Fix memory growth from pathlib sys.intern in long-running processes#65706
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potiuk merged 3 commits intoapache:mainfrom Apr 25, 2026
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Fix memory growth from pathlib sys.intern in long-running processes#65706potiuk merged 3 commits intoapache:mainfrom
potiuk merged 3 commits intoapache:mainfrom
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This is COOL. Thanks @wjddn279 ! |
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Hi maintainer, this PR was merged without a milestone set.
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Backport successfully created: v3-2-testNote: As of Merging PRs targeted for Airflow 3.X In matter of doubt please ask in #release-management Slack channel.
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… processes (apache#65706) * Fix memory growth from pathlib sys.intern in long-running processes * fix supporting python version * fix logic (cherry picked from commit d14862c) Co-authored-by: Jeongwoo Do <48639483+wjddn279@users.noreply.github.com>
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… processes (apache#65706) * Fix memory growth from pathlib sys.intern in long-running processes * fix supporting python version * fix logic (cherry picked from commit d14862c) Co-authored-by: Jeongwoo Do <48639483+wjddn279@users.noreply.github.com>
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Problem?
After patching for #65121, I found the memory growth in
parsed = [sys.intern(str(x)) for x in rel.split(sep) if x and x != '.']statement in python Path library.memray-flamegraph-output-2026-04-12.13.14.12.556546.html
Cause?
pathlib.PurePath._parse_pathcallssys.internon every path component. In long-running Airflow processes (scheduler, dag-processor, triggerer, workers) each task run produces log paths containing unique identifiers (dag_id, run_id, task_id, try_number), and those interned components accumulate in the interpreter's intern dict for the lifetime of the process.Solution?
The exactly same issue was reported in python issue (python/cpython#119518). Python 3.14 removes the interning call from _parse_path entirely (python/cpython#123356), but Airflow supports earlier versions. This PR backports the 3.14 parsing logic as a small Path subclass (_PatchedPath) and routes log-folder/log-file initialization through it, so path components can be garbage-collected as ordinary mortal strings.
After applying this change and #65121, I confirmed that all memory leaks under the local executor are gone. This improvement should also carry over to other components that rely on logging (e.g. dag-processor, celery executor).
memray-flamegraph-output-2026-04-17 08:12:02.069120.html
Was generative AI tooling used to co-author this PR?
{pr_number}.significant.rst, in airflow-core/newsfragments. You can add this file in a follow-up commit after the PR is created so you know the PR number.