Geospatial

Answering "what is near me?" at scale: turning two-dimensional coordinates into one-dimensional keys a database can index, the geohash and quadtree grids, boundary-safe neighbor search, and pushing live positions to the right people as they move.

Explainers

• Design a Proximity Service — Open a maps app, tap "restaurants near me," and a few of the 200 million businesses on Earth come back in under a second. This is the retrieval shape under Yelp, "find nearby drivers," and every store locator — built from zero: why a plain index on latitude and longitude quietly falls apart, geohash derived by hand (recursive halving, prefix as zoom, base-32) with the two boundary traps that make a naive query silently miss results and the eight-neighbor fix that catches them, an honest geohash-vs-quadtree-vs-S2 deliberation, the compound-row data model, the read path and why the user’s own coordinates are a terrible cache key — then "when the points move": nearby-friends over a pub/sub channel per cell and a WebSocket fleet, honest about the write volume it costs.
• Design Google Maps — Two systems wearing the same skin: a pyramid of pre-drawn tiles you pan and zoom, and a road graph cut into tiles you route across. From the naive one-server cut to a static tile pyramid on a CDN, hierarchical routing tiles searched with a live A*, ~1M-QPS location ingestion, and live-traffic ETAs — drawn, computed, and animated.