evolvishub-datacleanup 0.1.0

A professional data cleanup management library

Evolvishub Data Cleanup Adapter

A Python library for managing data cleanup and archiving in Evolvis applications.

About

This project is developed and maintained by Evolvis.ai.

Author

Alban Maxhuni, PhD
Email: [e-mail]

Features

• Configurable data cleanup based on file age and size thresholds
• Support for both INI and YAML configuration files
• File system monitoring with automatic cleanup
• Configurable retention policies for different file types
• Automatic backup of cleaned files
• Cleanup of old backup files
• Comprehensive logging
• Thread-safe operations
• Asynchronous operations for better performance

Installation

pip install evolvishub-data-cleanup-adapter

Usage

1. Create a configuration file (INI or YAML):

# config.ini
[folders]
data_folder1 = /path/to/folder1
data_folder2 = /path/to/folder2

[thresholds]
max_size_gb = 1.0
max_age_days = 30

[backup]
directory = /path/to/backup
max_age_days = 90

[monitoring]
check_interval_seconds = 3600

[retention]
policy_log = 7
policy_temp = 1

2. Use the library in your code:

import asyncio
from evolvishub_datacleanup import DataCleanupManager

async def main():
    try:
        # Initialize the manager with your config file
        manager = DataCleanupManager('config.ini')

        # Start monitoring
        await manager.start_monitoring()

        # Example of concurrent operations
        cleanup_task = asyncio.create_task(manager.cleanup_old_files())
        backup_task = asyncio.create_task(manager.cleanup_backup_files())
        
        # Wait for both operations to complete
        await asyncio.gather(cleanup_task, backup_task)

        # Get file information
        file_info = await manager.get_file_info()
        total_size = await manager.get_total_size()

        # ... your application code ...

    except Exception as e:
        print(f"An error occurred: {e}")
    finally:
        # Ensure monitoring is stopped
        await manager.stop_monitoring()

if __name__ == "__main__":
    asyncio.run(main())

Async Usage Guide

Basic Async Operations

All operations in the library are asynchronous and should be used with await:

# Single operation
await manager.cleanup_old_files()

# Multiple sequential operations
await manager.cleanup_old_files()
await manager.cleanup_backup_files()

Concurrent Operations

You can run multiple operations concurrently using asyncio.gather():

# Run multiple operations concurrently
await asyncio.gather(
    manager.cleanup_old_files(),
    manager.cleanup_backup_files(),
    manager.get_file_info()
)

Error Handling

Always wrap async operations in try-except blocks:

try:
    await manager.start_monitoring()
except Exception as e:
    print(f"Failed to start monitoring: {e}")

Best Practices

1. Resource Management: Always ensure proper cleanup by using try-finally blocks:

   try:
       await manager.start_monitoring()
       # ... your code ...
   finally:
       await manager.stop_monitoring()
   

2. Concurrent Operations: Use asyncio.gather() for independent operations:

   results = await asyncio.gather(
       manager.get_total_size(),
       manager.get_file_info(),
       return_exceptions=True
   )
   

3. Cancellation: Handle task cancellation gracefully:

   try:
       async with asyncio.timeout(30):  # 30 second timeout
           await manager.cleanup_old_files()
   except asyncio.TimeoutError:
       print("Operation timed out")
   

4. Event Loop: Use asyncio.run() as the main entry point:

   if __name__ == "__main__":
       asyncio.run(main())
   

Configuration

INI Format

[folders]
folder1 = /path/to/folder1
folder2 = /path/to/folder2

[thresholds]
max_size_gb = 1.0
max_age_days = 30

[backup]
directory = /path/to/backup
max_age_days = 90

[monitoring]
check_interval_seconds = 3600

[retention]
policy_log = 7
policy_temp = 1

YAML Format

data_folders:
  - /path/to/folder1
  - /path/to/folder2

cleanup_thresholds:
  max_size_gb: 1.0
  max_age_days: 30

backup_settings:
  directory: /path/to/backup
  max_backup_age_days: 90

monitoring_settings:
  check_interval: 3600

retention_policies:
  .log:
    max_age_days: 7
  .tmp:
    max_age_days: 1

API Reference

DataCleanupManager

The main class for managing data cleanup operations.

manager = DataCleanupManager(config_path: Union[str, Path])

Methods

• async start_monitoring(): Start monitoring data folders
• async stop_monitoring(): Stop monitoring
• async cleanup_old_files(): Manually trigger cleanup
• async cleanup_backup_files(): Clean up old backup files
• async get_total_size(): Get total size of monitored folders
• async get_file_info(): Get information about all files

Development

1. Clone the repository:

git clone https://github.com/yourusername/evolvishub-datacleanup.git
cd evolvishub-datacleanup

2. Install development dependencies:

pip install -e ".[dev]"

3. Run tests:

pytest

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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