[ https://issues.apache.org/jira/browse/SPARK-21782?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sergey Serebryakov updated SPARK-21782: --------------------------------------- Attachment: Screen Shot 2017-08-16 at 3.40.01 PM.png Distribution of partition sizes (in bytes) spotted in the wild. Horizontal axis: partition index ({{0..1023}}). Vertical axis: partition size in bytes. > 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 -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org