Start by clarifying requirements (scale, latency, accuracy, update frequency) and then design a pipeline that ingests events, maintains approximate top-K using a distributed streaming approach, and serves queries with low latency. Emphasize trade-offs between exactness and scalability, and how you'd handle updates and failures.
Pro tip: Meta values practical scalability and real-time systems; mention using a combination of Kafka for ingestion, Flink for stream processing, and a sharded in-memory store like Redis with a custom top-K algorithm (e.g., count-min sketch + heap) to handle high throughput and low latency.
Ask about scale (QPS, data volume), latency requirements, accuracy (exact vs approximate), update frequency, and query patterns (e.g., top-K per category, global).
Use a distributed message queue (e.g., Kafka) to collect events from producers; ensure partitioning by item ID for ordered processing and scalability.
Process events in real-time using a stream processor (e.g., Flink) to update counts; use approximate algorithms (e.g., count-min sketch, space-saving) to maintain top-K per shard, then merge.
Store top-K results in a low-latency store (e.g., Redis) with periodic updates; serve queries via an API that fetches from the store, possibly with caching.
Shard the processing and storage, use replication for fault tolerance, and design for eventual consistency; discuss trade-offs between update frequency and accuracy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.