Start by clarifying requirements and scale, then design a high-level architecture that separates write and read paths. Focus on data modeling for posts, full-text indexing, and geospatial indexing, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize how you would handle the 'hot' data (recent posts) differently from historical data, and discuss how to shard the search indexes to scale horizontally.
Ask about expected user base, post volume, search latency requirements, and consistency needs. Define functional and non-functional requirements.
Outline components: API servers, post storage, search indexes (full-text and geospatial), and caching. Consider write and read paths separately.
Choose databases for posts (e.g., wide-column for scalability), full-text search (e.g., inverted index), and geospatial (e.g., geohash or R-tree). Discuss sharding and replication.
Detail how full-text search works (tokenization, indexing, ranking) and geospatial search (proximity queries). Discuss indexing pipelines and query routing.
Discuss trade-offs: consistency vs. latency, cost of indexing, hot/cold data separation, and caching strategies. Mention monitoring and scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.