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https://issues.apache.org/jira/browse/SPARK-22276?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16205655#comment-16205655
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Fernando Pereira commented on SPARK-22276:
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I added a simple example that shows what I mean.
You see two exchanges, even though data is already partitioned according to the
same field. I wonder if the Exchange hashpartitioning is really needed, since
in my case it just slows things down.
Thanks
> 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?
> Example
> {code}
> nrn_vals.printSchema()
> (nrn_vals
> .sort("post_gid")
> .groupBy("post_gid")
> .agg(F.collect_list("pre_gid").alias("pre_gids"))
> ).explain()
> {code}
> Outputs the following
> {code}
> root
> |-- pre_gid: integer (nullable = true)
> |-- post_gid: integer (nullable = true)
> |-- floatvec: array (nullable = false)
> | |-- element: float (containsNull = true)
> == Physical Plan ==
> ObjectHashAggregate(keys=[post_gid#1386],
> functions=[collect_list(pre_gid#1385, 0, 0)])
> +- Exchange hashpartitioning(post_gid#1386, 1)
> +- ObjectHashAggregate(keys=[post_gid#1386],
> functions=[partial_collect_list(pre_gid#1385, 0, 0)])
> +- *Sort [post_gid#1386 ASC NULLS FIRST], true, 0
> +- Exchange rangepartitioning(post_gid#1386 ASC NULLS FIRST, 1)
> +- *FileScan parquet [pre_gid#1385,post_gid#1386] Batched: true,
> Format: Parquet, Location:
> InMemoryFileIndex[file:/media/psf/Home/dev/Functionalizer/pyspark/spykfunc_output/extended_touche...,
> PartitionFilters: [], PushedFilters: [], ReadSchema:
> struct<pre_gid:int,post_gid:int>
> {code}
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