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https://issues.apache.org/jira/browse/SPARK-6606?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14481639#comment-14481639
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Apache Spark commented on SPARK-6606:
-------------------------------------
User 'kayousterhout' has created a pull request for this issue:
https://github.com/apache/spark/pull/4145
> Accumulator deserialized twice because the NarrowCoGroupSplitDep contains rdd
> object.
> -------------------------------------------------------------------------------------
>
> Key: SPARK-6606
> URL: https://issues.apache.org/jira/browse/SPARK-6606
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.2.0, 1.3.0
> Reporter: SuYan
>
> 1. Use code like belows, will found accumulator deserialized twice.
> first:
> {code}
> task = ser.deserialize[Task[Any]](taskBytes,
> Thread.currentThread.getContextClassLoader)
> {code}
> second:
> {code}
> val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])](
> ByteBuffer.wrap(taskBinary.value),
> Thread.currentThread.getContextClassLoader)
> {code}
> which the first deserialized is not what expected.
> because ResultTask or ShuffleMapTask will have a partition object.
> in class
> {code}
> CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
> Partitioner)
> {code}, the CogroupPartition may contains a CoGroupDep:
> {code}
> NarrowCoGroupSplitDep(
> rdd: RDD[_],
> splitIndex: Int,
> var split: Partition
> ) extends CoGroupSplitDep {
> {code}
> in that *NarrowCoGroupSplitDep*, it will bring into rdd object, which result
> into the first deserialized.
> example:
> {code}
> val acc1 = sc.accumulator(0, "test1")
> val acc2 = sc.accumulator(0, "test2")
> val rdd1 = sc.parallelize((1 to 10).toSeq, 3)
> val rdd2 = sc.parallelize((1 to 10).toSeq, 3)
> val combine1 = rdd1.map { case a => (a, 1)}.combineByKey(a => {
> acc1 += 1
> a
> }, (a: Int, b: Int) => {
> a + b
> },
> (a: Int, b: Int) => {
> a + b
> }, new HashPartitioner(3), mapSideCombine = false)
> val combine2 = rdd2.map { case a => (a, 1)}.combineByKey(
> a => {
> acc2 += 1
> a
> },
> (a: Int, b: Int) => {
> a + b
> },
> (a: Int, b: Int) => {
> a + b
> }, new HashPartitioner(3), mapSideCombine = false)
> combine1.cogroup(combine2, new HashPartitioner(3)).count()
> {code}
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