How Yelp and Uber Use Geohashing for Proximity Search

Proximity search on platforms like Yelp requires efficiently finding points near a given location among millions of entries. The solution involves converting 2D coordinates into 1D keys via geohashing, then querying adjacent cells to handle edge cases. Real systems like Uber's H3 use hexagonal grids for cleaner neighbor calculations, always pruning the search space before computing exact distances.