pytemporal 1.3.13

High-performance bitemporal timeseries update processor

High-performance bitemporal data processing for Python

PyTemporal is a Rust-powered library for processing bitemporal timeseries data with world-class performance (157,000+ rows/second). Perfect for financial services, audit systems, and applications requiring immutable data trails with both business and system time dimensions.

Quick Start

# Install from source
git clone <your-repo>
cd pytemporal
uv run maturin develop --release

import pandas as pd
from pytemporal import BitemporalTimeseriesProcessor

# Initialize processor
processor = BitemporalTimeseriesProcessor(
    id_columns=['id'],
    value_columns=['price']
)

# Process temporal updates
result = processor.process_updates(
    current_state=current_df,
    updates=updates_df, 
    system_date='2025-01-27'
)

print(f"Updated {len(result.to_insert)} records")

Key Features

• 🚀 World-Class Performance: 157,000+ rows/second throughput
• 🔄 Bitemporal Processing: Track both business time and system time
• 🐍 Python-First: High-level DataFrame API with pandas integration
• ⚡ Zero-Copy: Apache Arrow columnar format for memory efficiency
• 🔧 Flexible Schema: Configure ID and value columns dynamically
• 🎯 Two Update Modes: Delta updates or full state replacement
• 🏗️ Production Ready: Comprehensive test coverage and clean architecture

Documentation

• 📚 API Reference - Complete API documentation
• 🎯 Usage Examples - Practical code examples
• ⚡ Performance Guide - Benchmarks and optimization tips

What is Bitemporal Data?

Bitemporal data tracks two time dimensions:

• Effective Time: When events occurred in the real world
• As-Of Time: When information was recorded in the system

This enables powerful queries like "What did we think the price was on Jan 15th, as of Jan 20th?"

Use Cases

• Financial Services: Price histories, portfolio valuations, risk calculations
• Audit Systems: Immutable change tracking with full reconstruction capability
• Regulatory Compliance: Time-accurate reporting for compliance requirements
• Data Warehousing: Slowly changing dimensions with full history preservation

Performance

Dataset Size	Processing Time	Throughput	Memory
800k × 80 cols	5.4 seconds	157k rows/sec	~14GB
100k × 20 cols	0.6 seconds	167k rows/sec	~2GB

Benchmarked on modern hardware with optimized settings

Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│ Python DataFrame│───▶│ PyTemporal (Rust)│───▶│ Processed Results│
│ (Pandas)        │    │ • Arrow Columnar │    │ (DataFrame)     │  
│                 │    │ • Parallel Proc  │    │                 │
└─────────────────┘    │ • Timeline Logic │    └─────────────────┘
                       └──────────────────┘

Built With:

• Rust: Core processing engine for maximum performance
• Apache Arrow: Columnar data format for zero-copy operations
• PyO3: Seamless Rust-Python integration
• Rayon: Data parallelism for multi-core performance

Development

# Run tests
cargo test                                    # Rust tests
uv run python -m pytest tests/ -v           # Python tests

# Performance benchmarks  
cargo bench                                  # Detailed benchmarks
uv run python validate_refactoring.py       # End-to-end validation

# Build release
uv run maturin develop --release

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make changes and add tests
4. Run the test suite: cargo test && uv run pytest
5. Submit a pull request

License

[Your License Here]

Acknowledgments

Built with modern Rust performance engineering and extensive profiling to achieve world-class bitemporal processing speeds while maintaining clean, maintainable code.