Designing Typeahead — Trie + Precomputed Top-K, Traced
Suggest as they type — in <100ms, at huge query volume
Typeahead / autocomplete must return ranked completions for a prefix on every keystroke. You can't run a search per keystroke at that volume, so the trick is to precompute: the answer for a prefix is already sitting at a node in a trie.
The architecture
- Trie (prefix tree) (see DSA → Trie): each node is a prefix; store the top-K completions cached at the node itself. A query is a walk down to the prefix node + return its list — no ranking at query time.
- Ranking from query logs: popularity is computed offline (aggregate the last N days of searches — the data-parallel/map-reduce pattern) and merged into the trie periodically. Query-time is read-only.
- Caching & sharding: hot prefixes live in memory; the trie is sharded by prefix across servers for scale.
Traced: user types "s" → "sy" → "sys"
- Each keystroke hits the node for that prefix; return its cached top-K (debounced client-side to cut requests).
- No search, no sort at request time — the heavy work happened offline.
The hard parts
- Storing top-K at every node is memory-heavy; only cache popular prefixes fully, recompute rare ones.
- Freshness vs cost: trending terms need the offline aggregation to run often enough.
- Personalization / typo-tolerance add layers (fuzzy match, per-user re-rank).
Takeaways
- Precompute: a trie with top-K cached per prefix node → query = a walk, not a search.
- Popularity ranked offline from query logs (map-reduce), merged in periodically.
- Cache hot prefixes, shard the trie; debounce on the client.
Re-authored for this guide; trie diagram hand-authored as SVG. Complements the "Designing Typeahead" problem page with a traced flow. See also: (DSA) Trie, Caching, Data Parallelism.
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