mridulm commented on a change in pull request #33644:
URL: https://github.com/apache/spark/pull/33644#discussion_r692293917
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File path: core/src/main/scala/org/apache/spark/rdd/RDD.scala
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@@ -1233,6 +1233,22 @@ abstract class RDD[T: ClassTag](
(i, iter) => iter.map((i % curNumPartitions, _))
Review comment:
Actually, I like @HyukjinKwon's proposal.
A variant of it would be add a new `treeAggregate` method to RDD which has
all the parameters explicitly specified (no defaults, no currying).
And have everything else delegate to it.
This should take care of @srowen's concerns about binary/source
compatibility (existing methods remain as is), while introducing a new method
in scala api which allows for per method customization (and not global config
affecting all aggregates).
Thoughts ?
In RDD:
```
def treeAggregate[U: ClassTag](zeroValue: U)(
seqOp: (U, T) => U,
combOp: (U, U) => U,
depth: Int = 2): U = withScope {
treeAggregate(zeroValue, seqOp, combOp, depth, false)
}
def treeAggregate[U: ClassTag](
zeroValue: U,
seqOp: (U, T) => U,
combOp: (U, U) => U,
depth: Int,
finalAggregateOnExecutor: Boolean): U = withScope {
// modified method taking finalAggregateOnExecutor into account
}
```
java api to mirror and delegate to the scala api as appropriate.
Thoughts @HyukjinKwon, @srowen, @akpatnam25, @venkata91 ?
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