Design a Search Autocomplete System
Last updated: September 4, 2025
Quick Overview
Build a query suggestion system with prefix matching, personalization, trending detection, and strict latency requirements under P99.
Perplexity
September 4, 20257
10
3,860 solved
Build a query suggestion system with prefix matching, personalization, trending detection, and strict latency requirements under P99.
Autocomplete is a key UX feature for search products. This tests real-time data structures and ranking under tight latency constraints.
What the Interviewer Expects
- Design efficient prefix matching with tries or inverted indexes
- Implement trending query detection with decay functions
- Add personalization based on user history
- Meet P99 latency under 50ms
- Blend ML-scored suggestions with rule-based boosting
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How do you handle offensive or dangerous query suggestions?
- How do you cold-start personalization for new users?
- What happens when trending queries spike suddenly?
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Practice System Design ProblemsSample Answer
Data Structure
Use a trie for prefix matching with frequency counts at each node. For scale, shard tries by first character or bigram. Complement with an inverted in...
Ranking and Personalization
Score suggestions by: (1) global popularity (query frequency with time decay), (2) trending boost (recent frequency spike detected via z-score), (3) p...
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