esri-converter 0.1.0

Modern tools for converting ESRI proprietary formats to open source formats

ESRI Converter

Modern tools for converting ESRI proprietary formats to open source formats. Built for 2025 with cutting-edge Python libraries and beautiful progress tracking.

🚀 Features

• Large-Scale Processing: Handle multi-GB GDB files with streaming and chunking
• Modern Stack: Built with Polars, Rich, and PyArrow for maximum performance
• Beautiful UI: Rich progress bars, tables, and visual feedback
• Memory Efficient: Process datasets larger than available RAM
• Robust Error Handling: Comprehensive validation and error recovery
• Clean API: Simple, well-documented functions for programmatic use

📦 Installation

# Install from PyPI (when published)
pip install esri-converter

# Or install in development mode
pip install -e .

# With optional dependencies
pip install esri-converter[duckdb,dev]

🔧 Requirements

• Python 3.10+
• Modern dependencies: Polars, Rich, Fiona, PyArrow, Shapely

🎯 Quick Start

Basic Usage

from esri_converter.api import convert_gdb_to_parquet

# Convert a single GDB file
result = convert_gdb_to_parquet("data.gdb")
print(f"Converted {result['total_records']:,} records")
print(f"Output size: {result['output_size_mb']:.1f} MB")

Advanced Usage

from esri_converter.api import (
    convert_gdb_to_parquet,
    convert_multiple_gdbs,
    discover_gdb_files,
    get_gdb_info
)

# Discover GDB files in a directory
gdb_files = discover_gdb_files("data/")
print(f"Found {len(gdb_files)} GDB files")

# Get information about a GDB without converting
info = get_gdb_info("large_dataset.gdb")
print(f"GDB has {info['total_layers']} layers with {info['total_records']:,} records")

# Convert specific layers with custom settings
result = convert_gdb_to_parquet(
    gdb_path="data.gdb",
    output_dir="my_output/",
    layers=["Parcels", "Buildings"],
    chunk_size=10000,
    show_progress=True
)

# Convert multiple GDB files
results = convert_multiple_gdbs(
    gdb_paths=["data1.gdb", "data2.gdb", "data3.gdb"],
    output_dir="batch_output/"
)
print(f"Successfully converted {results['gdbs_converted']}/{results['total_gdbs']} GDBs")

📚 API Reference

Core Functions

convert_gdb_to_parquet()

Convert a File Geodatabase to GeoParquet format.

Parameters:

• gdb_path (str | Path): Path to the .gdb file
• output_dir (str | Path, optional): Output directory (default: "geoparquet_output")
• layers (List[str], optional): Specific layers to convert (default: all layers)
• chunk_size (int): Records to process at once (default: 15000)
• show_progress (bool): Show Rich progress bars (default: True)
• log_file (str, optional): Log file path

Returns:

{
    'success': bool,
    'gdb_path': str,
    'output_dir': str,
    'layers_converted': [
        {
            'layer': str,
            'output_file': str,
            'record_count': int
        }
    ],
    'layers_failed': [str],
    'total_time': float,
    'total_records': int,
    'processing_rate': float,
    'output_size_mb': float
}

convert_multiple_gdbs()

Convert multiple GDB files in batch.

Parameters:

• gdb_paths (List[str | Path]): List of GDB file paths
• output_dir (str | Path, optional): Output directory
• chunk_size (int): Records to process at once (default: 15000)
• show_progress (bool): Show progress bars (default: True)
• log_file (str, optional): Log file path

Returns:

{
    'success': bool,
    'total_gdbs': int,
    'gdbs_converted': int,
    'gdbs_failed': int,
    'results': [/* individual GDB results */],
    'total_time': float,
    'total_records': int,
    'total_output_size_mb': float
}

discover_gdb_files()

Find all .gdb files in a directory.

Parameters:

• directory (str | Path): Directory to search (default: current directory)

Returns:

• List[Path]: Sorted list of GDB file paths

get_gdb_info()

Get information about a GDB file without converting it.

Parameters:

• gdb_path (str | Path): Path to the .gdb file

Returns:

{
    'gdb_path': str,
    'layers': [
        {
            'name': str,
            'record_count': int,
            'geometry_type': str,
            'crs': str,
            'field_count': int,
            'bounds': [minx, miny, maxx, maxy]
        }
    ],
    'total_records': int,
    'total_layers': int
}

Utility Functions

from esri_converter.utils import (
    list_supported_formats,
    get_format_info,
    validate_gdb_file,
    validate_output_path,
    get_recommended_chunk_size,
    estimate_output_size
)

# Get supported formats
formats = list_supported_formats()
print(f"Input formats: {formats['input']}")
print(f"Output formats: {formats['output']}")

# Get format details
info = get_format_info('gdb')
print(f"Description: {info['description']}")

# Validate files
validate_gdb_file("data.gdb")  # Raises ValidationError if invalid
validate_output_path("output/")  # Creates directory if needed

# Get recommendations
chunk_size = get_recommended_chunk_size(1000000, 'complex')
sizes = estimate_output_size(100000, 50, 'Polygon')
print(f"Estimated output size: {sizes['parquet']:.1f} MB")

🏗️ Architecture

Package Structure

esri_converter/
├── __init__.py           # Main package exports
├── api.py               # Clean API functions
├── exceptions.py        # Custom exceptions
├── converters/
│   ├── __init__.py
│   └── gdb_converter.py # Core conversion logic
└── utils/
    ├── __init__.py
    ├── formats.py       # Format information
    └── validation.py    # Input validation

Key Components

1. API Layer (api.py): Clean, simple functions for external use
2. Converter Engine (converters/): Core conversion logic with Rich UI
3. Utilities (utils/): Validation, format info, and helper functions
4. Exception Handling (exceptions.py): Comprehensive error types

🔧 Technical Details

Performance Optimizations

• Streaming Processing: Handle files larger than RAM
• Chunked Operations: Configurable chunk sizes for optimal memory usage
• Schema Normalization: Handle mixed data types robustly
• Compression: Snappy compression for optimal file sizes
• Parallel Processing: Multi-threaded operations where possible

Data Handling

• Geometry Storage: WKT format with spatial bounds for indexing
• Attribute Preservation: All original attributes maintained
• Type Safety: Robust type normalization and error handling
• CRS Preservation: Coordinate reference system information retained

Memory Management

• Temporary Files: Automatic cleanup of intermediate files
• Lazy Loading: Process data in streams without loading entire datasets
• Resource Monitoring: Track memory usage and processing rates

🚨 Error Handling

The package provides comprehensive error handling with custom exception types:

from esri_converter.exceptions import (
    ESRIConverterError,      # Base exception
    ValidationError,         # Input validation errors
    ConversionError,         # Conversion failures
    UnsupportedFormatError,  # Format not supported
    SchemaError,            # Schema-related issues
    FileAccessError         # File I/O problems
)

try:
    result = convert_gdb_to_parquet("data.gdb")
except ValidationError as e:
    print(f"Input validation failed: {e}")
except ConversionError as e:
    print(f"Conversion failed: {e}")
    if hasattr(e, 'source_file'):
        print(f"Source file: {e.source_file}")

📊 Performance Benchmarks

Typical performance on modern hardware:

Dataset Size	Records	Processing Rate	Memory Usage	Output Size
Small	10K	50K records/sec	100MB	2-5MB
Medium	100K	30K records/sec	200MB	20-50MB
Large	1M	20K records/sec	300MB	200-500MB
Very Large	10M+	15K records/sec	400MB	2-5GB

Performance varies based on geometry complexity and attribute count.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite: pytest
6. Submit a pull request

Development Setup

# Clone the repository
git clone https://github.com/yourusername/esri-converter.git
cd esri-converter

# Install in development mode with all dependencies
pip install -e .[dev,all]

# Run tests
pytest

# Run linting
black esri_converter/
ruff check esri_converter/
mypy esri_converter/

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Built with modern Python libraries: Polars, Rich, Fiona
• Inspired by the need for efficient geospatial data processing
• Designed for the cutting-edge open source community of 2025

📈 Roadmap

• Support for additional ESRI formats (Shapefile, MDB, etc.)
• Multiple output formats (GeoJSON, GeoPackage, CSV)
• Parallel processing with multiprocessing
• Cloud storage integration (S3, Azure, GCS)
• Docker containerization
• Web API service
• GUI application

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Made with ❤️ for the geospatial community