Start by clarifying the requirements and scale of the search feature, then propose a high-level architecture that integrates with the existing store design. Focus on trade-offs between simplicity and performance, and explain how you would evolve the solution as data grows.
Pro tip: Mention that you would start with a simple database query-based search and only introduce a dedicated search engine like Elasticsearch when scale or feature complexity demands it, showing you optimize for simplicity and cost.
Ask about expected data volume, query types (exact match, fuzzy, filters), latency requirements, and whether search needs to be real-time. This ensures the solution aligns with actual needs.
Propose an architecture: either extend the existing database with indexes or introduce a dedicated search service. Explain how data flows from the store to the search index (e.g., via change data capture or batch sync).
Define a search endpoint that accepts query parameters, filters, and pagination. Discuss response format, error handling, and how to integrate with existing APIs.
Compare options: SQL LIKE queries vs. full-text search vs. external search engine. Discuss consistency, cost, operational overhead, and how to scale (sharding, replication, caching).
Outline how to measure search quality (relevance, latency) and iterate. Mention logging, metrics, and A/B testing for improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.