I started with the API contract and worked inward, which felt right.
Start by clarifying functional and non-functional requirements, then design a high-level architecture with a feed generation service, ranking service, and caching layer. Focus on the API contract, pagination strategy, and how to handle personalization with fallback while ensuring low latency and deduplication.
Pro tip: Emphasize the trade-offs between precomputed and on-the-fly ranking, and propose a hybrid approach that balances latency and personalization. Also, discuss how to handle cursor-based pagination to avoid duplicates and support bidirectional navigation.
Ask about scale (DAU, QPS), latency targets, personalization depth, and consistency requirements. Confirm the need for endless scrolling, next/previous navigation, and deduplication.
Design a RESTful or gRPC API that returns an ordered list of video IDs with a cursor for pagination. Use cursor-based pagination (e.g., encoded offset or timestamp) to support endless scrolling and bidirectional navigation without duplicates.
Outline components: API gateway, feed service, ranking service, video metadata service, and cache (e.g., Redis). Consider precomputed feeds for active users and on-the-fly ranking for others, with a fallback to non-personalized (e.g., trending) content.
Explain how ranking works: use a mix of user features, video features, and real-time signals. Implement a fallback mechanism that serves non-personalized content if personalization fails or for new users.
Describe how to avoid duplicates: maintain a seen-set per session or use a bloom filter. For next/previous, store the cursor state on the client or server to allow seamless navigation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.