sunchao opened a new pull request, #38196:
URL: https://github.com/apache/spark/pull/38196
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### What changes were proposed in this pull request?
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This PR fixes a performance regression issue when one side of a join uses
`HashPartitioning` with `ShuffleExchange` while the other side uses
`SinglePartition`. In this case, Spark will re-shuffle the side with
`HashPartitioning` and both sides will end up with only a single partition.
This could hurt query performance a lot if the side with `HashPartitioning`
contains a lot of input data.
### Why are the changes needed?
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After SPARK-35703, when Spark sees that one side of the join has
`ShuffleExchange` (meaning it needs to be shuffled anyways), and the other side
doesn't, it'll try to avoid shuffling the side without `ShuffleExchange`. For
instance:
```
ShuffleExchange(HashPartition(200)) <-> HashPartition(150)
```
will be converted into
```
ShuffleExchange(HashPartition(150)) <-> HashPartition(150)
```
However, when the side without `ShuffleExchange` is `SinglePartition`, like
the following:
```
ShuffleExchange(HashPartition(150)) <-> SinglePartition
```
Spark will also do the same which causes the left-hand side to only use one
partition. This can hurt job parallelism dramatically, especially when using
DataSource V2, since `SinglePartition` is used by the V2 scan. On the other
hand, it seems DataSource V1 won't be impacted much as it always report
`UnknownPartitioning` in this situation.
### Does this PR introduce _any_ user-facing change?
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No.
### How was this patch tested?
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Added new unit tests in `EnsureRequirementsSuite`.
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