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https://issues.apache.org/jira/browse/SPARK-12704?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15956285#comment-15956285
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yucai commented on SPARK-12704:
-------------------------------
When sort-merge-join and shuffled-hash-join, the sparkPlan looks like
containing *equivalent class* info in _leftKeys_ and _rightKeys_
For example:
{code}
scala> sql("select * from t1 join t2 on t1.f1 = t2.x and t2.y = t1.f2").explain
17/04/05 12:19:42 INFO SparkSqlParser: Parsing command: select * from t1 join
t2 on t1.f1 = t2.x and t2.y = t1.f2
== Physical Plan ==
*SortMergeJoin [f1#80L, f2#81L], [x#88L, y#87L], Inner
{code}
It knows *f1* is equivalent to *x* and *f2* is equivalent to *y*
(ExtractEquiJoinKeys.unapply), is it possible we use this info to improve this
feature?
> we may repartition a relation even it's not needed
> --------------------------------------------------
>
> Key: SPARK-12704
> URL: https://issues.apache.org/jira/browse/SPARK-12704
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Reporter: Wenchen Fan
>
> The implementation of {{HashPartitioning.compatibleWith}} has been
> sub-optimal for a while. Think of the following case:
> if {{table_a}} is hash partitioned by int column `i`, and {{table_b}} is also
> partitioned by int column `i`, logically these 2 partitionings are
> compatible. However, {{HashPartitioning.compatibleWith}} will return false
> for this case as the {{AttributeReference}} of column `i` between these 2
> tables have different expr ids.
> With this wrong result of {{HashPartitioning.compatibleWith}}, we will go
> into [this
> branch|https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala#L390]
> and may add unnecessary shuffle.
> This won't impact correctness if the join keys are exactly the same with hash
> partitioning keys, as there’s still an opportunity to not partition that
> child in that branch:
> https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala#L428
> However, if the join keys are a super-set of hash partitioning keys, for
> example, {{table_a}} and {{table_b}} are both hash partitioned by column `i`,
> and we wanna join them using column `i, j`, logically we don't need shuffle
> but in fact the 2 tables start out as partitioned only by `i` and redundantly
> be repartitioned by `i, j`.
> A quick fix is just set the expr id of {{AttributeReference}} to 0 before we
> call {{this.semanticEquals(o)}} in {{HashPartitioning.compatibleWith}}, but
> for long term, I think we need a better design than the `compatibleWith`,
> `guarantees`, and `satisfies` mechanism, as it's quite complex
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