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A library for validating and transforming flat files

Project description

FlatForge

A generic, modular, and extensible library to validate flat files of fixed length or delimited format.

Features

  • Validate flat files against predefined schemas
  • Transform flat files from one format to another
  • Support for fixed-length and delimited file formats
  • Extensible rule system for validation and transformation
  • Global rules for cross-record validation
  • CLI interface for easy integration into workflows
  • Support for multi-section files (header, body, footer)
  • Comprehensive error reporting
  • Efficient processing of large files (>1GB) with chunked processing and progress reporting

Installation

pip install flatforge

Quick Start

Command Line Interface

# Validate a file against a schema
flatforge validate --config schema.yaml --input data.csv --output valid.csv --errors errors.csv

# Convert a file from one format to another
flatforge convert --config mapping.yaml --input data.csv --output converted.txt

# Process a large file with chunked processing and progress reporting
flatforge validate --config schema.yaml --input large_data.csv --output valid.csv --errors errors.csv --chunk-size 10000 --show-progress

Programmatic Usage

from flatforge.processors import ValidationProcessor
from flatforge.parsers import ConfigParser

# Parse the configuration
config_parser = ConfigParser.from_file("schema.yaml")
config = config_parser.parse()

# Create a processor
processor = ValidationProcessor(config)

# Process the file
result = processor.process("data.csv", "valid.csv", "errors.csv")
print(f"Processed {result.total_records} records with {result.error_count} errors")

# Process a large file in chunks with progress reporting
def update_progress(processed, total):
    print(f"Progress: {processed}/{total} records ({int(100 * processed / total)}%)")

result = processor.process_chunked(
    "large_data.csv", 
    "valid.csv", 
    "errors.csv", 
    chunk_size=10000, 
    progress_callback=update_progress
)
print(f"Processed {result.total_records} records with {result.error_count} errors")

Documentation

For detailed documentation, please refer to:

Changelog

See the CHANGELOG.md file for details on the changes made in each release.

Contributing

Contributions are welcome! Please see the CONTRIBUTING.md file for guidelines on how to contribute to this project.

Author

License

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

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