Case Studies
Search Autocomplete (Typeahead)
A precomputed trie serving ranked suggestions in under 100ms, built offline from aggregated query popularity.
Returning ranked suggestions as a user types, fast enough to feel instant — a good vehicle for testing whether a candidate reaches for a precomputed structure built offline, rather than ranking candidates live on every keystroke.
Think of the difference between a librarian who re-searches the shelves every time you ask a question, and one who spent last night writing out the answer to every question anyone's likely to ask, so that today she just hands you the card the instant you speak. Typeahead only works if you build the second librarian, not the first.
What this lesson covers
Serve suggestions from a trie built offline from query logs, each node caching its top-k completions so a keystroke is a prefix walk with no ranking at read time. Rebuild it periodically with an atomic swap, shard the trie in memory, and cache the hottest prefixes.
- Ranking candidates from scratch on every keystroke cannot feel instant; the expensive work has to happen before the keystroke.
- Show the top-k completions for a prefix, ranked by popularity, while still reflecting a term that is trending today.
- Separating the offline build path from the online serving path is the senior-level move; a prefix structure alone is mid-level.
- A trending prefix concentrates load on one node; know when a small cache in front earns its cost and when it is just complexity.
- Size shards by observed query volume per prefix range rather than assuming an alphabetic split is evenly loaded.
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The full lesson works the design through end to end — diagrams, trade-offs, and what interviewers expect at each level. About 16 minutes.
- Understanding the Problem
- Functional Requirements
- Non-Functional Requirements
- Capacity Estimation
- Core Entities
- API Interface
- High-Level Design
- Trade-offs
- Final Design
- Operations & Observability
- Level Expectations
- Follow-Up Questions
- Try It Yourself