I started with Haversine for great-circle distance which was fine, but I fumbled a bit when they pushed on edge cases like antimeridian crossings.
Start by clarifying requirements: scale (number of locations, query rate), latency, accuracy, and whether updates are needed. Then propose a two-phase approach: first use a spatial index like a geohash or R-tree to narrow down candidates, then compute exact distances (e.g., Haversine) and select top k using a max-heap. Discuss trade-offs between precomputation, indexing, and distributed processing for scale.
Pro tip: Mention that for very large scale, you can precompute geohash prefixes and use a distributed key-value store or search engine like Elasticsearch with geo-point indexing, but always validate with real-world constraints like Earth's curvature and edge cases near poles or the antimeridian.
Ask about the number of locations, query throughput, latency requirements, and whether the dataset is static or dynamic. This determines the choice of index and architecture.
Explain that Haversine or Vincenty formula is needed for accurate geographic distances, but for small areas, Euclidean distance on projected coordinates may suffice. Mention trade-offs between accuracy and speed.
Propose using a spatial index such as R-tree, Quadtree, or Geohash to efficiently filter nearby candidates. For distributed systems, consider geohash-based sharding or using a search engine with geo capabilities.
Describe a two-step process: first, use the index to retrieve a set of candidate points within a bounding region (expanding if needed), then compute exact distances and use a max-heap of size k to find the k nearest.
Discuss horizontal scaling via sharding by geohash, caching frequent queries, and using approximate methods like k-d trees for lower latency. Mention trade-offs between precomputation, memory, and accuracy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.