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

This guide helps you choose the right algorithms and approaches for your use case.

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

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

**Euclidean Distance**
- Treats coordinates as flat X,Y points
- Use for small geographic areas or projected coordinates
- Doesn't account for Earth's curvature

**Haversine Distance** 
- Assumes Earth is a sphere
- Good for most geographic distance calculations
- Less accurate at very long distances due to spherical approximation

**Vincenty Distance**
- Uses ellipsoidal Earth model (WGS84)
- More accurate for geographic distances
- More computational work than Haversine

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
--------------

- Choose the right algorithm for your accuracy needs
- Use batch operations when processing multiple items
- Process large datasets in chunks to manage memory
- Test performance with your actual data rather than making assumptions
- For polylines, use precision 5 unless you need sub-meter accuracy