How Location Apps Like Yelp Find Nearby Places Across Millions of Records

Proximity search in apps like Yelp solves the challenge of efficiently finding nearby locations from tens of millions of data points spread across the globe. Standard database indexes are one-dimensional and cannot handle the two-dimensional nature of geographic proximity, making brute-force distance calculations impractical at scale. A common solution is geohashing, which encodes locations as strings where shared prefixes indicate nearby places, allowing ordinary string indexes to handle spatial queries. Databases like PostgreSQL with PostGIS offer dedicated spatial indexes such as R-trees, which partition space so queries only scan relevant regions. In all approaches, the strategy is the same: use a spatial index to narrow candidates quickly, then compute exact distances and apply filters like ratings or business hours on that smaller set.