Fix memory leak in remote logging connection cache#56695
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potiuk merged 1 commit intoapache:mainfrom Oct 16, 2025
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The remote logging connection cache was using `@lru_cache` with the API client instance as a parameter. This caused client references to be retained in the cache indefinitely, preventing garbage collection and causing memory leaks when tasks created multiple client instances. The new implementation ensures connection details are cached for performance while allowing client instances to be properly garbage collected after use.
potiuk
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You can attempt to backport this manually by running: cherry_picker 416c73e v3-1-testThis should apply the commit to the v3-1-test branch and leave the commit in conflict state marking After you have resolved the conflicts, you can continue the backport process by running: cherry_picker --continue |
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Looks like some earlier change needs to be cherry-picked first |
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The remote logging connection cache was using `@lru_cache` with the API client instance as a parameter. This caused client references to be retained in the cache indefinitely, preventing garbage collection and causing memory leaks when tasks created multiple client instances. The new implementation ensures connection details are cached for performance while allowing client instances to be properly garbage collected after use.
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The remote logging connection cache was using `@lru_cache` with the API client instance as a parameter. This caused client references to be retained in the cache indefinitely, preventing garbage collection and causing memory leaks when tasks created multiple client instances. The new implementation ensures connection details are cached for performance while allowing client instances to be properly garbage collected after use.
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The remote logging connection cache was using `@lru_cache` with the API client instance as a parameter. This caused client references to be retained in the cache indefinitely, preventing garbage collection and causing memory leaks when tasks created multiple client instances. The new implementation ensures connection details are cached for performance while allowing client instances to be properly garbage collected after use.
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The remote logging connection cache was using `@lru_cache` with the API client instance as a parameter. This caused client references to be retained in the cache indefinitely, preventing garbage collection and causing memory leaks when tasks created multiple client instances. The new implementation ensures connection details are cached for performance while allowing client instances to be properly garbage collected after use. (cherry picked from commit 416c73e)
TyrellHaywood
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The remote logging connection cache was using `@lru_cache` with the API client instance as a parameter. This caused client references to be retained in the cache indefinitely, preventing garbage collection and causing memory leaks when tasks created multiple client instances. The new implementation ensures connection details are cached for performance while allowing client instances to be properly garbage collected after use.
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vatsrahul1001
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)" This reverts commit 416c73e.
potiuk
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RoyLee1224
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* Revert "Fix memory leak in remote logging connection cache (apache#56695)" This reverts commit 416c73e. * enable e2e ui test to install pnpm if not installed
Copilot AI
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* Revert "Fix memory leak in remote logging connection cache (apache#56695)" This reverts commit 416c73e. * enable e2e ui test to install pnpm if not installed
itayweb
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* Revert "Fix memory leak in remote logging connection cache (apache#56695)" This reverts commit 416c73e. * enable e2e ui test to install pnpm if not installed
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The remote logging connection cache was using
@lru_cachewith the API client instance as a parameter. This caused client references to be retained in the cache indefinitely, preventing garbage collection and causing memory leaks when tasks created multiple client instances.The new implementation ensures connection details are cached for performance while allowing client instances to be properly garbage collected after use.
In Airflow 3.0.6 various tasks running on Celery failed with OOMs as the memory leaks were significant. After applying changes in this PR, the memory stayed mostly-flat and there were 0 task failures.
Celery Worker with 4 GB memory on Airflow 3.0.6
Celery Worker with 4 GB memory with the changes in this PR**
Part of #56641
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