Start by clarifying functional and non-functional requirements, then design a scalable architecture that separates write and read paths. Use a geospatial indexing strategy (e.g., geohash or S2) for efficient nearest-neighbor queries, and employ a globally distributed database with strong consistency for writes and low-latency reads via caching and read replicas.
Pro tip: Emphasize the trade-off between consistency and latency: use synchronous replication for writes to ensure strong consistency, but serve reads from local replicas or caches to meet sub-100ms latency. Also, consider sharding by geographic region to distribute load and reduce cross-region traffic.
Ask questions to understand functional requirements (e.g., search radius, sorting, menu updates) and non-functional requirements (100M+ DAU, sub-100ms reads, strong consistency on writes). Estimate QPS and storage needs.
Outline components: API gateway, write service, read service, geospatial index, database, cache, and CDN. Separate read and write paths to optimize for different SLAs.
Design schemas for restaurants and menus. Choose a geospatial indexing technique (e.g., geohash, S2, or Quadtree) to efficiently query nearby restaurants. Discuss how to store and update location data.
Address global distribution: shard data by region, use a distributed database (e.g., Spanner, CockroachDB) for strong consistency on writes, and employ read replicas and caching for low-latency reads. Discuss trade-offs.
Detail caching strategies (e.g., Redis for hot data), CDN for static assets, and asynchronous replication for reads. Discuss how to handle consistency vs. latency and potential bottlenecks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.