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https://issues.apache.org/jira/browse/FLINK-14815?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16976365#comment-16976365
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Piotr Nowojski commented on FLINK-14815:
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Regarding the aggregation of the metrics. If one single subtask is
back-pressured, do we report that whole task is back-pressured? I think that
would make sense.
For the pool usages, I'm not sure about the "max" value, as we are loosing a
lot of the fidelity. If any sub task is back-pressured, both its input and
output pool will be full, so the aggregated value will be also "100%". Which is
a redundant information with the back-pressured status (drawing the task vertex
in red). Maybe average would give us more information? Thanks to that, one
could judge how many subtasks are affected by the back-pressure.
> Expose network pool usage in IOMetricsInfo
> ------------------------------------------
>
> Key: FLINK-14815
> URL: https://issues.apache.org/jira/browse/FLINK-14815
> Project: Flink
> Issue Type: Sub-task
> Components: Runtime / Metrics, Runtime / Network, Runtime / REST
> Reporter: lining
> Assignee: lining
> Priority: Major
>
> * If sub task is not back pressured, but it is causing a back pressure (full
> input, empty output)
> * By comparing exclusive/floating buffers usage, whether all channels are
> back-pressured or only some of them
> {code:java}
> public final class IOMetricsInfo {
> private final float outPoolUsage;
> private final float inputExclusiveBuffersUsage;
> private final float inputFloatingBuffersUsage;
> }
> {code}
> JobDetailsInfo.JobVertexDetailsInfo merge use Math.max.(ps: outPoolUsage is
> from upstream)
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