[ https://issues.apache.org/jira/browse/SPARK-4737?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14236494#comment-14236494 ]
Matt Cheah commented on SPARK-4737: ----------------------------------- Forgot to mention that I'm actively working on this now - you can mark as in progress. https://github.com/mccheah/spark/commit/097e7a21e15d3adf45687bd58ff095088f0282f7 <-- this will become a pull request once I get some of my internal mentors to review it. > 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