Start by clarifying the input format, tokenization rules, and query types to define the problem precisely. Then outline a two-phase solution: first, parse and aggregate tokens into a hashmap with appropriate data structures; second, process each query efficiently using the precomputed map. Discuss trade-offs between preprocessing time/space and query latency, and consider edge cases like malformed input or missing keys.
Pro tip: Emphasize that in ML engineering at DoorDash, such tokenization and aggregation pipelines often feed into feature stores or real-time models, so discuss how your design supports incremental updates and low-latency lookups at scale.
Ask about input format, tokenization rules (e.g., delimiters, case sensitivity), query types, expected data volume, and latency requirements. This ensures you design the right solution and shows attention to detail.
Choose a tokenization method (e.g., split by whitespace, regex) and define the hashmap schema (key-value pairs, nested structures). Explain how you will iterate over tokens and update the map, handling duplicates and aggregations.
Precompute aggregations or indexes in the hashmap to answer queries in O(1) or O(log n) time. Discuss whether to use additional data structures (e.g., sorted lists, heaps) for range or top-k queries.
Compare time and space complexity of preprocessing vs. querying. Discuss how the solution scales with data size and query volume, and whether streaming or batch processing is more appropriate.
Address empty input, unknown tokens, missing keys, and concurrent updates. Propose tests to verify correctness and performance, and mention monitoring for production.
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