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https://issues.apache.org/jira/browse/HIVE-7540?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14085755#comment-14085755
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Sandy Ryza commented on HIVE-7540:
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There are two main cases where serialization occurs in Spark
* Serializing each row for shuffling or caching
* Serializing the functions and data required to execute a task to send from
the driver to the executors
My understanding is that the error here is caused by serialization in the
latter, which has minimal performance impact. For per-row serialization, I
agree that using Writable serialization is a worthy goal. Serializing
Writables in task closures is both much more difficult and will have minimal
performance benefit.
> 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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