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https://issues.apache.org/jira/browse/FLINK-31898?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17716490#comment-17716490
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Kyungmin Kim commented on FLINK-31898:
--------------------------------------
[~gyfora]
After watching more metrics, I found out that the busyMsPerSecond metric does
fluctuate a lot (It records only 1k or zero) and I think it results in
incorrect TRUE_PROCESSING_RATE.
It was because my test job throttles the number of record inputs per second.
I changed my job's behavior to allow all inputs, add some delay inside the map
operator and change the configuration as you suggested.
Autoscaler now works very well :). It finds the optimal parallelism.
Sorry for the confusion and I think you can close the issue.
By the way can you let me know when you guys are planning to release 1.5
version?
> Flink k8s autoscaler does not work as expected
> ----------------------------------------------
>
> Key: FLINK-31898
> URL: https://issues.apache.org/jira/browse/FLINK-31898
> Project: Flink
> Issue Type: Bug
> Components: Autoscaler, Kubernetes Operator
> Affects Versions: kubernetes-operator-1.4.0
> Reporter: Kyungmin Kim
> Priority: Major
> Attachments: image-2023-04-24-10-54-58-083.png,
> image-2023-04-24-13-27-17-478.png, image-2023-04-24-13-28-15-462.png,
> image-2023-04-24-13-31-06-420.png, image-2023-04-24-13-41-43-040.png,
> image-2023-04-24-13-42-40-124.png, image-2023-04-24-13-43-49-431.png,
> image-2023-04-24-13-44-17-479.png, image-2023-04-24-14-18-12-450.png,
> image-2023-04-24-16-47-35-697.png
>
>
> Hi I'm using Flink k8s autoscaler to automatically deploy jobs in proper
> parallelism.
> I was using 1.4 version but I found that it does not scale down properly
> because TRUE_PROCESSING_RATE becoming NaN when the tasks are idled.
> In the main branch, I checked the code was fixed to set TRUE_PROCESSING_RATE
> to positive infinity and make scaleFactor to very low value so I'm now
> experimentally using docker image built with main branch of
> Flink-k8s-operator repository in my job.
> It now scales down properly but the problem is, it does not converge to the
> optimal parallelism. It scales down well but it jumps up again to high
> parallelism.
>
> Below is the experimental setup and my figure of parallelism changes result.
> * about 40 RPS
> * each task can process 10 TPS (intended throttling)
> !image-2023-04-24-10-54-58-083.png|width=999,height=266!
> Even using default configuration leads to the same result. What can I do
> more? Thank you.
>
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