← Microsoft Interview Insights
I started with a trie and they immediately asked why not a ternary search tree or an FST.
Start by clarifying requirements (scale, latency, freshness, personalization) and then propose a trie-based data structure with top-k cached at each node for fast prefix lookups. Cover the ingestion pipeline that builds and updates the trie from query logs, and describe a distributed serving architecture with sharding, replication, and caching to meet real-time SLAs.
Pro tip: Emphasize trade-offs between precomputing top-k at every trie node (fast reads, expensive writes) versus computing on the fly (slow reads, cheap writes), and propose a hybrid approach with periodic batch updates and incremental adjustments. Also mention how you'd handle trending queries and personalization without sacrificing latency.
Ask about scale (QPS, number of queries), latency SLA, data freshness, personalization, and whether results should be global or user-specific. This shapes all subsequent design decisions.
Propose a trie (prefix tree) where each node stores top-k completions for its prefix, ranked by a scoring function (e.g., frequency, recency, click-through rate). Discuss how to update top-k efficiently when new queries arrive.
Describe how raw query logs are processed (e.g., via stream processing like Kafka + Flink) to compute scores, filter spam, and periodically rebuild or update the trie. Address batch vs. streaming trade-offs.
Explain how to partition the trie across servers (e.g., by prefix range), replicate for fault tolerance, and use caching (e.g., Redis) for hot prefixes. Discuss load balancing and handling of skewed traffic.
Discuss trade-offs: precomputation vs. on-the-fly, consistency vs. availability, and memory vs. latency. Mention optimizations like pruning low-frequency branches, using approximate top-k, and personalization layers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.