Implement top-K over a stream

Q: Implement top-K over a stream

This question evaluates a candidate's proficiency in stream-processing algorithms and scalable data structures for frequency estimation and top-K queries, emphasizing analysis of time/space trade-offs, sliding-window semantics, and distributed aggregation.

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Question

Given a high-volume stream of events (e.g., account IDs from new account openings), design and implement a data structure that supports: (

inserting an item, (
returning the current top-K most frequent items, and (
optionally returning the top-K over the last T minutes (sliding window). Discuss algorithm choices (heaps, bucket counting, count–min sketch with heap), time/space complexities, handling ties, changing K at query time, and window expiration. Outline a scalable distributed approach, including partitioning, partial aggregation, and merge semantics.

Implement top-K over a stream

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