🏗️ DOD Architecture Alignment: Update nendb-python for Data-Oriented Design

[inagib21]

🎯 Goal: Align nendb-python with NenDB's Data-Oriented Design Architecture

Following the successful DOD migration in nen-db (which achieved 97% code reduction and 8x performance improvement), the Python language binding should be updated to leverage and complement the DOD architecture.

📋 Required Changes:

1. Python Binding Data Structure Optimization

• NumPy Array Integration: Use NumPy arrays for SoA data representation
• Memory-Aligned Buffers: Ensure Python buffers are aligned for optimal C interop
• Object Pooling: Implement object pools for frequently used Python objects
• Batch API Methods: Group multiple operations into efficient batch calls

2. Performance Enhancements

• Cython Optimization: Use Cython for performance-critical binding code
• Zero-Copy Data Transfer: Minimize memory copying between Python and C
• Vectorized Operations: Leverage NumPy vectorization for data processing
• Memory Views: Use memory views for efficient buffer management

3. Python-Specific DOD Features

• Pandas Integration: Efficient DataFrame-based batch operations
• Async Support: Async/await support for batch operations
• Generator APIs: Memory-efficient iteration over large result sets
• Context Managers: Proper resource management for batch operations

4. Data Science Integration

• NumPy Arrays: Native NumPy array support for graph data
• Pandas DataFrames: Direct DataFrame integration for batch operations
• Scikit-learn Compatibility: Efficient integration with ML pipelines
• Jupyter Notebook Support: Optimized display and interaction in notebooks

🚀 Expected Outcomes:

• API Performance: Significantly faster Python API operations
• Memory Efficiency: Reduced Python memory overhead through efficient C interop
• Data Science Workflow: Seamless integration with Python data science stack
• Developer Experience: Pythonic APIs that leverage DOD benefits

📖 Reference:

• See nen-db commit 5fb6d0b for DOD implementation examples
• Architecture: SoA layouts in src/memory/layout.zig
• Performance: SIMD operations in src/memory/simd.zig

🐍 Python-Specific Considerations:

• NumPy Integration: Efficient conversion between DOD SoA and NumPy arrays
• Memory Management: Minimize Python object creation and destruction
• Type Hints: Comprehensive type hints for better developer experience
• Error Handling: Pythonic error handling for batch operations

Priority: High (Primary language binding for data science applications)
Complexity: Medium-High (Requires optimization for Python's memory model)