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https://issues.apache.org/jira/browse/SPARK-4737?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14238435#comment-14238435
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Apache Spark commented on SPARK-4737:
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User 'mccheah' has created a pull request for this issue:
https://github.com/apache/spark/pull/3638
> Prevent serialization errors from ever crashing the DAG scheduler
> -----------------------------------------------------------------
>
> Key: SPARK-4737
> URL: https://issues.apache.org/jira/browse/SPARK-4737
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.2.0
> Reporter: Patrick Wendell
> Assignee: Matthew Cheah
> Priority: Blocker
>
> Currently in Spark we assume that when tasks are serialized in the
> TaskSetManager that the serialization cannot fail. We assume this because
> upstream in the DAGScheduler we attempt to catch any serialization errors by
> serializing a single partition. However, in some cases this upstream test is
> not accurate - i.e. an RDD can have one partition that can serialize cleanly
> but not others.
> Do do this in the proper way we need to catch and propagate the exception at
> the time of serialization. The tricky bit is making sure it gets propagated
> in the right way.
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