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https://issues.apache.org/jira/browse/SPARK-22665?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16276140#comment-16276140
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Apache Spark commented on SPARK-22665:
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User 'mgaido91' has created a pull request for this issue:
https://github.com/apache/spark/pull/19870
> Dataset API: .repartition() inconsistency / issue
> -------------------------------------------------
>
> Key: SPARK-22665
> URL: https://issues.apache.org/jira/browse/SPARK-22665
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Adrian Ionescu
>
> We currently have two functions for explicitly repartitioning a Dataset:
> {code}
> def repartition(numPartitions: Int)
> {code}
> and
> {code}
> def repartition(numPartitions: Int, partitionExprs: Column*)
> {code}
> The second function's signature allows it to be called with an empty list of
> expressions as well.
> However:
> * {{df.repartition(numPartitions)}} does RoundRobin partitioning
> * {{df.repartition(numPartitions, Seq.empty: _*)}} does HashPartitioning on a
> constant, effectively moving all tuples to a single partition
> Not only is this inconsistent, but the latter behavior is very undesirable:
> it may hide problems in small-scale prototype code, but will inevitably fail
> (or have terrible performance) in production.
> I suggest we should make it:
> - either throw an {{IllegalArgumentException}}
> - or do RoundRobin partitioning, just like {{df.repartition(numPartitions)}}
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