Start by clarifying requirements and scale (e.g., read-heavy, eventual consistency acceptable), then outline a high-level design with a focus on scalability and data modeling. Structure your answer by covering API design, data model, and scaling strategies, and discuss trade-offs at each step.
Pro tip: Emphasize idempotency and eventual consistency for likes, and discuss how you'd handle hot partitions and celebrity brands with sharding or caching. Also, mention monitoring and metrics to detect anomalies.
Ask questions to understand expected read/write ratio, latency requirements, consistency needs, and scale (e.g., number of brands, users, likes per second). This ensures your design meets actual needs.
Sketch the main components: clients, API gateway, like service, database, cache, and message queue. Explain the flow of a like/unlike request and how data is stored and retrieved.
Define RESTful endpoints (e.g., POST /brands/{id}/like, DELETE /brands/{id}/like, GET /brands/{id}/likes) with idempotency keys and appropriate status codes. Discuss authentication and rate limiting.
Propose a schema: a likes table with user_id, brand_id, timestamp, and a unique constraint on (user_id, brand_id). Consider a separate counter table or denormalized count for fast reads, and discuss trade-offs.
Address scaling: shard by brand_id or user_id, use caching (e.g., Redis) for counts, employ async processing via queues for writes, and handle hot partitions with techniques like sharding by brand_id + time bucket.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.