X-czh commented on code in PR #581:
URL:
https://github.com/apache/flink-kubernetes-operator/pull/581#discussion_r1188110764
##########
flink-kubernetes-operator-autoscaler/src/main/java/org/apache/flink/kubernetes/operator/autoscaler/config/AutoScalerOptions.java:
##########
@@ -98,6 +98,13 @@ private static ConfigOptions.OptionBuilder
autoScalerConfig(String key) {
.withDescription(
"Max scale down factor. 1 means no limit on scale
down, 0.6 means job can only be scaled down with 60% of the original
parallelism.");
+ public static final ConfigOption<Double> MAX_SCALE_UP_FACTOR =
+ autoScalerConfig("scale-up.max-factor")
+ .doubleType()
+ .defaultValue(2.0)
Review Comment:
But yes, our use case might be a bit extreme for most operator users. We
tend to add tight constraints on scaling for the overall stability of our
clusters while serving most "well-behaved" jobs well at the cost of limiting
the elasticity of a single job. Here, "well-behaved" refer to the job input
changing smoothly.
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