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https://issues.apache.org/jira/browse/SPARK-23053?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16323541#comment-16323541
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Apache Spark commented on SPARK-23053:
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User 'ivoson' has created a pull request for this issue:
https://github.com/apache/spark/pull/20244
> 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: Spark Core
> Affects Versions: 2.1.0
> Reporter: huangtengfei
>
> 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.
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