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Michael Armbrust commented on SPARK-4737: ----------------------------------------- I think another big problem here is that the DAGScheduler restarts (somewhat silently) and comes back in a bad state. Perhaps if the DAGScheduler crashes we should kill the whole process if we aren't actually resilient to restarts. > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org