I started with the functional requirements which felt okay, multiple window sizes, per-region vs global aggregation, query latency targets.
Start by clarifying requirements: what K, window sizes, and scale (QPS, cardinality). Then propose a distributed architecture that shards the stream, uses approximate counting (e.g., Count-Min Sketch) per shard, and merges results to get top K. Discuss trade-offs between accuracy, latency, and resource usage.
Pro tip: Emphasize that exact top-K over sliding windows at scale is impractical; instead, use approximate algorithms with bounded error and explain how to handle out-of-order events and window expiration.
Ask about K, window sizes, data volume, latency requirements, and accuracy tolerance. This scopes the problem and shows you avoid over-engineering.
Propose a distributed pipeline: ingest (Kafka), process (stream processors like Flink/Spark Streaming), and serve (query layer). Shard by item key to parallelize counting.
Use approximate counting per shard (e.g., Count-Min Sketch) and a heap or Space-Saving algorithm to maintain local top-K. Merge shard results to get global top-K.
Implement windowing via bucketing (e.g., per-minute buckets) and aggregate over buckets. Use event-time processing with watermarks to handle out-of-order data.
Discuss accuracy vs. memory, latency vs. freshness, and strategies like heavy-hitter detection, caching, and pre-aggregation to reduce load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.