MemWatcher

Intelligent Memory Leak Detective for Python

MemWatcher is a lightweight, easy-to-use Python library for detecting memory leaks in your applications. It monitors memory usage in real-time, analyzes patterns, and alerts you to potential leaks before they become critical issues.

Table of Contents

• Features
• Quick Start
  • Installation
  • Basic Usage
  • Using Decorators
  • Context Manager
• Example Report
• Use Cases
• Advanced Configuration
• Documentation
• Running Tests
• Contributing
• License
• Credits

Features

• Lightweight & Fast - Minimal overhead, runs in background thread
• Smart Detection - Statistical algorithms detect real leaks, not just growth
• Beautiful Reports - Human-readable reports with actionable insights
• Simple API - Decorators, context managers, or manual control
• Framework Ready - Works with Django, FastAPI, Flask, and more
• Real-time Monitoring - Continuous monitoring with customizable intervals
• Configurable - Thresholds, sensitivity, callbacks - all customizable

Quick Start

Installation

pip install memwatcher

Basic Usage

from memwatcher import MemoryWatcher

# Start monitoring
watcher = MemoryWatcher(interval=5.0)
watcher.start()

# Your application code here
# ...

# Stop and get report
watcher.stop()
report = watcher.get_report()
print(report)

Using Decorators

from memwatcher import watch_memory, detect_leaks

@watch_memory(interval=1.0)
def process_large_dataset():
    # Your code here
    pass

@detect_leaks(sensitivity=0.1)
def long_running_task():
    # Your code here
    pass

Context Manager

from memwatcher import MemoryWatcher

with MemoryWatcher(interval=2.0) as watcher:
    # Your code here
    pass

# Report automatically generated
report = watcher.get_report()

Example Report

============================================================
MEMORY WATCHER REPORT
============================================================

Duration: 45.2s
Snapshots: 9

Memory Usage:
  Start:  145.23 MB
  End:    289.67 MB
  Change: +144.44 MB
  Peak:   289.67 MB
  Min:    145.23 MB

Leak Detection:
  Status: ⚠️  LEAK DETECTED
  Severity: MEDIUM
  Confidence: 87.3%
  Growth Rate: 3.197 MB/min
  Total Increase: 144.44 MB

Recommendation: Warning: Potential memory leak detected. Monitor closely.
============================================================

Use Cases

• Development: Catch leaks during development before they hit production
• Testing: Add memory checks to your test suite
• Production: Lightweight monitoring in production environments
• CI/CD: Automated leak detection in your pipeline
• Profiling: Quick memory profiling for specific functions

Advanced Configuration

from memwatcher import MemoryWatcher

watcher = MemoryWatcher(
    interval=5.0,              # Snapshot every 5 seconds
    threshold_mb=500.0,        # Alert if exceeds 500MB
    enable_tracemalloc=True,   # Detailed tracking (higher overhead)
    callback=my_alert_function,# Custom callback on leak detection
    max_snapshots=100          # Keep last 100 snapshots
)

Documentation

Full documentation coming soon!

For now, check out the examples/ directory for more usage patterns.

Running Tests

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=memwatcher --cov-report=html

Contributing

We welcome contributions from the community! Whether you're fixing bugs, adding features, or improving documentation, your help is appreciated.

How to Contribute

1. Fork the Repository

   • Click the "Fork" button at the top right of this repository

2. Clone Your Fork

   git clone https://github.com/your-username/memwatcher.git
   cd memwatcher
   

3. Create a Branch

   git checkout -b feature/your-feature-name
   

4. Set Up Development Environment

   # Install in development mode with all dependencies
   pip install -e ".[dev]"
   

5. Make Your Changes

   • Write clean, readable code
   • Follow PEP 8 style guidelines
   • Add tests for new features
   • Update documentation as needed

6. Run Tests

   # Run all tests
   pytest
   
   # Run with coverage
   pytest --cov=memwatcher --cov-report=html
   

7. Commit Your Changes

   git add .
   git commit -m "feat: add your feature description"
   

   Follow Conventional Commits format:

   • feat: for new features
   • fix: for bug fixes
   • docs: for documentation changes
   • test: for test additions/changes
   • refactor: for code refactoring

8. Push to Your Fork

   git push origin feature/your-feature-name
   

9. Submit a Pull Request

   • Go to the original repository
   • Click "New Pull Request"
   • Provide a clear description of your changes

Reporting Issues

Found a bug or have a feature request? Please open an issue with:

• Clear description of the problem or suggestion
• Steps to reproduce (for bugs)
• Expected vs actual behavior
• Your environment (Python version, OS, etc.)

Code of Conduct

Please be respectful and constructive in all interactions. We're here to build something great together!

License

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

Credits

Built with ❤️ by Yeakin Iqra

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Star us on GitHub if MemWatcher helps you catch those sneaky memory leaks!