dianfu opened a new pull request #10453: [FLINK-14026][python] Manage the resource of Python worker properly URL: https://github.com/apache/flink/pull/10453 ## What is the purpose of the change *This pull request add support to manage the resource of the Python worker. The memory of the Python worker will be accounted as the managed memory and managed by Flink.* Note: This feature is still [voting](http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/VOTE-FLIP-88-PyFlink-User-Defined-Function-Resource-Management-tt35439.html). Just submit the PR for review to make sure we can commit this change before the code freeze of 1.10. (Should only be merged after vote passed) ## Brief change log - *Introduce configuration python.fn-execution.framework.memory.size and python.fn-execution.buffer.memory.size* - *At compile phase, request the required amount of memory for the Python worker* - *At runtime phase, reserve the required amount of memory for the Python worker from MemoryManager* ## Verifying this change This change added tests and can be verified as follows: - *Added test in PythonOptionsTest* ## Does this pull request potentially affect one of the following parts: - Dependencies (does it add or upgrade a dependency): (no) - The public API, i.e., is any changed class annotated with `@Public(Evolving)`: (yes) - The serializers: (no) - The runtime per-record code paths (performance sensitive): (no) - Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Yarn/Mesos, ZooKeeper: (no) - The S3 file system connector: (no) ## Documentation - Does this pull request introduce a new feature? (no) - If yes, how is the feature documented? (not applicable)
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