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I knew trie-based autocomplete at a surface level but hadn't thought through the scoring part carefully.
Start by clarifying requirements (scale, latency, update frequency) and then propose a trie-based solution where each node stores top-K suggestions for its prefix. Explain how to handle real-time updates efficiently by updating only affected nodes and discuss trade-offs between precomputation and on-the-fly computation.
Pro tip: Mention that you would use a min-heap to maintain top-K suggestions at each node and that you'd consider a distributed cache like Redis for serving suggestions at scale, showing awareness of production systems.
Ask about data scale (number of dishes, query rate), latency requirements, update frequency, and whether suggestions should be personalized. This ensures the design meets actual needs.
Propose a trie (prefix tree) where each node stores the top-K highest-scoring dishes for the prefix represented by that node. Explain how to build and update it.
For real-time updates, when a dish's score changes, traverse the trie along the dish's path and update the top-K lists at each node using a heap or sorted list. Discuss incremental updates vs. batch rebuilds.
Discuss partitioning the trie across machines, caching hot prefixes, and using approximate algorithms (e.g., count-min sketch) if exact top-K is too costly. Mention distributed systems like Redis or Cassandra for storage.
Compare precomputing top-K at each node (fast queries, slower updates) vs. computing on the fly (slower queries, faster updates). Suggest a hybrid approach based on query/update patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.