Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14140#discussion_r70429952
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
---
@@ -408,8 +409,12 @@ class IsotonicRegression private (private var
isotonic: Boolean) extends Seriali
*/
private def parallelPoolAdjacentViolators(
input: RDD[(Double, Double, Double)]): Array[(Double, Double,
Double)] = {
- val parallelStepResult = input
- .sortBy(x => (x._2, x._1))
+ val keyedInput = input
--- End diff --
I think there may be shorter ways to write this with `groupBy`, but, this
and other approaches like that have the big drawback of reading lots of data
into memory. Here you have to sort the whole partition in memory (!).
How about `repartitionAndSortWithinPartitions`? oddly specific method, but,
likely just what you need here, to both partition according to some criteria
but then end up with sorted partitions. It's more scalable.
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