bulkflow 0.1.0

A high-performance CSV to PostgreSQL data loader with chunked processing and error handling

BulkFlow

A high-performance Python package for efficiently loading large CSV datasets into PostgreSQL databases. Features chunked processing, automatic resume capability, and comprehensive error handling.

Key Features

• 🚀 High Performance: Optimized chunk-based processing for handling large datasets efficiently
• 🔄 Resume Capability: Automatically resume interrupted imports from the last successful position
• 🛡️ Error Resilience: Comprehensive error handling with detailed logging and failed row tracking
• 🔍 Data Validation: Preview data before import and validate row structure
• 📊 Progress Tracking: Real-time progress updates with ETA and processing speed
• 🔄 Duplicate Handling: Smart handling of duplicate records
• 🔌 Connection Pooling: Efficient database connection management
• 📝 Detailed Logging: Comprehensive logging of all operations and errors

Installation

pip install bulkflow

Quick Start

1. Create a database configuration file (db_config.json):

{
    "dbname": "your_database",
    "user": "your_username",
    "password": "your_password",
    "host": "localhost",
    "port": "5432"
}

2. Run the import:

python -m bulkflow.main path/to/your/file.csv your_table_name

Project Structure

bulkflow/
├── src/
│   ├── models/          # Data models
│   ├── processors/      # Core processing logic
│   ├── database/        # Database operations
│   └── utils/          # Utility functions

Usage Examples

Basic Usage

from bulkflow import process_file

db_config = {
    "dbname": "your_database",
    "user": "your_username",
    "password": "your_password",
    "host": "localhost",
    "port": "5432"
}

process_file(file_path, db_config, table_name)

CLI Usage

# Basic usage
python -m bulkflow.main data.csv target_table

# Custom config file
python -m bulkflow.main data.csv target_table --config my_config.json

Error Handling

BulkFlow provides comprehensive error handling:

1. Failed Rows File: failed_rows_YYYYMMDD_HHMMSS.csv

   • Records individual row failures
   • Includes row number, content, error reason, and timestamp

2. Import State File: import_state.json

   • Tracks overall import progress
   • Enables resume capability
   • Records failed chunk information

Performance Optimization

BulkFlow automatically optimizes performance by:

• Calculating optimal chunk sizes based on available memory
• Using connection pooling for database operations
• Implementing efficient duplicate handling strategies
• Minimizing memory usage through streaming processing

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

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

License

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

Acknowledgments

• Inspired by the need for robust, production-ready data import solutions
• Built with modern Python best practices
• Designed for real-world use cases and large-scale data processing

Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue if your problem isn't already listed
3. Provide as much context as possible in your issue description

Author

Created and maintained by Chris Willingham