tgravescs commented on a change in pull request #27223:
[SPARK-30511][SPARK-28403][CORE] Don't treat failed/killed speculative tasks as
pending in Spark scheduler
URL: https://github.com/apache/spark/pull/27223#discussion_r370401524
##########
File path: core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
##########
@@ -263,9 +263,15 @@ private[spark] class ExecutorAllocationManager(
*/
private def maxNumExecutorsNeeded(): Int = {
val numRunningOrPendingTasks = listener.totalPendingTasks +
listener.totalRunningTasks
- math.ceil(numRunningOrPendingTasks * executorAllocationRatio /
- tasksPerExecutorForFullParallelism)
- .toInt
+ val maxNeeded = math.ceil(numRunningOrPendingTasks *
executorAllocationRatio /
+ tasksPerExecutorForFullParallelism).toInt
+ if (listener.pendingSpeculativeTasks > 0 &&
tasksPerExecutorForFullParallelism > 1) {
+ // If we have pending speculative tasks, allocate one more executor to
satisfy the
+ // locality requirements of speculative tasks
+ maxNeeded + 1
Review comment:
I typoed my example should be 1000 tasks with 2 tasks per executor, would
results in 500 executors, we don't need to add one more
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