Github user yhuai commented on the issue:
https://github.com/apache/spark/pull/19080
Have a question after reading the new approach. Let's say that we have a
join like `T1 JOIN T2 on T1.a = T2.a`. Also `T1` is hash partitioned by the
value of `T1.a` and it has 10 partitions, and `T2` is range partitioned by the
value of `T2.a` and it has 10 partitions. Both sides will satisfy the required
distribution of the join. However, we need to add an exchange at either side in
order to produce the correct result. How will we handle this case with this
change?
Also, regarding
> For multiple children, Spark only guarantees they have the same number of
partitions, and it's the operator's responsibility to leverage this guarantee
to achieve more complicated requirements.
Can you give a concrete example?
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