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https://issues.apache.org/jira/browse/SPARK-17817?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-17817:
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Assignee: Apache Spark
> 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
> Affects Versions: 1.6.1, 1.6.2, 2.0.0, 2.0.1
> Reporter: Mike Dusenberry
> Assignee: Apache Spark
>
> 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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