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https://issues.apache.org/jira/browse/SPARK-22276?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16205400#comment-16205400
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Liang-Chi Hsieh commented on SPARK-22276:
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Can you provide an simple example to reproduce this issue? Then it is easier to
verify the problem.
> Unnecessary repartitioning
> --------------------------
>
> Key: SPARK-22276
> URL: https://issues.apache.org/jira/browse/SPARK-22276
> Project: Spark
> Issue Type: Bug
> Components: Optimizer
> Affects Versions: 2.2.0
> Reporter: Fernando Pereira
>
> When a dataframe is sorted it is partitioned with a RangePartitioner.
> If later we aggregate by the exact same fields over which sort was applied
> there is a new (apparently useless) Exchange repartitioning by a
> HashPartitioner.
> In my use case the groupBy exchange is still very costly as the aggregate
> function won't reduce the data volume.
> Is there any reason why groupBy always shuffles data, or could this be
> improved?
> Is there currently a way to workaround for the moment, without going to
> mapPartitions?
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