[jira] [Created] (SPARK-23053) taskBinarySerialization and task partitions calculate in DagScheduler.submitMissingTasks should keep the same RDD checkpoint status

["huangtengfei (JIRA)" <ji...@apache.org>]

huangtengfei created SPARK-23053:
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             Summary: 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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