English | 中文

logging_mp is a Python library specifically designed for multiprocessing support in logging.

It solves the common logging problems in multiprocessing environments, especially interleaved output and file writing conflicts. In spawn mode, logging_mp uses Monkey Patch technology to connect child processes to a central logging queue automatically.

1. ✨ Features

• ⚡ Support Multi-Processing & Thread: Fully compatible with threading modules. Child processes automatically send logs to the main process.
• 💻 Cross-Platform Support: Works seamlessly with both fork (Linux) and spawn (Windows/macOS) start methods.
• 🎨 Rich Integration: Beautiful, colorized console output powered by Rich.
• 📂 File Logging: Aggregates logs from all processes and threads into timestamped log files with size-based rollover and count-based cleanup.
• 🔒Thread Safe: Fully compatible with threading modules.

2. 🛠️ Installation

2.1 from source

git clone https://github.com/silencht/logging-mp
cd logging_mp
pip install -e .

2.2 from PyPI

pip install logging-mp

3. 🚀 Quick Start

Using logging_mp feels very close to using the standard logging module. You only need one initialization step in the main process entry point.

3.1 Basic Example

Initialize the logging system in your entry point script (for example, main.py) before creating any processes.

import multiprocessing
import time

import logging_mp
# Call basicConfig before creating any processes or importing submodules that create loggers.
# In spawn mode, this automatically starts the listener process and applies the required monkey patch.
logging_mp.basicConfig(
    level=logging_mp.INFO, 
    console=True, 
    file=True,
    file_path="logs",
    backup_count=10,
    max_file_size=100 * 1024 * 1024
)
# Get a logger
logger_mp = logging_mp.getLogger(__name__)

def worker_task(name):
    # In the child process, just get a logger and write logs.
    # No manual queue or listener setup is needed.
    worker_logger_mp = logging_mp.getLogger("worker")
    worker_logger_mp.info(f"👋 Hello from {name} (PID: {multiprocessing.current_process().pid})")
    time.sleep(0.5)

if __name__ == "__main__":
    logger_mp.info("🚀 Starting processes...")
    
    processes = []
    for i in range(3):
        p = multiprocessing.Process(target=worker_task, args=(f"Worker-{i}",))
        p.start()
        processes.append(p)
        
    for p in processes:
        p.join()
    
    logger_mp.info("✅ All tasks finished.")

3.2 Configuration Options

The basicConfig method accepts the following arguments:

Argument	Type	Default	Description
level	int	logging_mp.WARNING	The global logging threshold (e.g., INFO, DEBUG).
console	bool	True	Enable/Disable Rich console output.
file	bool	False	Enable/Disable writing to a log file.
file_path	str	"logs"	Directory to store log files.
backup_count	int	10	Maximum number of timestamped log files to keep.
max_file_size	int	100*1024*1024	Maximum size in bytes of a single timestamped log file. Once exceeded, logging continues in a new timestamped file like example_20260324_153000_123456.log.

3.3 More Examples

See the example directory for a complete runnable example.

4. 📂 Directory Structure

.
├── example
│   ├── example.py             # Complete usage demonstration
│   ├── module_a
│   │   ├── module_b
│   │   └── worker_ta.py       # Example worker module
│   └── module_c
│       └── worker_tc.py       # Example worker module
├── src
│   └── logging_mp
│       └── __init__.py        # Core library implementation
├── LICENSE
├── pyproject.toml
└── README

5. 🧠 How It Works

The standard Python logging library is thread-safe, but it is not designed for multiprocessing by default. logging_mp uses a queue-based architecture so that multi-threading support is preserved while multi-process logging conflicts are handled centrally:

• Centralized Listening: When the main process starts, the library creates a dedicated background process named _logging_mp_queue_listener. This single consumer receives records from the queue and performs Rich console output or file writing in one place.
• Transparent Injection: To keep the user-facing API simple, the library patches multiprocessing.Process on import. In spawn mode, the log queue is injected during child process bootstrap (_bootstrap), so child processes can send logs back immediately after startup.
• Threads And Processes:
  • Threads: It keeps the thread-safety behavior of the standard logging module. Thread logs do not need cross-process communication, so the overhead stays low.
  • Processes: In each child process, logger.info() acts as a producer. Records are sent to a cross-process queue first, while console output and file I/O are handled by the listener process. This greatly reduces logging-related blocking in normal use, though it is not a strict zero-blocking system.
• Linear Ordering: Logs from all processes and threads ultimately converge into a single in-memory queue. The listener processes them in receive order, which avoids interleaved output and multi-process file writing conflicts.

6. ⚠️ Notes

• Import Order: In multiprocessing environments using spawn mode, ensure that you import logging_mp and call basicConfig before creating any Process objects.

• Windows/macOS: Because these platforms use spawn, always place process-starting code inside an if __name__ == "__main__": block. Otherwise, recursive startup errors may occur.

• Process Subclassing: If you create processes by subclassing multiprocessing.Process and override __init__, be sure to call super().__init__().

• Shutdown Semantics: The library shuts down its listener automatically at process exit. If the program is terminated abruptly, the last few log records may still be lost.

7. 📄 License

This project is licensed under the MIT License - see the LICENSE file for details.