The view tracking part is where I spent most of my energy and probably where I should have been more careful about tradeoffs.
Start by clarifying requirements and scale, then design the product page with a clear separation between the read-heavy product display and the write-heavy view tracking. Propose a scalable, asynchronous view counting mechanism that avoids impacting page load latency, and discuss trade-offs between accuracy and performance.
Pro tip: Emphasize that view counting should be decoupled from the critical path of serving the page—use client-side beacons or server-side async logging to avoid slowing down the user experience. Also, mention that deduplication and bot filtering are often needed to make the metric meaningful.
Ask about expected traffic (e.g., millions of views per day), latency requirements, accuracy needs (exact vs approximate counts), and whether views should be deduplicated per user/session.
Outline the main components: a product service to fetch product details, a view tracking service, a data store for view counts, and a CDN for static assets. Ensure the view tracking is asynchronous and non-blocking.
Design how views are captured: client-side JavaScript beacon or server-side logging on page load. Use a message queue (e.g., Kafka) to buffer view events and process them asynchronously.
Choose a storage solution for view counts: a fast in-memory store like Redis for real-time counters, with periodic persistence to a database (e.g., DynamoDB, Cassandra). Consider using a stream processor (e.g., Flink) for aggregation and deduplication.
Discuss trade-offs: exact vs approximate counting (e.g., HyperLogLog), latency vs accuracy, cost of storage, and how to handle spikes. Explain how the system scales horizontally and remains fault-tolerant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.