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From "Xiao Li (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-17817) PySpark RDD Repartitioning Results in Highly Skewed Partition Sizes
Date Mon, 17 Oct 2016 07:38:59 GMT

     [ https://issues.apache.org/jira/browse/SPARK-17817?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Xiao Li updated SPARK-17817:
----------------------------
    Component/s: PySpark

> PySpark RDD Repartitioning Results in Highly Skewed Partition Sizes
> -------------------------------------------------------------------
>
>                 Key: SPARK-17817
>                 URL: https://issues.apache.org/jira/browse/SPARK-17817
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 1.6.1, 1.6.2, 2.0.0, 2.0.1
>            Reporter: Mike Dusenberry
>            Assignee: Liang-Chi Hsieh
>             Fix For: 2.1.0
>
>
> Calling {{repartition}} on a PySpark RDD to increase the number of partitions results
in highly skewed partition sizes, with most having 0 rows.  The {{repartition}} method should
evenly spread out the rows across the partitions, and this behavior is correctly seen on the
Scala side.
> Please reference the following code for a reproducible example of this issue:
> {code}
> # Python
> num_partitions = 20000
> a = sc.parallelize(range(int(1e6)), 2)  # start with 2 even partitions
> l = a.repartition(num_partitions).glom().map(len).collect()  # get length of each partition
> min(l), max(l), sum(l)/len(l), len(l)  # skewed!
> # Scala
> val numPartitions = 20000
> val a = sc.parallelize(0 until 1e6.toInt, 2)  # start with 2 even partitions
> val l = a.repartition(numPartitions).glom().map(_.length).collect()  # get length of
each partition
> print(l.min, l.max, l.sum/l.length, l.length)  # even!
> {code}
> The issue here is that highly skewed partitions can result in severe memory pressure
in subsequent steps of a processing pipeline, resulting in OOM errors.



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