Start by clarifying requirements (throughput, latency, accuracy, time windows) and then present a high-level architecture that separates ingestion, counting, and query layers. Dive into the core algorithm (e.g., Count-Min Sketch with a min-heap for top-K) and explain how sharding and merging work across nodes. Conclude with scaling strategies and trade-offs.
Pro tip: Emphasize that approximate counting with sketches is a deliberate trade-off for scalability, and discuss how to handle time windows by using multiple sketches with epoch-based rotation. This shows you understand both the algorithmic and operational aspects.
Ask about event rate, item cardinality, desired accuracy, latency, and time window semantics (e.g., sliding vs. tumbling). This ensures the design meets the actual needs.
Outline the components: ingestion layer (e.g., Kafka), stream processing (e.g., Flink/Spark Streaming), distributed counting nodes, and a query API. Explain data flow from ingestion to query.
Describe using a Count-Min Sketch for frequency estimation and a min-heap of size K to track top items. Discuss error bounds and how to merge sketches across shards.
Explain how to partition the stream by item hash to distribute load. Each shard maintains its own sketch and heap; a query merges results by summing sketches and merging heaps.
Discuss time-window semantics: use multiple sketches for different windows (e.g., last 1 min, 5 min) with rotation. Scale by adding shards and using a distributed query layer that aggregates results.
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