Google·Software Engineer·Onsite - System Design / Architecture·Senior

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Apr 2026

Summary

Google system design interview, focused entirely on autocomplete. One question but it had a lot of layers and they pushed hard on the tradeoffs side.

Questions Asked (1)

How would you design an autocomplete system for a search results page, including the data structures you'd use and the tradeoffs involved?

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with a trie and they seemed fine with that, but then the follow-ups got more interesting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, latency, personalization, freshness) and then walk through the end-to-end system: data collection, trie-based indexing, query serving, and ranking. Discuss tradeoffs between memory, latency, and accuracy, and mention how you'd handle updates and sharding.

Pro tip: Emphasize that autocomplete is a latency-sensitive, read-heavy system, so caching and precomputation are critical; also mention that you'd measure success with metrics like suggestion acceptance rate and time-to-first-keystroke.

1. Clarify Requirements and Scope

Ask about scale (QPS, number of users), latency targets, personalization, and data freshness. Define what 'autocomplete' means: prefix-based suggestions, trending queries, or personalized results.

2. High-Level Architecture

Outline the main components: data ingestion (query logs), offline processing (aggregation, ranking), storage (trie or inverted index), and online serving (API, cache). Mention sharding and replication for scalability.

3. Data Structures and Algorithms

Propose a trie (prefix tree) with top-k suggestions stored at each node, or a combination of trie and inverted index. Discuss how to handle ranking (e.g., by frequency, recency, personalization) and memory optimizations like double-array tries or compression.

4. Tradeoffs and Optimizations

Compare tradeoffs: trie vs. inverted index (memory vs. speed), precomputation vs. on-the-fly ranking, personalization vs. latency. Discuss caching (CDN, in-memory), sharding by prefix, and handling updates (batch vs. real-time).

5. Evaluation and Monitoring

Describe how to measure performance: latency, throughput, suggestion quality (click-through rate, acceptance rate). Mention A/B testing and monitoring for freshness and failures.

Key Points to Mention

Trie data structure with top-k suggestions per node, and memory optimizations like double-array trie or compression.
Ranking signals: query frequency, recency, user personalization, and context (e.g., location, time).
Caching strategies: CDN for static suggestions, in-memory cache (Redis/Memcached) for hot prefixes.
Sharding and replication: shard by prefix to distribute load, replicate for fault tolerance.
Tradeoff between precomputation (fast but stale) and on-the-fly ranking (fresh but slower).
Handling updates: batch processing for offline index building, and incremental updates for trending queries.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.