Github user kiszk commented on a diff in the pull request:
https://github.com/apache/spark/pull/21859#discussion_r207819972
--- Diff: core/src/main/scala/org/apache/spark/Partitioner.scala ---
@@ -166,7 +170,13 @@ class RangePartitioner[K : Ordering : ClassTag, V](
// Assume the input partitions are roughly balanced and over-sample
a little bit.
val sampleSizePerPartition = math.ceil(3.0 * sampleSize /
rdd.partitions.length).toInt
val (numItems, sketched) = RangePartitioner.sketch(rdd.map(_._1),
sampleSizePerPartition)
- if (numItems == 0L) {
+ // get the sampled data
+ sampledArray = sketched.foldLeft(sampledArray)((total, sample) => {
--- End diff --
Do we need to always create `sampledArray` and to store into `var`? It may
lead to overhead when the execution would go to L182.
It would be good to calculate only length here and to create the array at
L179.
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