Github user mccheah commented on a diff in the pull request: https://github.com/apache/spark/pull/3638#discussion_r22695144 --- Diff: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala --- @@ -865,26 +865,6 @@ class DAGScheduler( } if (tasks.size > 0) { - // Preemptively serialize a task to make sure it can be serialized. We are catching this - // exception here because it would be fairly hard to catch the non-serializable exception - // down the road, where we have several different implementations for local scheduler and - // cluster schedulers. - // - // We've already serialized RDDs and closures in taskBinary, but here we check for all other - // objects such as Partition. - try { - closureSerializer.serialize(tasks.head) - } catch { - case e: NotSerializableException => - abortStage(stage, "Task not serializable: " + e.toString) - runningStages -= stage - return - case NonFatal(e) => // Other exceptions, such as IllegalArgumentException from Kryo. - abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}") - runningStages -= stage - return - } - --- End diff -- This is the main addition in the patch - to make it so that task serialization error handling is only done when the serialization actually occurs. It turns out there are many scenarios where this selective sampling does not actually work. For example, when you create an RDD from an in-memory collection, perhaps some of the items are serializable but others are not. E.g. consider a list of containers, where the first item in the list is an empty container, and the second item in the list is a non-empty container with non-serializable things.
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