beeperpurge 1.0.14

High-throughput parallel file system cleaner

BeeperPurge 🧹

High-throughput parallel file system cleaner designed for efficiently eliminating millions of old files as close to simultaneously as possible.

Features

• 🚀 Parallel processing with multi-threading
• 🎯 Precision targeting of files by age
• 🔍 Dry-run mode for operation verification
• 📝 Kubernetes-friendly JSON logging
• 🔒 Safe handling of sensitive file systems
• ⚙️ Configurable age thresholds
• 🐳 Production-ready container with security best practices

Installation

Using Docker (Recommended)

docker pull ghcr.io/RiveryIO/beeper-purge:latest

# Always verify targets first with dry run
docker run -v /path/to/clean:/data ghcr.io/RiveryIO/beeper-purge:latest \
    /data --dry-run --max-age-hours 36

# Execute purge operation
docker run -v /path/to/clean:/data ghcr.io/RiveryIO/beeper-purge:latest \
    /data --max-age-hours 36

Using pip

pip install beeper-purge

Usage

# Show help
beeperpurge --help

# Reconnaissance (dry run)
beeperpurge /path/to/clean --dry-run --max-age-hours 36

# Execute purge
beeperpurge /path/to/clean --max-age-hours 36 --workers 16

# Show version
beeperpurge --version

Operational Metrics

$ beeperpurge /data --dry-run
{
    "timestamp": "2024-11-02T10:15:30,123",
    "level": "INFO",
    "message": "Starting purge operation",
    "extra_fields": {
        "root_path": "/data",
        "dry_run": true,
        "max_workers": 16
    }
}
...
{
    "timestamp": "2024-11-02T10:15:35,456",
    "level": "INFO",
    "message": "Operation completed",
    "extra_fields": {
        "files_processed": 1000000,
        "files_targeted": 150000,
        "duration_seconds": 5.33,
        "elimination_rate": 187617
    }
}

Safety Protocols

• 🛡️ Dry-run mode for target verification
• 🔗 No symlink following
• 🚨 Comprehensive error handling
• 👤 Non-root container execution
• ✅ Extensive test coverage

Performance Specifications

Scalability

• Efficiently handles millions of files
• Memory usage scales linearly with worker count
• I/O optimized operations

Recommended Configurations

• Standard systems: 8-16 workers
• High-performance systems: 16-32 workers
• Adjust based on:
  • Available CPU cores
  • I/O capabilities
  • File system response times

Development

Setup

# Clone repository
git clone https://github.com/RiveryIO/BeeperPurge.git
cd beeperpurge

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

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

Testing

# Full test suite
pytest

# Coverage analysis
pytest --cov=beeper_purge

# Specific test execution
pytest tests/test_cleaner.py

Container Build

docker build -t beeper-purge .

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/enhancement)
3. Commit your changes (git commit -m 'Add enhancement')
4. Push to the branch (git push origin feature/enhancement)
5. Open a Pull Request

License

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