I went straight to a sliding window approach with a distributed counter store, which felt right, but I fumbled when they pushed on consistency vs.
Start by clarifying requirements (scale, latency, accuracy, data sources) and then design a scalable, real-time pipeline that ingests play events, aggregates counts per song over a sliding 7-day window, and serves the top 10 via a low-latency API. Focus on data modeling, stream processing, and storage trade-offs to handle high throughput and provide fresh results.
Pro tip: Emphasize the trade-off between accuracy and latency: using approximate algorithms like Count-Min Sketch with a heap can reduce memory and cost while still delivering near-real-time top-K results, which is often acceptable for trending content.
Ask about scale (e.g., daily active users, songs, events per second), latency requirements (real-time vs. batch), accuracy tolerance, and data sources (e.g., client logs, streaming events).
Propose a scalable ingestion layer (e.g., Kafka) to collect play events, and a stream processing engine (e.g., Flink, Spark Streaming) to aggregate counts per song in real-time or micro-batches.
Choose a storage solution that supports efficient updates and queries over a 7-day sliding window, such as a time-series database, Redis sorted sets, or a custom aggregation store with TTL.
Implement a mechanism to maintain top-K songs (e.g., using a heap or sorted set) and expose an API that returns the top 10 with low latency, possibly caching results.
Discuss partitioning, replication, handling late data, and trade-offs between exact vs. approximate counting, and between real-time vs. batch processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.