Sergey Serebryakov created SPARK-21782:
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Summary: 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
*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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