Merge "Add multiprocessing utilities with spawn start method"

This commit is contained in:
Zuul
2025-11-26 16:36:55 +00:00
committed by Gerrit Code Review
3 changed files with 223 additions and 0 deletions

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# Copyright (C) 2025 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""Utilities for multiprocessing using the ``spawn`` start method.
This module provides two helpers:
* ``get_spawn_context()`` — return a multiprocessing context configured to
use the ``spawn`` start method.
* ``get_spawn_pool()`` — create a ``Pool`` instance from this context.
These helpers allow oslo.service to safely use multiprocessing without relying
on the interpreter-wide ``set_start_method()``. This is a critical distinction:
Why not call ``set_start_method('spawn')`` globally?
---------------------------------------------------
``multiprocessing.set_start_method()`` can only be invoked **once per Python
interpreter**, and attempting to set it a second time will raise a
``RuntimeError``. More importantly, setting the method globally can interfere
with other components of OpenStack that also interact with multiprocessing,
including cotyledon, eventlet shims, or other subsystems that may require
different semantics.
Instead, we rely on ``multiprocessing.get_context('spawn')`` which returns a
*local, isolated multiprocessing context* that uses ``spawn`` **without**
modifying the global start method. This design is safer and avoids conflicts
between libraries while ensuring spawn-based semantics everywhere this module
is used.
Why ``spawn``?
--------------
Using ``spawn`` prevents deadlocks inherited from ``fork``, especially when
the parent process holds Python or C-level locks (e.g. logging locks, I/O
locks, or any lock held by threads). Forking while locks are held can result
in children inheriting these locked states and blocking indefinitely. This
issue is well-documented in:
* https://pythonspeed.com/articles/python-multiprocessing/
* The CPython issue tracker and Python documentation on multiprocessing.
By enforcing ``spawn`` in a controlled, local manner, oslo.service avoids these
deadlocks while remaining compatible with threaded runtimes and future
removal of eventlet.
Usage example:
--------------
.. code-block:: python
from oslo_service._multiprocessing import get_spawn_pool
# Safe spawn-based pool
with get_spawn_pool(processes=4) as pool:
results = pool.map(func, items)
This module performs no side-effects at import time and does not alter
global multiprocessing state.
"""
import multiprocessing
from multiprocessing.context import SpawnContext
from multiprocessing.pool import Pool
from collections.abc import Callable
__all__ = ("get_spawn_context", "get_spawn_pool")
def get_spawn_context() -> SpawnContext:
"""Get a multiprocessing context using the 'spawn' start method.
This function returns a multiprocessing context that uses the 'spawn'
start method, which avoids fork-inherited lock deadlocks by starting
fresh processes without inheriting locks from the parent process.
:returns: A multiprocessing context with start method set to 'spawn'
"""
return multiprocessing.get_context('spawn')
def get_spawn_pool(
processes: int | None = None,
initializer: Callable | None = None,
init_args: tuple = (),
max_tasks_per_child: int | None = None,
) -> Pool:
"""Get a multiprocessing pool created with the 'spawn' start method.
This function creates a multiprocessing pool using the 'spawn' start
method, which avoids fork-inherited lock deadlocks. All parameters are
passed through to the underlying Pool constructor.
:param processes: The number of worker processes to use. If None, the
Pool will use the number returned by
os.process_cpu_count()
(or os.cpu_count() on older Python versions).
:param initializer: If not None, each worker process will call
initializer(*init_args) when it starts.
:param init_args: Arguments to be passed to the initializer.
:param max_tasks_per_child: The number of tasks a worker process can
complete before it will exit and be replaced
with a fresh worker process, to enable unused
resources to be freed.
:returns: A multiprocessing pool created with the 'spawn' start method
"""
context = get_spawn_context()
return context.Pool(
processes=processes,
initializer=initializer,
initargs=init_args,
maxtasksperchild=max_tasks_per_child
)

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# Copyright (C) 2025 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import multiprocessing
import subprocess
import sys
from oslo_service import _multiprocessing
from oslo_service.tests import base
class TestMultiprocessing(base.ServiceBaseTestCase):
"""Test multiprocessing utilities."""
def test_get_spawn_context(self):
"""Test that get_spawn_context returns a context with spawn."""
context = _multiprocessing.get_spawn_context()
# Verify it's a spawn context
self.assertEqual(context.get_start_method(), 'spawn')
# Verify it's the correct type
self.assertIsInstance(context, multiprocessing.context.SpawnContext)
def test_get_spawn_pool(self):
"""Test that get_spawn_pool returns a pool with spawn start method."""
pool = None
try:
pool = _multiprocessing.get_spawn_pool(processes=1)
self.assertIsInstance(pool, multiprocessing.pool.Pool)
finally:
if pool:
pool.close()
pool.join()
def test_get_spawn_pool_with_parameters(self):
"""Test that get_spawn_pool accepts all standard pool parameters."""
pool = _multiprocessing.get_spawn_pool(
processes=1,
initializer=None,
init_args=(),
max_tasks_per_child=1
)
try:
# Verify the pool was created successfully
self.assertIsInstance(pool, multiprocessing.pool.Pool)
finally:
pool.close()
pool.join()
def test_spawn_avoids_lock_deadlock(self):
"""Test that spawn method avoids fork-inherited lock deadlocks."""
# This test demonstrates that spawn context is available
# The actual deadlock avoidance is demonstrated by the context creation
context = _multiprocessing.get_spawn_context()
self.assertEqual(context.get_start_method(), 'spawn')
# Test that we can create a pool (but don't use it to avoid hanging)
pool = None
try:
pool = _multiprocessing.get_spawn_pool(processes=1)
self.assertIsInstance(pool, multiprocessing.pool.Pool)
finally:
if pool:
pool.close()
pool.join()
def test_stdlib_multiprocessing_not_affected_by_import(self):
"""Test that oslo_service import doesn't affect stdlib multiprocessing.
This test ensures that importing oslo_service doesn't have side effects
on the stdlib multiprocessing module, specifically that it doesn't
implicitly import _multiprocessing.
"""
code = ("import oslo_service, sys; "
"print('_multiprocessing' in sys.modules)")
out = subprocess.check_output([sys.executable, "-c", code], text=True)
self.assertIn("False", out)

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---
features:
- |
Added `oslo_service._multiprocessing` module providing
``get_spawn_context()`` and ``get_spawn_pool()`` utilities.
These enforce the ``spawn`` start method to avoid
fork-inherited lock deadlocks, as explained in:
https://pythonspeed.com/articles/python-multiprocessing/