[jira] [Commented] (SPARK-21782) Repartition creates skews when numPartitions is a power of 2

["Apache Spark (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-21782?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17699875#comment-17699875 ] 

Apache Spark commented on SPARK-21782:
--------------------------------------

User 'megaserg' has created a pull request for this issue:
https://github.com/apache/spark/pull/18990

> Repartition creates skews when numPartitions is a power of 2
> ------------------------------------------------------------
>
>                 Key: SPARK-21782
>                 URL: https://issues.apache.org/jira/browse/SPARK-21782
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.2.0
>            Reporter: Sergey Serebryakov
>            Assignee: Sergey Serebryakov
>            Priority: Major
>              Labels: repartition
>             Fix For: 2.3.0
>
>         Attachments: Screen Shot 2017-08-16 at 3.40.01 PM.png
>
>
> *Problem:*
> When an RDD (particularly with a low item-per-partition ratio) is repartitioned to {{numPartitions}} = power of 2, the resulting partitions are very uneven-sized. This affects both {{repartition()}} and {{coalesce(shuffle=true)}}.
> *Steps to reproduce:*
> {code}
> $ spark-shell
> scala> sc.parallelize(0 until 1000, 250).repartition(64).glom().map(_.length).collect()
> res0: Array[Int] = Array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 144, 250, 250, 250, 106, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
> {code}
> *Explanation:*
> Currently, the [algorithm for repartition|https://github.com/apache/spark/blob/v2.2.0/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L450] (shuffle-enabled coalesce) is as follows:
> - for each initial partition {{index}}, generate {{position}} as {{(new Random(index)).nextInt(numPartitions)}}
> - then, for element number {{k}} in initial partition {{index}}, put it in the new partition {{position + k}} (modulo {{numPartitions}}).
> So, essentially elements are smeared roughly equally over {{numPartitions}} buckets - starting from the one with number {{position+1}}.
> Note that a new instance of {{Random}} is created for every initial partition {{index}}, with a fixed seed {{index}}, and then discarded. So the {{position}} is deterministic for every {{index}} for any RDD in the world. Also, [{{nextInt(bound)}} implementation|http://grepcode.com/file/repository.grepcode.com/java/root/jdk/openjdk/8u40-b25/java/util/Random.java/#393] has a special case when {{bound}} is a power of 2, which is basically taking several highest bits from the initial seed, with only a minimal scrambling.
> Due to deterministic seed, using the generator only once, and lack of scrambling, the {{position}} values for power-of-two {{numPartitions}} always end up being almost the same regardless of the {{index}}, causing some buckets to be much more popular than others. So, {{repartition}} will in fact intentionally produce skewed partitions even when before the partition were roughly equal in size.
> The behavior seems to have been introduced in SPARK-1770 by https://github.com/apache/spark/pull/727/
> {quote}
> The load balancing is not perfect: a given output partition
> can have up to N more elements than the average if there are N input
> partitions. However, some randomization is used to minimize the
> probabiliy that this happens.
> {quote}
> Another related ticket: SPARK-17817 - https://github.com/apache/spark/pull/15445



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