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https://issues.apache.org/jira/browse/FLINK-19177?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17235216#comment-17235216
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Zhu Zhu commented on FLINK-19177:
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[~xintongsong] [~dian.fu]
Do you think we should introduce a section in [memory tuning
doc|https://ci.apache.org/projects/flink/flink-docs-master/ops/memory/mem_tuning.html]
to guide users to configure managed memory for python? And also add a pointer
in [managed-memory introduction
doc|https://ci.apache.org/projects/flink/flink-docs-master/ops/memory/mem_setup_tm.html#managed-memory]
like the docs for statebackend and batch operators?
> FLIP-141: Intra-Slot Managed Memory Sharing
> -------------------------------------------
>
> Key: FLINK-19177
> URL: https://issues.apache.org/jira/browse/FLINK-19177
> Project: Flink
> Issue Type: Improvement
> Components: Runtime / Coordination
> Reporter: Xintong Song
> Assignee: Xintong Song
> Priority: Major
> Labels: Umbrella
> Fix For: 1.12.0
>
>
> This is the umbrella ticket of [FLIP-141: Intra-Slot Managed Memory
> Sharing|https://cwiki.apache.org/confluence/display/FLINK/FLIP-141%3A+Intra-Slot+Managed+Memory+Sharing].
>
> [FLIP-53|https://cwiki.apache.org/confluence/display/FLINK/FLIP-53%3A+Fine+Grained+Operator+Resource+Management]
> introduced a fraction based approach for sharing managed memory within a
> slot. This approach needs to be extended as python operators, which also use
> managed memory, are introduced. This FLIP proposes a design for extending
> intra-slot managed memory sharing for python operators and other potential
> future managed memory use cases.
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