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From "Sergey Serebryakov (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-21782) Repartition creates skews when numPartitions is a power of 2
Date Fri, 18 Aug 2017 06:31:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-21782?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16131794#comment-16131794
] 

Sergey Serebryakov commented on SPARK-21782:
--------------------------------------------

Your understanding is correct. Either reusing the same {{Random}} instance multiple times
(not really an option as shuffle is parallel), using a better RNG, or substantially scrambling
the seed (hashing?) will help.
Changing the "smearing" algorithm would also work, e.g. to something like this:
{code}
      val distributePartition = (index: Int, items: Iterator[T]) => {
         val rng = new Random(index)
         items.map { t => (rng.nextInt(numPartitions), t) }
       } : Iterator[(Int, T)]
{code}

Please let me know which way you'd like to see it.

> 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
>              Labels: repartition
>         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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