Performance Guide
=================

rapidgeo provides fast geographic computations through Rust implementations with Python bindings.

Algorithm Characteristics
--------------------------

Distance Calculations
~~~~~~~~~~~~~~~~~~~~~

**Euclidean Distance**
- Fastest option for flat-plane calculations
- Suitable for small geographic areas or projected coordinates
- Not accurate for geographic coordinates over long distances

**Haversine Distance** 
- Spherical Earth approximation
- Good balance of speed and accuracy for most use cases
- Less accurate at very long distances

**Vincenty Distance**
- Ellipsoidal Earth model (WGS84)
- Highest accuracy for geographic distances
- Slower than Haversine but more precise

Optimization Strategies
------------------------

Algorithm Selection
~~~~~~~~~~~~~~~~~~~

Choose algorithms based on your accuracy requirements:

- Use Euclidean for projected coordinates or small areas
- Use Haversine for general geographic distance calculations  
- Use Vincenty when millimeter accuracy is required

Batch Processing
~~~~~~~~~~~~~~~~

Process multiple items together when possible:

.. code-block:: python

   # More efficient
   distances = batch.pairwise_haversine(points)
   
   # Less efficient  
   distances = [haversine(p1, p2) for p1, p2 in zip(points[:-1], points[1:])]

Memory Management
~~~~~~~~~~~~~~~~~

For large datasets:

- Process data in chunks rather than loading everything into memory
- Use streaming operations when available
- Consider using NumPy arrays for better memory layout

Best Practices
--------------

- Profile your specific use case rather than assuming performance characteristics
- Choose precision levels appropriate for your data (5 vs 6 decimal places for polylines)
- Use parallel batch operations for large datasets when available