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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