Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/6652#discussion_r31777313
--- Diff: core/src/main/scala/org/apache/spark/MapOutputTracker.scala ---
@@ -284,6 +291,41 @@ private[spark] class MapOutputTrackerMaster(conf:
SparkConf)
cachedSerializedStatuses.contains(shuffleId) ||
mapStatuses.contains(shuffleId)
}
+ /**
+ * Return a list of locations which have the largest map outputs given a
shuffleId
+ * and a reducerId.
+ *
+ * This method is not thread-safe
+ */
+ def getLocationsWithLargestOutputs(
+ shuffleId: Int,
+ reducerId: Int,
+ numReducers: Int,
+ numTopLocs: Int)
+ : Option[Array[BlockManagerId]] = {
+ if (!shuffleIdToReduceLocations.contains(shuffleId) &&
mapStatuses.contains(shuffleId)) {
+ // Pre-compute the top locations for each reducer and cache it
+ val statuses = mapStatuses(shuffleId)
+ if (statuses.nonEmpty) {
+ val ordering = Ordering.by[(BlockManagerId, Long),
Long](_._2).reverse
+ shuffleIdToReduceLocations(shuffleId) = new HashMap[Int,
Array[BlockManagerId]]
+ var r = 0
+ while (r < numReducers) {
+ // Add up sizes of all blocks at the same location
+ val locs = statuses.map { s =>
--- End diff --
If perf. is important here, why not write some java-style code that does an
aggregate-by-key using a mutable hashmap?
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