Github user nishkamravi2 commented on a diff in the pull request:
https://github.com/apache/spark/pull/6607#discussion_r31800833
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
---
@@ -308,12 +305,41 @@ class ReceiverTracker(ssc: StreamingContext,
skipReceiverLaunch: Boolean = false
supervisor.start()
supervisor.awaitTermination()
}
+
// Run the dummy Spark job to ensure that all slaves have registered.
// This avoids all the receivers to be scheduled on the same node.
if (!ssc.sparkContext.isLocal) {
ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x,
1)).reduceByKey(_ + _, 20).collect()
}
+ // Right now, we only honor preferences if all receivers have them
+ val hasLocationPreferences =
receivers.map(_.preferredLocation.isDefined).reduce(_ && _)
+
+ // If no location preferences are specified, set host location for
each receiver
+ // so as to distribute them evenly over executors in a round-robin
fashion
+ var roundRobin = false;
+ if (!hasLocationPreferences && !ssc.sparkContext.isLocal) {
+ val executors = getExecutors(ssc)
+ if (!executors.isEmpty) {
+ var i = 0;
+ for (i <- 0 to (receivers.length - 1)) {
+ receivers(i).host = Some(executors(i % executors.length))
+ }
+ roundRobin = true
+ }
+ }
+
+ val tempRDD =
+ if (hasLocationPreferences) {
--- End diff --
Thanks for the review. PreferredLocation set for all receivers or none
sounds like a good assumption (either user decides how to schedule or we do).
Here is a slightly modified version of the algorithm:
0. Leave receiver class unmodified and use an external array for storing
locations
1. If location preferences are set (which should be for all receivers or
none), goto 4
2. If num_receivers > num_executors, distribute receivers among executors
(as in current PR)
3. If num_executors > num_receivers, distribute executors among receivers
4. makeRDD
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