advancedxy commented on code in PR #45267:
URL: https://github.com/apache/spark/pull/45267#discussion_r1521751659
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala:
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@@ -635,6 +636,22 @@ trait ShuffleSpec {
*/
def createPartitioning(clustering: Seq[Expression]): Partitioning =
throw SparkUnsupportedOperationException()
+
+ /**
+ * Return a set of [[Reducer]] for the partition expressions of this shuffle
spec,
+ * on the partition expressions of another shuffle spec.
+ * <p>
+ * A [[Reducer]] exists for a partition expression function of this shuffle
spec if it is
+ * 'reducible' on the corresponding partition expression function of the
other shuffle spec.
+ * <p>
+ * If a value is returned, there must be one Option[[Reducer]] per partition
expression.
+ * A None value in the set indicates that the particular partition
expression is not reducible
+ * on the corresponding expression on the other shuffle spec.
+ * <p>
+ * Returning none also indicates that none of the partition expressions can
be reduced on the
+ * corresponding expression on the other shuffle spec.
+ */
+ def reducers(spec: ShuffleSpec): Option[Seq[Option[Reducer[_]]]] = None
Review Comment:
> The Reducer here allows data sources to specify relationships between
transforms beyond the bucketing case.
Yeah, I can get the potential usage now. However, It's still hard for
developers to correctly understands what `Reducer` actually mean (it's
mathematically clear though) and how does it work. Maybe we should add some
concrete examples in the JavaDoc of `Reducer` class. It would also be great to
demonstrate the use case beyonds the bucketing case in the test, but I think
that's optional.
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