pyfunceble-process-manager
The process manager library for and from the PyFunceble project.
Decision gist · record as of 2026-08-14
Yes. Low install friction, active maintenance, no known vulnerabilities, and a permissive license make it a safe choice. Install it if you need a structured multiprocess task queue with worker lifecycle hooks and dynamic scaling; the example code is clear and the API surface is straightforward. Skip it if you only need simple thread-based concurrency or if you prefer a heavier framework like Celery.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; multiprocessing on Windows may require wrapping manager code in `if __name__ == '__main__':`.
- Low friction: pure Python wheel with only setuptools as a runtime dependency.
- Actively maintained with recent commits; no known vulnerabilities.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2026-01-24 (202 days) · last repo commit 2026-07-20
0 known vulnerabilities (OSV.dev, 2026-08-14) · 106,748 downloads/mo, #12,643 on PyPI
Alternatives
Verify before relying
pip install pyfunceble-process-manager
from PyFunceble.ext.process_manager import ProcessManagerCore, WorkerCore
class MyWorker(WorkerCore):
def target(self, consumed):
return consumed.upper()
class MyManager(ProcessManagerCore):
STD_NAME = "my-manager"
WORKER_CLASS = MyWorker
manager = MyManager(max_workers=4, generate_output_queue=True)
manager.start()
manager.push_to_input_queue("hello")
manager.terminate(mode="soft")- Performance characteristics and throughput limits under typical workloads
- Whether dynamic scaling works reliably with dependent managers in production
- Behavior and recovery guarantees during process crashes or queue saturation
What it is and what it does
This package is a process manager library extracted from the PyFunceble project to enable multiprocess work distribution without requiring the full PyFunceble installation. It provides a framework for spawning and managing worker processes that consume tasks from input queues and produce results to output queues, with built-in support for lifecycle hooks (poweron, preflight, inflight, postflight, poweroff checks) and reserved control messages for graceful or immediate shutdown.
You define custom workers by subclassing WorkerCore and implementing a target() method, then wrap them in a ProcessManagerCore subclass to control the number of workers, queue connections, and scaling behavior. The manager handles process lifecycle, inter-process messaging, and queue routing, allowing you to chain multiple managers together for multi-stage data pipelines.
Use it for
- Build a data processing pipeline where one pool filters records and another pool writes results, with automatic worker scaling based on queue depth.
- Distribute CPU-bound validation tasks across multiple processes while collecting results in a shared output queue for downstream consumption.
- Implement a worker pool for I/O-bound operations (API calls, file reads) with graceful shutdown and preflight checks before each task.
- Chain multiple process managers to create a multi-stage ETL workflow with independent worker counts and dynamic up/down scaling.
- Process large datasets in parallel with custom filtering logic at each stage, using reserved control messages to coordinate shutdown across workers.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Low install friction, active maintenance, no known vulnerabilities, and a permissive license make it a safe choice. Install it if you need a structured multiprocess task queue with worker lifecycle hooks and dynamic scaling; the example code is clear and the API surface is straightforward. Skip it if you only need simple thread-based concurrency or if you prefer a heavier framework like Celery.
Install
pyfunceble-process-manager on PyPI
Before you install
Low friction: pure Python wheel with only setuptools as a runtime dependency. Actively maintained with recent commits; no known vulnerabilities.
Requires Python 3.8 or later; multiprocessing on Windows may require wrapping manager code in `if __name__ == '__main__':`.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install pyfunceble-process-manager
from PyFunceble.ext.process_manager import ProcessManagerCore, WorkerCore
class MyWorker(WorkerCore):
def target(self, consumed):
return consumed.upper()
class MyManager(ProcessManagerCore):
STD_NAME = "my-manager"
WORKER_CLASS = MyWorker
manager = MyManager(max_workers=4, generate_output_queue=True)
manager.start()
manager.push_to_input_queue("hello")
manager.terminate(mode="soft")Verify before relying
- Performance characteristics and throughput limits under typical workloads
- Whether dynamic scaling works reliably with dependent managers in production
- Behavior and recovery guarantees during process crashes or queue saturation
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release <4,>=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagesetuptools |
| Maintenance | Actively maintained 202 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 106,748 / month, #12,643 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/StableEnvironment :: ConsoleIntended Audience :: DevelopersLicense :: OSI ApprovedProgramming Language :: PythonProgramming Language :: Python :: 3Topic :: Internet |
Evidence: pyfunceble_process_manager-1.0.11-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 › “multiprocess task queue”
- pyfunceble-process-managerProvides a multiprocess task queue framework with worker pools,…
- multiprocessMultiprocess is an enhanced fork of Python's standard multiprocessing…
- tqdm-multiprocessDisplays multiple tqdm progress bars from worker processes in a…
Give your agent the search over MCP, or paste the wish link into any chat.
More Internet packages
Botocore provides low-level, data-driven access to Amazon Web Services APIs, serving as the foundation for the AWS CLI and boto3 libraries.
Install it if you need programmatic access to AWS services.
Provides an async client for AWS services using botocore and aiohttp, allowing you to call AWS APIs asynchronously within asyncio-based applications.
Install it if you need to call AWS services from async Python code; it is the standard way to do so.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides a platform-independent file locking mechanism to coordinate access to files across processes and threads.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides common Protocol Buffer message definitions used across Google Cloud APIs, enabling Python clients to interact with Google services.
See also bounded-pool-executor · mpire · multiprocess · tqdm-multiprocess · faktory · cotyledon · taskiq · loky · dvc-task · aioprocessing