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https://issues.apache.org/jira/browse/HIVE-7540?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14085821#comment-14085821
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Brock Noland commented on HIVE-7540:
------------------------------------
Thanks Sandy. For the specific RangePartitioner case, I think we could use
writeObject/readObject which are [optional
methods|http://docs.oracle.com/javase/7/docs/api/java/io/Serializable.html]
Serializable's can implement.
If the number of cases where we need to serialize a writable via java
serialization is small, then providing point solutions using
writeObject/readObject in RangePartitioner might be a reasonable fix. If anyone
has a feeling for the number of times we will end up hitting this, please speak
up. In the absence of more information, I would suggest we work around this
issue on the Hive side and then once we've gathered more information (how many
times we need to serialize a writable in a closure, performance impact, etc) we
can decide on how to proceed.
> NotSerializableException encountered when using sortByKey transformation
> ------------------------------------------------------------------------
>
> Key: HIVE-7540
> URL: https://issues.apache.org/jira/browse/HIVE-7540
> Project: Hive
> Issue Type: Bug
> Components: Spark
> Environment: Spark-1.0.1
> Reporter: Rui Li
>
> This exception is thrown when sortByKey is used as the shuffle transformation
> between MapWork and ReduceWork:
> {quote}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task not
> serializable: java.io.NotSerializableException:
> org.apache.hadoop.io.BytesWritable
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1049)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1033)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1031)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1031)
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:772)
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:715)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$submitStage$4.apply(DAGScheduler.scala:719)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$submitStage$4.apply(DAGScheduler.scala:718)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:718)
> at
> org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:699)
> …
> {quote}
> The root cause is that the RangePartitioner used by sortByKey contains
> rangeBounds: Array[BytesWritable], which is considered not serializable in
> spark.
> A workaround to this issue is to set the number of partitions to 1 when
> calling sortByKey, in which case the rangeBounds will be just an empty array.
> NO PRECOMMIT TESTS. This is for spark branch only.
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