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[SPARK-2403] Catch all errors during serialization in DAGScheduler #1329

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[SPARK-2403] Catch all errors during serialization in DAGScheduler #1329

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361e962
Catch all errors during serialization in DAGScheduler.
darabos Jul 8, 2014
52c22ba
Only catch NonFatal exceptions.
darabos Jul 8, 2014
3aceaad
Print full stack trace for miscellaneous exceptions during serializat…
darabos Jul 8, 2014
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4 changes: 4 additions & 0 deletions core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
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Expand Up @@ -768,6 +768,10 @@ class DAGScheduler(
abortStage(stage, "Task not serializable: " + e.toString)
runningStages -= stage
return
case e: Throwable => // Other exceptions, such as IllegalArgumentException from Kryo.
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Please catch NonFatal(e) instead. I think we should catch StackOverflowError here (as that is a possible error during serialization), but we should not catch OOMs and other such throwables except to re-throw them.

NB: Despite what the documentation says, NonFatal does indeed seem to catch StackOverflowError:

scala> NonFatal(new StackOverflowError())
res1: Boolean = true

scala> NonFatal(new OutOfMemoryError())
res2: Boolean = false

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I suspect you are testing this on 2.10. Looks like a change in 2.11:

scala/scala@6460365#diff-ff42321ce198f97308744271b7e17c76

I think their argument applies to Spark too. Sounds like it is not safe to try and recover from StackOverflowError.

Thanks for the comments! I'll update the pull request in a moment.

abortStage(stage, "Task serialization failed: " + e.toString)
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In the prior case, of NotSerializableException, the error message was likely sufficient. However, since we're dealing with arbitrary exceptions here, the user may need to actually see the full stack trace to understand what's going on. Maybe something like:

abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}")

runningStages -= stage
return
}

logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
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