Github user yhuai commented on the pull request:
https://github.com/apache/spark/pull/5208#issuecomment-91339237
Actually, instead of introducing new `Distribution` and `Partitioning`, how
about we add the following two concepts to a `SparkPlan`.
* `requiredPartitionOrdering: Seq[Seq[SortOrder]]` defines the required
ordering of rows in a partition for the children of a `SparkPlan`. For every
child, `Seq[SortOrder]` defines the required ordering of rows generated by this
child.
* `outputPartitionOrdering: Seq[SortOrder]` defines the ordering of rows
generated by a `SparkPlan`.
With these concepts, we can mix the requirements on the data distribution
in a partition with our existing requirements on the data distribution of the
entire dataset. For example, for `SortMergeJoin`, we need
`ClusteredDistribution` (or `OrderedDistribution`) and a non-empty list of
columns for `RequiredPartitionOrdering`s of its children.
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