run concurrent jobs using the same rdd which is marked to do
> checkpoint. If one job has finished running the job, and start the process of
> RDD.doCheckpoint, while another job is submitted, then submitStage and
> submitMissingTasks will be called. In
> [submitMissingTasks|https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L961],
> will serialize taskBinaryBytes and calculate task partitions which are both
> affected by the status of checkpoint, if the former is calculated before
> doCheckpoint finished, while the latter is calculated after doCheckpoint
> finished, when run task, rdd.compute will be called, for some rdds with
> particular partition type such as
> [MapWithStateRDD|https://github.com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/rdd/MapWithStateRDD.scala]
> who will do partition type cast, will get a ClassCastException because the
> part params is actually a CheckpointRDDPartition.
> This error occurs because rdd.doCheckpoint occurs in the same thread that
> called sc.runJob, while the task serialization occurs in the DAGSchedulers
> event loop.
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