Start by clarifying requirements and scale (e.g., daily active users, events per second, latency needs), then design a pipeline that ingests events into a durable log, processes them in a streaming or batch fashion to maintain per-user top-K counts, and serves results via a low-latency API. Emphasize trade-offs between accuracy, cost, and freshness, and how you'd handle hot users and data skew.
Pro tip: Propose a hybrid approach: use a streaming layer for near-real-time approximate counts and a batch layer for exact daily recomputation, then merge results at query time—this shows you understand real-world constraints and can balance freshness with correctness.
Ask about expected QPS, event volume, latency SLA, accuracy requirements (exact vs approximate), and whether counts are global or time-windowed. This scopes the design and shows you prioritize user needs.
Propose a scalable ingestion layer (e.g., Kafka or a similar distributed log) that can handle high write throughput and durably buffer events. Discuss partitioning by user ID to ensure ordered processing per user.
Outline a stream processing job (e.g., Flink, Spark Streaming) that consumes events, maintains per-user song counts, and computes top-10 using a space-efficient algorithm like Count-Min Sketch with a heap. Mention handling late/out-of-order events and data skew.
Store the top-10 lists in a low-latency store (e.g., Redis or a wide-column database) keyed by user ID. Design an API that fetches the list with caching and fallback to batch-computed results if needed.
Discuss trade-offs: approximate vs exact counts, streaming vs batch, cost vs latency. Cover failure scenarios (e.g., stream job restart, data loss) and how to ensure correctness (e.g., idempotent processing, checkpointing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.