Imran Rashid updated SPARK-23053:
    Fix Version/s: 2.4.0

> 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
>            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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