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https://issues.apache.org/jira/browse/FLINK-34152?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17822504#comment-17822504
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Yang LI commented on FLINK-34152:
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Hello [~mxm] , I am very interested in this memory tuning feature and I have
reviewed this PR
[https://github.com/apache/flink-kubernetes-operator/pull/762|https://github.com/apache/flink-kubernetes-operator/pull/762.]
Here is my feedback about this auto-tuning feature
* We may need to dynamically adjust the Kubernetes CPU and memory limits for
both the job manager and task manager eventually, to align with the
automatically tuned memory and CPU parameters and prevent unnecessary resource
allocation.
* In our specific use-case, our Flink cluster is deployed on a dedicated node
group with predefined CPU and memory settings, unlike a typical Kubernetes
cluster. Consequently, this auto-tuning feature might not aid in reducing
infrastructure costs, as billing is based on the allocated nodes behind the
scenes.
> Tune TaskManager memory
> -----------------------
>
> Key: FLINK-34152
> URL: https://issues.apache.org/jira/browse/FLINK-34152
> Project: Flink
> Issue Type: Sub-task
> Components: Autoscaler, Kubernetes Operator
> Reporter: Maximilian Michels
> Assignee: Maximilian Michels
> Priority: Major
> Labels: pull-request-available
> Fix For: kubernetes-operator-1.8.0
>
>
> The current autoscaling algorithm adjusts the parallelism of the job task
> vertices according to the processing needs. By adjusting the parallelism, we
> systematically scale the amount of CPU for a task. At the same time, we also
> indirectly change the amount of memory tasks have at their dispense. However,
> there are some problems with this.
> # Memory is overprovisioned: On scale up we may add more memory than we
> actually need. Even on scale down, the memory / cpu ratio can still be off
> and too much memory is used.
> # Memory is underprovisioned: For stateful jobs, we risk running into
> OutOfMemoryErrors on scale down. Even before running out of memory, too
> little memory can have a negative impact on the effectiveness of the scaling.
> We lack the capability to tune memory proportionally to the processing needs.
> In the same way that we measure CPU usage and size the tasks accordingly, we
> need to evaluate memory usage and adjust the heap memory size.
> https://docs.google.com/document/d/19GXHGL_FvN6WBgFvLeXpDABog2H_qqkw1_wrpamkFSc/edit
>
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