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https://issues.apache.org/jira/browse/FLINK-19177?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dian Fu updated FLINK-19177:
----------------------------
Release Note: The configuration python.fn-execution.buffer.memory.size and
python.fn-execution.framework.memory.size have been removed and so will not
take effect any more. Besides, the default value of
python.fn-execution.memory.managed has been changed to true and so managed
memory will be used by default for Python workers. In cases where Python UDFs
are used together with RocksDB state backend in streaming or built-in batch
algorithms in batch, the user can control how managed memory should be shared
between data processing (RocksDB state backend or batch algorithms) and Python,
by overwriting [managed memory consumer weights]({% link ops/mem_setup_tm.md
%}#consumer-weights). (was: In cases where Python UDFs are used together with
RocksDB state backend in streaming or built-in batch algorithms in batch, the
user can control how managed memory should be shared between data processing
(RocksDB state backend or batch algorithms) and Python, by overwriting [managed
memory consumer weights]({% link ops/mem_setup_tm.md %}#consumer-weights).)
> 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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