mridulm commented on a change in pull request #27773: [SPARK-29154][CORE]
Update Spark scheduler for stage level scheduling
URL: https://github.com/apache/spark/pull/27773#discussion_r390233895
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File path:
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
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@@ -606,10 +608,10 @@ class CoarseGrainedSchedulerBackend(scheduler:
TaskSchedulerImpl, val rpcEnv: Rp
}
- override def maxNumConcurrentTasks(): Int = synchronized {
- executorDataMap.values.map { executor =>
- executor.totalCores / scheduler.CPUS_PER_TASK
- }.sum
+ override def maxNumConcurrentTasks(rp: ResourceProfile): Int = synchronized {
+ val cpusPerTask = rp.getTaskCpus.getOrElse(scheduler.CPUS_PER_TASK)
+ val executorsWithResourceProfile =
executorDataMap.values.filter(_.resourceProfileId == rp.id)
Review comment:
Yes, executors need not have launched, launched/running executors might have
failed (after computing `rdd` in the example above), or number of executors is
< number of partitions in rdd.
`maxNumConcurrentTasks` will tell us how many (max) can be run based on
current state.
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