billiard
Python multiprocessing fork with improvements and bugfixes
Decision gist · record as of 2026-08-14
Yes, if you are using Celery or need enhanced multiprocessing capabilities. billiard is production-stable, actively maintained, has no known vulnerabilities, and installs with zero friction. It's a dependency for Celery rather than a direct choice for most users, but if you need it, it's reliable and well-supported.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel distribution.
- Active maintenance with recent commits and no known vulnerabilities.
- Widely used in production as a Celery dependency.
License · maintenance · safety
BSD (permissive) — BSD license is permissive; you may use, modify, and distribute billiard freely in commercial and open-source projects with minimal restrictions.
last release 2025-11-30 (257 days) · last repo commit 2026-08-13 · 435 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 53,634,981 downloads/mo, #550 on PyPI
Alternatives
Verify before relying
pip install billiard
from billiard import Pool
with Pool(processes=4) as pool:
results = pool.map(function, iterable)- Whether billiard's improvements over standard multiprocessing remain relevant for current Python versions (3.8+)
- Performance characteristics compared to native multiprocessing in modern Python releases
What it is and what it does
billiard is a maintained fork of Python's multiprocessing module, originally created to backport improvements and fixes from newer Python versions and the Celery project. It provides enhanced process pool management, better handling of edge cases, and additional bug fixes beyond what the standard library offers. The package has no external runtime dependencies and installs as a pure-Python wheel.
It is primarily used as a dependency for Celery, the distributed task queue framework, where it handles worker process spawning and management. Developers working directly with billiard typically use it when they need more control over multiprocessing behavior or when integrating with Celery-based systems that depend on it.
Use it for
- Celery task queue workers that require enhanced process pool management and bug fixes
- Parallel task execution in applications needing more robust multiprocessing than the standard library
- Distributed computing setups where process lifecycle and communication need fine-grained control
- Legacy systems requiring multiprocessing backports for older Python versions
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are using Celery or need enhanced multiprocessing capabilities.
billiard is production-stable, actively maintained, has no known vulnerabilities, and installs with zero friction. It's a dependency for Celery rather than a direct choice for most users, but if you need it, it's reliable and well-supported.
Install
billiard on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Active maintenance with recent commits and no known vulnerabilities. Widely used in production as a Celery dependency.
License in practice
BSD license is permissive; you may use, modify, and distribute billiard freely in commercial and open-source projects with minimal restrictions.
Quickstart
pip install billiard
from billiard import Pool
with Pool(processes=4) as pool:
results = pool.map(function, iterable)Verify before relying
- Whether billiard's improvements over standard multiprocessing remain relevant for current Python versions (3.8+)
- Performance characteristics compared to native multiprocessing in modern Python releases
Package facts
| License | BSD permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 257 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 53,634,981 / month, #550 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: BSD LicenseOperating System :: Microsoft :: WindowsOperating System :: POSIXProgramming Language :: CProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Software Development :: Libraries :: Python ModulesTopic :: System :: Distributed Computing |
Evidence: billiard-4.2.4-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “multiprocessing fork python”
- billiardbilliard is a fork of Python's multiprocessing module with bug fixes…
- lokyLoky provides a reusable ProcessPoolExecutor that handles parallel…
- multiprocessing-loggingProvides a logging handler that tunnels log records from…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also multiprocess · celery · mpire · multiprocessing · loky · aiomultiprocess · multiprocessing-logging · aioprocessing · tqdm-multiprocess · django-q2