I started with the basic read/write paths and got through the core subscription model okay.
Start by clarifying functional and non-functional requirements, then design a high-level architecture that separates ingestion, storage, and serving layers. Focus on scalability, personalization, and low-latency feed generation, and discuss trade-offs for key components like ranking algorithms and data stores.
Pro tip: Emphasize how you would handle the cold-start problem for new users and the importance of caching personalized feeds to reduce latency and load on backend services.
Ask questions to understand scale (DAU, articles per day), latency requirements, personalization depth, and whether real-time updates are needed. Define core features: topic/publisher subscriptions, top stories, personalized feed, and redirects.
Outline main components: ingestion pipeline (crawling, parsing, deduplication), storage (article store, user profile store, graph of subscriptions), and serving layer (API gateway, feed service, ranking service). Sketch data flow from ingestion to user feed.
Choose appropriate databases: e.g., Cassandra for articles and user feeds, Redis for caching, Elasticsearch for search. Design schemas for articles, user subscriptions, and precomputed feeds. Discuss sharding and replication strategies.
Explain how to generate personalized feeds: candidate generation (from subscriptions and trending), ranking (ML model or heuristic), and filtering (deduplication, freshness). Discuss precomputation vs. on-the-fly and trade-offs.
Address scaling: horizontal scaling of services, caching strategies (CDN, Redis), and handling hot topics. Discuss trade-offs: consistency vs. availability, latency vs. freshness, and cost vs. personalization quality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.