mxm commented on code in PR #586:
URL:
https://github.com/apache/flink-kubernetes-operator/pull/586#discussion_r1187270164
##########
flink-kubernetes-operator-autoscaler/src/main/java/org/apache/flink/kubernetes/operator/autoscaler/config/AutoScalerOptions.java:
##########
@@ -68,15 +68,16 @@ private static ConfigOptions.OptionBuilder
autoScalerConfig(String key) {
public static final ConfigOption<Double> TARGET_UTILIZATION_BOUNDARY =
autoScalerConfig("target.utilization.boundary")
.doubleType()
- .defaultValue(0.1)
+ .defaultValue(0.4)
Review Comment:
Not at all. The boundary is used to calculate a scale down and a scale up
rate. If the processing capacity falls below the scale up rate, we will scale
up to reach the target capacity. If we exceeds the scale down rate, we will
scale down to the target capacity. This is a bit counter-intuitive because the
upper and lower bound are actually reversed.
A `1.0` utilization for the upscale threshold will lower the scale up rate
which means that we delaying upscale to utilize 100% of our processing capacity
based on the calculated rates. However, we will still scale up if our
processing capacity is lower than the scale up rate. The scale up rate is
always computed via the target rate, but the comparison is made using the
actual processing capacity.
For example: Let's say we currently have a processing capacity of 100
records/sec. The processing capacity if always estimated at 100% utilization
(we call this also *true rate*). At a target rate of 50 records/second (e.g.
Kafka ingestion rate), the scale up bound will be 50 rec/s. That means we will
only scale up once our processing capacity falls below 50 rec/s. So we will
delay scaling as much as possible. If the target rate was to increase to 110
rec/s, we would scale up because our processing capacity of 100 rec/s is now
lower.
Similarly, the downscale rate will be raised (instead of lowered) when we
increase the utilization boundary. That means we won't scale down as quickly.
The tradeoff here is slightly higher resource usage but the scaling becomes
less aggressive because we will only scale down once our processing capacity
exceeds the know increased "lower bound".
To illutrate this further here some sketches:
Balanced:
```
------ upscale target rate
------ processing capacity (true rate)
------ downscale target rate
```
We will scale up:
```
------ downscale rate
------ upscale rate
------ processing capacity (true rate)
```
We will scale down:
```
------ processing capacity (true rate)
------ downscale rate
------ upscale rate
```
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