[jira] [Commented] (HIVE-7540) NotSerializableException encountered when using sortByKey transformation

["Brock Noland (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/HIVE-7540?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14085539#comment-14085539 ] 

Brock Noland commented on HIVE-7540:
------------------------------------

Hi [~lirui],

It seems this related to serializing a closure as opposed to data. I talked with [~sandyr] and it seems that using Writable serialization is probably difficult/impossible. It looks like the Spark folks have also tried using Kryo to [serialize closures|http://mail-archives.apache.org/mod_mbox/spark-dev/201405.mbox/%3CCAPh_B=Z3mNZp4A=B3M9TnQqe6n+foB6fQSmLEkVwB+mUbb=Akg@mail.gmail.com%3E].

Can you try creating a class HiveBytesWritable which extends from BytesWritable and implements Serializable and then transform the objects into that class before the soryByKey?


> 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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