The prompt was genuinely short, like one sentence and a list of strings.
Start by clarifying requirements (case sensitivity, result ordering, scale) and then propose a trie-based solution for efficient prefix search. Discuss trade-offs between preprocessing and query time, and mention how to handle large datasets with distributed or cached approaches.
Pro tip: At Uber's scale, autocomplete must handle millions of queries with low latency; mention that you'd precompute top results per prefix and use a distributed cache like Redis to serve them quickly.
Ask about input size, expected query volume, case sensitivity, ordering of results, and whether the store list is static or dynamic.
Propose a trie (prefix tree) for efficient prefix matching, or a sorted array with binary search if the list is static and memory is a concern.
Outline insertion and query operations: build the trie by inserting all store names, then traverse to the prefix node and collect all descendant store names.
Discuss precomputing top suggestions per prefix, caching frequent queries, and sharding the trie for distributed systems.
Compare time/space complexity of different approaches and justify your choice based on the clarified requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.