Spent probably too long on the trie data structure angle before they nudged me toward scale considerations.
Start by clarifying requirements (scale, latency, personalization, data freshness) and then walk through a high-level design covering data collection, indexing, query serving, and ranking. Emphasize trade-offs between latency, cost, and relevance, and discuss how to handle scale and failures.
Pro tip: Proactively mention how you would measure success (e.g., CTR, latency percentiles) and iterate, showing product sense and operational maturity. Also, highlight the importance of handling typos and personalization without sacrificing speed.
Ask about expected QPS, latency SLA, data sources (queries, products, user behavior), personalization needs, and freshness requirements. This ensures the design meets actual business needs.
Outline components: data ingestion (logs, catalog), offline/online indexing pipeline, query service, cache, and ranking layer. Sketch how a user query flows through the system.
Discuss how to build and maintain the suggestion index: use tries, inverted indices, or n-gram models; incorporate popularity, personalization, and freshness. Explain offline batch vs. real-time updates.
Detail the online query path: prefix matching, candidate retrieval, ranking (e.g., by popularity, personalization, business rules), and returning top-K results within latency budget.
Address sharding, replication, caching, and fallbacks. Discuss trade-offs (e.g., latency vs. freshness, cost vs. relevance) and how to monitor performance and quality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.