---
id: pyfunceble-process-manager
version: "1.0.11"
license: Apache 2.0
license_treatment: permissive
maintenance: active
---

# pyfunceble-process-manager — The process manager library for and from the PyFunceble project.

License: permissive · Maintenance: active · Downloads: 106.7K/mo

## 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 above — verify before relying.

Provides a multiprocess task queue framework with worker pools, inter-process messaging, and dynamic scaling for distributing work across processes.

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

pip install pyfunceble-process-manager
uv add pyfunceble-process-manager
poetry add pyfunceble-process-manager

## Installing pyfunceble-process-manager

Before you install: Low friction: pure Python wheel with only setuptools as a runtime dependency. Actively maintained with recent commits; no known vulnerabilities.

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")

Requires Python 3.8 or later; multiprocessing on Windows may require wrapping manager code in `if __name__ == '__main__':`.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 106.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

multiprocess task queue, worker pool manager, process pool framework, inter-process communication, parallel task distribution, dynamic worker scaling, process management library, multiprocessing, task-queue, worker-pool

[View on SkillFed](https://skillfed.io/packages/pyfunceble-process-manager) · [View on PyPI](https://pypi.org/project/pyfunceble-process-manager/)