Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/7888#discussion_r38681374
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
---
@@ -413,25 +413,38 @@ class CoarseGrainedSchedulerBackend(scheduler:
TaskSchedulerImpl, val rpcEnv: Rp
*
* @param executorIds identifiers of executors to kill
* @param replace whether to replace the killed executors with new ones
+ * @param force whether to kill busy executors (who are running tasks)
* @return whether the kill request is acknowledged.
*/
- final def killExecutors(executorIds: Seq[String], replace: Boolean):
Boolean = synchronized {
+ final def killExecutors(
+ executorIds: Seq[String],
+ replace: Boolean,
+ force: Boolean): Boolean = synchronized {
logInfo(s"Requesting to kill executor(s) ${executorIds.mkString(",
")}")
val (knownExecutors, unknownExecutors) =
executorIds.partition(executorDataMap.contains)
unknownExecutors.foreach { id =>
logWarning(s"Executor to kill $id does not exist!")
}
+ val idleExecutors = {
+ if (force) {
+ knownExecutors
+ } else {
+ knownExecutors.filter(executor =>
+ !scheduler.taskIdToExecutorId.exists(_._2 == executor))
--- End diff --
This would be somewhat expensive if you have 50k tasks, and we iterate
through this once per executor we want to kill. Maybe in `TaskSchedulerImpl` we
should keep track of a set of executors that currently have tasks scheduled on
them instead, so this would take O(num executors) instead of O(num executors *
num active tasks).
---
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