bsyk commented on code in PR #17988:
URL: https://github.com/apache/druid/pull/17988#discussion_r2176201417
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indexing-service/src/main/java/org/apache/druid/indexing/seekablestream/supervisor/autoscaler/LagBasedAutoScaler.java:
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@@ -200,84 +209,141 @@ private Runnable computeAndCollectLag()
* @param lags the lag metrics of Stream(Kafka/Kinesis)
* @return Integer. target number of tasksCount, -1 means skip scale action.
*/
- private int computeDesiredTaskCount(List<Long> lags)
+ @VisibleForTesting
+ int computeDesiredTaskCount(List<Long> lags)
{
- // if supervisor is not suspended, ensure required tasks are running
- // if suspended, ensure tasks have been requested to gracefully stop
log.debug("Computing desired task count for [%s], based on following lags
: [%s]", dataSource, lags);
+ final int currentActiveTaskCount = supervisor.getActiveTaskGroupsCount();
+ final int partitionCount = supervisor.getPartitionCount();
+ if (partitionCount <= 0) {
+ log.warn("Partition number for [%s] <= 0 ? how can it be?", dataSource);
+ return -1;
+ }
+
+ // Cache the factorization in an immutable list for quick lookup later
+ // Partition counts *can* change externally without a new instance of this
class being created
+ if (partitionFactors.isEmpty() || partitionCount !=
partitionFactors.get(partitionFactors.size() - 1)) {
+ log.debug("(Re)computing partitionCount factorization for
partitionCount=[%d]", partitionCount);
+ partitionFactors = factorize(partitionCount);
+ }
+
+ Preconditions.checkState(!partitionFactors.isEmpty(), "partitionFactors
should not be empty");
+
+ final int desiredActiveTaskCount = computeDesiredTaskCountHelper(lags,
currentActiveTaskCount);
+ return applyMinMaxChecks(desiredActiveTaskCount, currentActiveTaskCount,
partitionCount);
+ }
+
+ private int computeDesiredTaskCountHelper(final List<Long> lags, final int
currentActiveTaskCount)
+ {
int beyond = 0;
int within = 0;
- int metricsCount = lags.size();
- for (Long lag : lags) {
+ final int metricsCount = lags.size();
+ for (final Long lag : lags) {
if (lag >= lagBasedAutoScalerConfig.getScaleOutThreshold()) {
beyond++;
}
if (lag <= lagBasedAutoScalerConfig.getScaleInThreshold()) {
within++;
}
}
- double beyondProportion = beyond * 1.0 / metricsCount;
- double withinProportion = within * 1.0 / metricsCount;
+ final double beyondProportion = beyond * 1.0 / metricsCount;
+ final double withinProportion = within * 1.0 / metricsCount;
- log.debug("Calculated beyondProportion is [%s] and withinProportion is
[%s] for dataSource [%s].", beyondProportion,
+ log.debug(
+ "Calculated beyondProportion is [%s] and withinProportion is [%s] for
dataSource [%s].", beyondProportion,
withinProportion, dataSource
);
- int currentActiveTaskCount = supervisor.getActiveTaskGroupsCount();
- int desiredActiveTaskCount;
- int partitionCount = supervisor.getPartitionCount();
- if (partitionCount <= 0) {
- log.warn("Partition number for [%s] <= 0 ? how can it be?", dataSource);
+ if (beyondProportion >=
lagBasedAutoScalerConfig.getTriggerScaleOutFractionThreshold()) {
+ return currentActiveTaskCount +
lagBasedAutoScalerConfig.getScaleOutStep();
+ } else if (withinProportion >=
lagBasedAutoScalerConfig.getTriggerScaleInFractionThreshold()) {
+ return currentActiveTaskCount -
lagBasedAutoScalerConfig.getScaleInStep();
+ }
+
+ return currentActiveTaskCount;
+ }
+
+
+ private int applyMinMaxChecks(int desiredActiveTaskCount, final int
currentActiveTaskCount, final int partitionCount)
+ {
+ // for now, only attempt to scale to nearest factor for scale up
Review Comment:
> This is because you risk scaling down more than you need to
If we scale to the next largest _above_ the desired, we won't scale down too
far. And we'd protect some indexers from having more partitions assigned than
others.
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