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Inverted indexes, BM25 ranking, prefix tries, and the p99 < 100ms latency budget that drives every architectural choice.
Design a search system that serves two related queries: (1) full-text search over a document corpus (products, articles, jobs, code), returning ranked results in under 100ms p99; (2) autocomplete suggestions as the user types, returning the top 10 candidates in under 50ms p99. Both must handle continuous index updates as the underlying corpus changes.
This problem is graded on three things: the choice of inverted index structure and how it shards, the ranking pipeline (lexical → learned), and the latency engineering that makes p99 < 100ms achievable. Strong candidates open with the latency budget and design backwards from it.
Asking these before diving into a solution is the difference between a "hire" and a "no signal" rating. Pick the questions whose answers would change your design.
Capacity estimation · architecture with all 9 components explained · 6 deep dives · trade-off analysis · 8 common follow-up questions
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Reading is the floor. The interview signal is in walking through this live with someone probing follow-ups. Use the AI mock interview to practice talking through requirements, architecture, and trade-offs out loud.
Mock interview: design Search + Autocomplete →