I went deep on the scoring model early, like way too deep.
Start by clarifying requirements and scale, then propose a high-level architecture that separates real-time ingestion, scoring, and serving layers. Discuss caching strategies (e.g., multi-tier with TTLs) and the tradeoff between freshness and relevance, emphasizing how to tune based on business metrics.
Pro tip: At Uber, where real-time data and scale are critical, highlight how you'd use streaming (e.g., Kafka/Flink) for near-real-time updates and a hybrid scoring model that balances recency and popularity. Also, mention the importance of A/B testing and monitoring to validate the freshness-relevance tradeoff.
Ask about expected traffic, update frequency, definition of 'trending', and business goals (e.g., revenue vs. engagement). Establish scale (e.g., millions of users, thousands of items) to inform design choices.
Propose a pipeline that ingests user interactions (views, purchases) in real-time, computes popularity scores using algorithms like time-decayed counts or Hacker News ranking, and stores scores in a fast database (e.g., Redis).
Use a multi-tier cache: CDN for static assets, application-level cache (e.g., Redis) for trending lists with short TTLs (e.g., 1-5 minutes), and a fallback to precomputed lists. Discuss cache invalidation and consistency.
Explain how to tune the scoring algorithm's decay factor and cache TTL to balance fresh content with stable popularity. Suggest using A/B testing to measure impact on key metrics.
Outline how to scale horizontally (sharding, replication) and monitor system health (latency, cache hit rate, score distribution). Mention handling hot items and ensuring fault tolerance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.