peter-toth commented on code in PR #54330:
URL: https://github.com/apache/spark/pull/54330#discussion_r2880116822
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sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala:
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@@ -549,23 +521,19 @@ case class EnsureRequirements(
// whether partially clustered distribution can be applied. For
instance, the
// optimization cannot be applied to a left outer join, where the
left hand
// side is chosen as the side to replicate partitions according to
stats.
- // Similarly, the partially clustered distribution cannot be applied
if the
- // partially clustered side must use the scan's key-grouped
partitioning to
- // satisfy some unrelated required distribution in its plan (for
example, for an aggregate
- // or window function), as this will give incorrect results (for
example, duplicate
- // row_number() values).
// Otherwise, query result could be incorrect.
- val canReplicateLeft = canReplicateLeftSide(joinType) &&
- canApplyPartialClusteredDistribution(right)
- val canReplicateRight = canReplicateRightSide(joinType) &&
- canApplyPartialClusteredDistribution(left)
+ val canReplicateLeft = canReplicateLeftSide(joinType)
+ val canReplicateRight = canReplicateRightSide(joinType)
Review Comment:
Before this PR partition grouping logic was applied in the leaf scan node,
which meant that there could be conflicting requrements from a join for the
scan to provide partially clustered data; and from a node below the join for
the scan to provide grouped (clustered) data. We needed the guard to not
override requrements of nodes below the join.
After this PR we can place `GroupPartitionsExec` to anywhere in the plan if
we need grouping/regrouping of partitions or keep unclustered partitions as
they are.
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