mxm commented on code in PR #920:
URL:
https://github.com/apache/flink-kubernetes-operator/pull/920#discussion_r1858997326
##########
flink-autoscaler/src/main/java/org/apache/flink/autoscaler/JobVertexScaler.java:
##########
@@ -289,21 +274,26 @@ private ParallelismChange applyScaleDownInterval(
var scaleDownInterval = conf.get(SCALE_DOWN_INTERVAL);
if (scaleDownInterval.toMillis() <= 0) {
// The scale down interval is disable, so don't block scaling.
- return ParallelismChange.required(newParallelism);
- }
-
- var firstTriggerTime =
delayedScaleDown.getFirstTriggerTimeForVertex(vertex);
- if (firstTriggerTime.isEmpty()) {
- LOG.info("The scale down of {} is delayed by {}.", vertex,
scaleDownInterval);
- delayedScaleDown.updateTriggerTime(vertex, clock.instant());
- return ParallelismChange.optional(newParallelism);
+ return ParallelismChange.build(newParallelism);
}
- if
(clock.instant().isBefore(firstTriggerTime.get().plus(scaleDownInterval))) {
- LOG.debug("Try to skip immediate scale down within scale-down
interval for {}", vertex);
- return ParallelismChange.optional(newParallelism);
+ var now = clock.instant();
+ var delayedScaleDownInfo = delayedScaleDown.triggerScaleDown(vertex,
now, newParallelism);
+
+ // Never scale down within scale down interval
+ if
(now.isBefore(delayedScaleDownInfo.getFirstTriggerTime().plus(scaleDownInterval)))
{
+ if (now.equals(delayedScaleDownInfo.getFirstTriggerTime())) {
+ LOG.info("The scale down of {} is delayed by {}.", vertex,
scaleDownInterval);
+ } else {
+ LOG.debug(
+ "Try to skip immediate scale down within scale-down
interval for {}",
+ vertex);
+ }
+ return ParallelismChange.noChange();
} else {
- return ParallelismChange.required(newParallelism);
+ // Using the maximum parallelism within the scale down interval
window instead of the
+ // latest parallelism when scaling down
+ return
ParallelismChange.build(delayedScaleDownInfo.getMaxRecommendedParallelism());
Review Comment:
Can we make sure though, that the recommended scale down parallelism is not
higher than the current one? This could theoretically happen if the recommended
parallelism is higher than the current parallelism during the scale down period.
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