sharath1709 commented on code in PR #686:
URL:
https://github.com/apache/flink-kubernetes-operator/pull/686#discussion_r2286286182
##########
flink-autoscaler/src/main/java/org/apache/flink/autoscaler/metrics/ScalingMetrics.java:
##########
@@ -78,6 +88,45 @@ public static void computeDataRateMetrics(
}
}
+ private static Optional<Double> getObservedTpr(
+ Map<FlinkMetric, AggregatedMetric> flinkMetrics,
+ Map<ScalingMetric, Double> scalingMetrics,
+ double numRecordsInPerSecond,
+ Configuration conf) {
+
+ // If there are no incoming records we return infinity to allow scale
down
+ if (numRecordsInPerSecond == 0) {
+ return Optional.of(Double.POSITIVE_INFINITY);
+ }
+
+ // We only measure observed tpr when we are catching up, that is when
the lag is beyond the
+ // configured observe threshold
+ boolean catchingUp =
+ scalingMetrics.getOrDefault(ScalingMetric.LAG, 0.)
+ >=
conf.get(AutoScalerOptions.OBSERVE_TRUE_PROCESSING_RATE_LAG_THRESHOLD)
+ .toSeconds()
+ * numRecordsInPerSecond;
+ if (!catchingUp) {
+ return Optional.empty();
+ }
+
+ double observedTpr =
+ computeObservedTprWithBackpressure(
+ numRecordsInPerSecond,
+
flinkMetrics.get(FlinkMetric.BACKPRESSURE_TIME_PER_SEC).getAvg());
+
+ return Double.isNaN(observedTpr) ? Optional.empty() :
Optional.of(observedTpr);
+ }
+
+ public static double computeObservedTprWithBackpressure(
Review Comment:
I understand that job.autoscaler.catch-up.duration refers to the time to
catch up only after the scaling operation. However, I find the assumption that
backpressure can always be eliminated in the next scaling to be inaccurate. In
practice, I’ve observed cases where a task remains backpressured even when the
downstream task shows low utilization, depending on the task’s business logic.
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