huaxingao commented on PR #34785:
URL: https://github.com/apache/spark/pull/34785#issuecomment-1132363307
Thanks @aokolnychyi for the proposal. I agree that we should support both
strictly required distribution and best effort distribution. For best effort
distribution, if user doesn't request a specific number of partitions, we will
split skewed partitions and coalesce small partitions. For strictly required
distribution, if user doesn't request a specific number of partitions, we will
coalesce small partitions but we will NOT split skewed partitions since this
changes the required distribution.
In interface `RequiresDistributionAndOrdering`, I will add
```
default boolean distributionStrictlyRequired() { return true; }
```
Then in `DistributionAndOrderingUtils`.`prepareQuery`, I will change the
code to something like this:
```
val queryWithDistribution = if (distribution.nonEmpty) {
if (!write.distributionStrictlyRequired() && numPartitions == 0) {
RebalancePartitions(distribution, query)
} else {
if (numPartitions > 0) {
RepartitionByExpression(distribution, query, numPartitions)
} else {
RepartitionByExpression(distribution, query, None)
}
}
...
```
Basically, in the best effort case, if the requested numPartitions is 0, we
will use `RebalancePartitions` so both `OptimizeSkewInRebalancePartitions` and
`CoalesceShufflePartitions` will be applied. In the strictly required
distribution case, if the requested numPartitions is 0, we will use
`RepartitionByExpression(distribution, query, None)` so
`CoalesceShufflePartitions` will be applied.
Does this sound correct for every one?
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