This was basically the entire interview compressed into one question.
Start by clarifying requirements (exact vs approximate top-K, time window, latency/throughput targets), then propose a hybrid solution: a hash map for exact counts plus a min-heap or bucket-based structure for top-K, and for sliding windows use time-bucketed counters with periodic aggregation. For distributed scaling, discuss sharding by key, local aggregation with merge, and tradeoffs between accuracy and performance.
Pro tip: Mention that for high-volume streams, approximate algorithms like Count-Min Sketch with a heap are often preferred over exact counting due to memory constraints, and that sliding windows can be implemented with a ring buffer of time buckets to avoid recomputation.
Ask about data volume, latency requirements, exact vs approximate results, window size, and distributed setup to tailor the solution.
Propose a hash map for frequency counts and a min-heap of size K for top-K, or bucket-based counting for O(1) updates; discuss tradeoffs.
Use time-bucketed counters (e.g., per-minute) in a ring buffer, and compute top-K over the window by aggregating buckets, possibly with lazy eviction.
Compare time/space complexity of exact vs approximate methods, and discuss update/query costs, memory usage, and accuracy.
Shard by key, have each node maintain local top-K, then merge at a coordinator; consider approximate algorithms for cross-shard aggregation and consistency tradeoffs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.