Classic trie problem but the ranking twist is where it gets interesting.
Start by clarifying requirements: scale, latency, update frequency, and whether the system is distributed. Then propose a trie-based solution where each node stores the top k queries for its prefix, and discuss how to handle updates and distributed deployment.
Pro tip: Mention that the top k lists can be precomputed and cached at each trie node, and that for distributed systems, you can shard by prefix and use a caching layer like Redis to meet low-latency requirements.
Ask about scale (number of queries, QPS), latency requirements, update frequency, and whether the system needs to be distributed. This shows you think about practical constraints.
Suggest a trie where each node stores the top k most frequent queries for the prefix represented by that node. Explain how to maintain the top k lists during insertion and updates.
Discuss how to update frequencies and adjust top k lists efficiently. Mention using a min-heap or sorted list per node, and consider batch updates for scalability.
Explain how to shard the trie by prefix across multiple machines, and use a caching layer (e.g., Redis) to serve frequent prefixes with low latency.
Discuss trade-offs: memory vs. latency, precomputation vs. on-the-fly, and how to handle hot prefixes. Mention compression techniques like double-array tries if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.