Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/6430#discussion_r31355203
--- Diff:
core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala ---
@@ -262,15 +267,22 @@ private[spark] class ExecutorAllocationManager(
val maxNeeded = maxNumExecutorsNeeded
if (maxNeeded < numExecutorsTarget) {
- // The target number exceeds the number we actually need, so stop
adding new
- // executors and inform the cluster manager to cancel the extra
pending requests
- val oldNumExecutorsTarget = numExecutorsTarget
- numExecutorsTarget = math.max(maxNeeded, minNumExecutors)
- client.requestTotalExecutors(numExecutorsTarget)
- numExecutorsToAdd = 1
- logInfo(s"Lowering target number of executors to $numExecutorsTarget
because " +
- s"not all requests are actually needed (previously
$oldNumExecutorsTarget)")
- numExecutorsTarget - oldNumExecutorsTarget
+ if (!numTargetExecutorAdjustable.get) {
+ // Keep the initial number of target executor to not ramp down
until the first job is
--- End diff --
Yeah, now I get it.
But during the initialization phase, we can't actually ramp up beyond
initial executors can we? This is because the only way to ramp up is to have a
backlog of tasks, and this only happens after we have submitted a stage, at
which point we are no longer in the initialization phase.
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