memwatcher 0.1.0

Intelligent Memory Leak Detective for Python

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.

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

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details

Credits

Built by Yeakin Iqra

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