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https://issues.apache.org/jira/browse/FLINK-27314?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17528732#comment-17528732
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Chesnay Schepler commented on FLINK-27314:
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Keep in mind that the adaptive scheduler (without reactive mode) can be used
outside of standalone mode. Although this is still only recommended for
application clusters.
> Support reactive mode for native Kubernetes integration in Flink Kubernetes
> Operator
> ------------------------------------------------------------------------------------
>
> Key: FLINK-27314
> URL: https://issues.apache.org/jira/browse/FLINK-27314
> Project: Flink
> Issue Type: New Feature
> Components: Kubernetes Operator
> Reporter: Fuyao Li
> Priority: Major
>
> Generally, this task is a low priority task now.
> Flink has some system level Flink metrics, Flink kubernetes operator can
> detect these metrics and rescale automatically based checkpoint(similar to
> standalone reactive mode) and rescale policy configured by users.
> The rescale behavior can be based on CPU utilization or memory utilization.
> # Before rescaling, Flink operator should check whether the cluster has
> enough resources, if not, the rescaling will be aborted.
> # We can create a addition field to support this feature. The fields below
> is just a rough suggestion.
> {code:java}
> reactiveScaling:
> enabled: boolean
> scaleMetric: enum ["CPU", "MEM"]
> scaleDownThreshold:
> scaleUpThreshold:
> minimumLimit:
> maximumLimit:
> increasePolicy: <increase/decrease exponentially or linearly..>
> <some other timeout configuration...>{code}
>
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