I jumped straight into the trie data structure and the interviewer kind of let me run with it before asking about scale.
Start by clarifying requirements and scale, then design a multi-tier system with a fast in-memory prefix tree (trie) for low-latency lookups and a distributed cache for hot queries. Discuss data collection, ranking, and trade-offs between latency, freshness, and cost.
Pro tip: Emphasize that typeahead is a read-heavy, latency-sensitive service, so precomputation and caching are critical; also mention that personalization and trending queries require a balance between static and dynamic data.
Ask about expected QPS, latency SLA, data freshness, personalization, and whether suggestions are global or user-specific. Estimate scale (e.g., billions of queries per day, millions of unique prefixes).
Propose a client-server architecture with a load balancer, stateless API servers, a caching layer (e.g., Redis), and a backend service that queries a precomputed trie or inverted index. Mention data collection pipeline for query logs.
Describe using a trie (prefix tree) for efficient prefix matching, with top-k suggestions stored at each node. Discuss storing the trie in memory for speed, and using a distributed cache for hot prefixes. Consider alternatives like finite state transducers for compression.
Explain how suggestions are ranked (e.g., by popularity, recency, user history). Discuss offline aggregation of query counts and online blending of personalized signals. Mention handling trending queries with real-time updates.
Discuss trade-offs: latency vs. freshness, memory vs. cost, global vs. personalized. Optimize with caching, sharding, and precomputation. Address failure modes and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.