Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/11327#discussion_r57361377
--- Diff: core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala ---
@@ -324,6 +319,40 @@ private class PartitionCoalescer(maxPartitions: Int,
prev: RDD[_], balanceSlack:
}
}
} else {
+ // It is possible to have unionRDD where one rdd has preferred
locations and another rdd
+ // that doesn't. To make sure we end up with the requested number of
partitions,
+ // make sure to put a partitions in every group.
+
+ if (groupArr.size > initialHash.size) {
+ // we don't have a partition assigned to every group yet so first
try to fill them
+ // with the partitions with preferred locations
+ var tries = 0
+ val rotIt = new LocationIterator(prev)
+ while (tries < prev.partitions.length && initialHash.size <
groupArr.size) {
+ // if the number of partitions with preferred locations is less
then
+ // number of total partitions this might loop over some more
then once but we need to
+ // handle both cases and its not easy to get # of partitions
with preferred locs
+ var (nxt_replica, nxt_part) = rotIt.next()
+ if (!initialHash.contains(nxt_part)) {
+ groupArr.find(pg => pg.size == 0).map(firstEmpty => {
+ firstEmpty.arr += nxt_part
+ initialHash += nxt_part
+ })
+ }
+ tries += 1
+ }
+ }
+ // we have went through all with preferred locations now just make
sure one
+ // partition per group
+ val numEmptyPartitionGroups = groupArr.length - getPartitions.length
+ val partitionsNotInGroups = prev.partitions.filter(p =>
!initialHash.contains(p))
+ for (i <- 0 until math.min(numEmptyPartitionGroups,
partitionsNotInGroups.length)) {
+ groupArr.find(pg => pg.size == 0).map(firstEmpty => {
--- End diff --
This is still O(N*N)
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