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From nishkamravi2 <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-8881] Fix algorithm for scheduling exec...
Date Wed, 08 Jul 2015 00:38:46 GMT
Github user nishkamravi2 commented on a diff in the pull request:

    https://github.com/apache/spark/pull/7274#discussion_r34107025
  
    --- Diff: core/src/main/scala/org/apache/spark/deploy/master/Master.scala ---
    @@ -544,38 +544,60 @@ private[master] class Master(
        * has enough cores and memory. Otherwise, each executor grabs all the cores available
on the
        * worker by default, in which case only one executor may be launched on each worker.
        */
    -  private def startExecutorsOnWorkers(): Unit = {
    -    // Right now this is a very simple FIFO scheduler. We keep trying to fit in the first
app
    -    // in the queue, then the second app, etc.
    +
    +  private[master] def scheduleExecutorsOnWorkers(app: ApplicationInfo, usableWorkers:
Array[WorkerInfo],
    +    spreadOutApps: Boolean): Array[Int] = {
    +    val coresPerExecutor = app.desc.coresPerExecutor.getOrElse(1)
    +    val memoryPerExecutor = app.desc.memoryPerExecutorMB
    +    val numUsable = usableWorkers.length
    +    val assignedCores = new Array[Int](numUsable) // Number of cores to give to each
worker
    +    val assignedMemory = new Array[Int](numUsable) // Amount of memory to give to each
worker
    +    var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
    +    var pos = 0
         if (spreadOutApps) {
    -      // Try to spread out each app among all the workers, until it has all its cores
    -      for (app <- waitingApps if app.coresLeft > 0) {
    -        val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
    -          .filter(worker => worker.memoryFree >= app.desc.memoryPerExecutorMB &&
    -            worker.coresFree >= app.desc.coresPerExecutor.getOrElse(1))
    -          .sortBy(_.coresFree).reverse
    -        val numUsable = usableWorkers.length
    -        val assigned = new Array[Int](numUsable) // Number of cores to give on each node
    -        var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
    -        var pos = 0
    -        while (toAssign > 0) {
    -          if (usableWorkers(pos).coresFree - assigned(pos) > 0) {
    -            toAssign -= 1
    -            assigned(pos) += 1
    -          }
    -          pos = (pos + 1) % numUsable
    -        }
    -        // Now that we've decided how many cores to give on each node, let's actually
give them
    -        for (pos <- 0 until numUsable if assigned(pos) > 0) {
    -          allocateWorkerResourceToExecutors(app, assigned(pos), usableWorkers(pos))
    +      // Try to spread out executors among workers (sparse scheduling)
    +      while (toAssign > 0) {
    +        if (usableWorkers(pos).coresFree - assignedCores(pos) >= coresPerExecutor
&&
    +            usableWorkers(pos).memoryFree - assignedMemory(pos) >= memoryPerExecutor)
{
    +          toAssign -= coresPerExecutor
    +          assignedCores(pos) += coresPerExecutor
    +          assignedMemory(pos) += memoryPerExecutor
             }
    +        pos = (pos + 1) % numUsable
           }
         } else {
    -      // Pack each app into as few workers as possible until we've assigned all its cores
    -      for (worker <- workers if worker.coresFree > 0 && worker.state ==
WorkerState.ALIVE) {
    -        for (app <- waitingApps if app.coresLeft > 0) {
    -          allocateWorkerResourceToExecutors(app, app.coresLeft, worker)
    +      // Pack executors into as few workers as possible (dense scheduling)
    +      while (toAssign > 0) {
    --- End diff --
    
    Not identical. In one we are spreading out, in another we aren't.


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