Description
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
nrn_vals.printSchema() (nrn_vals .sort("post_gid") .groupBy("post_gid") .agg(F.collect_list("pre_gid").alias("pre_gids")) ).explain()
Outputs the following
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>
Attachments
Issue Links
- duplicates
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SPARK-22223 ObjectHashAggregate introduces unnecessary shuffle
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- Resolved
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