Hisoka-X opened a new pull request, #42194:
URL: https://github.com/apache/spark/pull/42194
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### What changes were proposed in this pull request?
When only one side of a SPJ (Storage-Partitioned Join) is
KeyGroupedPartitioning, Spark currently needs to shuffle both sides using
HashPartitioning. However, we may just need to shuffle the other side according
to the partition transforms defined in KeyGroupedPartitioning. This is
especially useful when the other side is relatively small.
1. Add new config `spark.sql.sources.v2.bucketing.shuffleOneSide.enabled` to
control this feature enable or not.
2. Add `PartitionValueMapShuffleSpec`,`PartitionValueMapPartitioning` and
`PartitionValueMapPartitioner` use to partition when we know the tranform value
of another side (KeyGroupedPartitioning at now). Spark already know the
partition value with partition id of KeyGroupedPartitioning side in
`EnsureRequirements`. Then save it in `PartitionValueMapPartitioner` use to
shuffle another partition, to make sure the same key data will shuffle into
same partition.
3. only `identity` transform will work now. Because have another a problem
for now, same transform between DS V2 connector implement and catalog function
will report different value, before solve this problem, we should only support
`identity`. eg: in test package, `YearFunction`
https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/connector/catalog/functions/transformFunctions.scala#L47
and
https://github.com/apache/spark/blob/master/sql/catalyst/src/test/scala/org/apache/spark/sql/connector/catalog/InMemoryBaseTable.scala#L143
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### Why are the changes needed?
Reduce data shuffle in specific SPJ scenarios
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### Does this PR introduce _any_ user-facing change?
No
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### How was this patch tested?
add new test
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