attilapiros commented on code in PR #39825:
URL: https://github.com/apache/spark/pull/39825#discussion_r1092616469
##########
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodsAllocator.scala:
##########
@@ -141,9 +143,26 @@ class ExecutorPodsAllocator(
totalExpectedExecutorsPerResourceProfileId.put(rp.id, numExecs)
}
logDebug(s"Set total expected execs to
$totalExpectedExecutorsPerResourceProfileId")
- if (numOutstandingPods.get() == 0) {
+ if (numOutstandingPods.get() < maxPendingPods) {
Review Comment:
The [default of KUBERNETES_MAX_PENDING_PODS is
Int.MaxValue](https://github.com/apache/spark/blob/master/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Config.scala#L737)
(too keep the old behaviour when it was introduced) and the
`numOutstandingPods` main intention was to slow down upscaling at very steep
peaks:
https://github.com/apache/spark/blob/b5b40113a64b4dbbcd4efe86da4409f2be8e9c56/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodsAllocator.scala#L397-L399
What about to use the allocation batch size (more a factor of it as good
lower limit)?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
