Start by clarifying functional and non-functional requirements, then outline a high-level architecture that separates write and read paths. Focus on data modeling for efficient time-range and geo-filtered queries, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize that search and order histories have different access patterns and retention needs, so consider separate storage solutions (e.g., Elasticsearch for search, a relational or NoSQL store for orders) and a unified query layer. Also, mention the importance of user privacy and data isolation.
Ask about scale (QPS, data volume), latency requirements, consistency needs, and whether history includes both search and order events. Confirm that users can only access their own data.
Propose a schema that captures user ID, timestamp, location (lat/long or geohash), and event details. Consider denormalization for read efficiency and partitioning by user ID and time.
Select appropriate databases: e.g., Cassandra for orders (time-series, high write throughput) and Elasticsearch for search history (full-text and geo queries). Use composite indexes on (user_id, timestamp) and geospatial indexes.
Define an API endpoint that accepts time range, location filters, and pagination. Implement query routing to the appropriate store, and consider caching frequent queries.
Discuss trade-offs: consistency vs. latency, cost of geo-indexing, and data retention. Explain how to scale with sharding, replication, and possibly a unified query service.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.