sumeetgajjar opened a new pull request #35047:
URL: https://github.com/apache/spark/pull/35047
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### What changes were proposed in this pull request?
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This PR aims at improving performance for Hash joins with many duplicate
keys.
A HashedRelation uses a map underneath to store rows against a corresponding
key. A LongToUnsafeRowMap is used by LongHashedRelation and a BytesToBytesMap
is used by UnsafeHashedRelation.
We propose to reorder the underlying map thereby placing all the rows for a
given key adjacent in the memory to improve the spatial locality while
iterating over them in the stream side of the join.
This is achieved in the following steps:
- creating another copy of the underlying map
- for all keys in the existing map
- get the corresponding rows
- insert all the rows for the given key at once in the new map
- use the new map for look-ups
This optimization can be enabled by specifying
`spark.sql.hashedRelationReorderFactor=<value>`.
Once the condition `number of rows >= number of unique keys * above value`
is satisfied for the underlying map, the optimization will kick in.
### Why are the changes needed?
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There is no order maintained when the rows are added to the underlying map,
thus for a given key, the corresponding rows are typically non-adjacent in
memory, resulting in a poor spatial locality. Placing the rows for adjacent in
memory yields a performance boost thereby reducing execution time.
### Does this PR introduce _any_ user-facing change?
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No.
### How was this patch tested?
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- Modified existing unit tests to run against the suggested improvement.
- Added a couple of cases to test the scenarios when the improvement throws
an exception due to insufficient memory.
- Added a micro-benchmark that clearly indicates performance improvements
when there are duplicate keys.
- Ran the four example queries mentioned in the JIRA in spark-sql as a final
check for performance improvement.
### Credits
This work is based on the initial idea proposed by @bersprockets.
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