IntermediateSystem-Design
30 min
Design Search Autocomplete
SearchIRCachingML
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Interview Question
Design an autocomplete service that suggests queries as users type, with personalization and typo tolerance.
Key Points to Cover
- Data structures: tries/prefix trees, FSTs, n-gram indexes
- Signals: popularity, personalization, context, seasonality
- Typo tolerance: edit distance, phonetic hashing
- Latency: in-memory shards, cache warmers, precompute
- Feedback loops: click/log-based re-ranking
Evaluation Rubric
Efficient prefix and typo-tolerant indexing25% weight
Signal-based ranking and personalization25% weight
Low-latency serving path and caching25% weight
Feedback loops and evaluation25% weight
Hints
- 💡Keep P99 under ~50ms at peak.
Common Pitfalls to Avoid
- ⚠️**Ignoring User-Specific Context:** Relying solely on global popularity for suggestions leads to generic results, diminishing user engagement and relevance for individual users.
- ⚠️**Suboptimal Data Structures and Inefficient Indexing:** Using basic data structures or database lookups instead of specialized prefix trees (Tries/FSTs) leads to slow query performance and high latency.
- ⚠️**Over-reliance on Edit Distance for Typo Tolerance:** While useful, raw edit distance calculations can be computationally expensive and slow for long queries or large dictionaries without careful optimization or pre-computation.
- ⚠️**Lack of Real-time or Near Real-time Updates:** Without mechanisms for incremental updates or a 'hot queries' cache, the service can quickly become stale, failing to suggest trending topics or new popular queries.
- ⚠️**Underestimating Data Volume and System Scalability:** Failing to design for distributed processing, sharding, and caching from the outset can lead to bottlenecks, system instability, and high operational costs as user data and query logs grow exponentially.
Potential Follow-up Questions
- ❓How do you handle banned terms?
- ❓How do you avoid query echo chambers?
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