I started with the data model and fan-out logic but quickly realized I was treating it like a static feed problem.
Start by clarifying requirements and scale (e.g., billions of users, millions of concurrent comments per post), then outline a high-level architecture covering real-time delivery, storage, and ranking. Dive into key components like WebSocket connections, pub/sub, and data modeling, discussing trade-offs and optimizations.
Pro tip: Emphasize the importance of handling hot posts (e.g., celebrity posts with millions of comments) through techniques like sharding, caching, and rate limiting, and discuss how to ensure low-latency delivery while maintaining consistency.
Ask questions to understand functional and non-functional requirements: expected QPS, latency targets, consistency needs, and features like threading, reactions, and moderation. Estimate scale (e.g., 2B users, 100M concurrent comments).
Sketch the main components: clients (web/mobile), API gateway, comment service, real-time delivery service (WebSocket/SSE), storage (SQL/NoSQL), cache, and message queue/pub-sub. Explain data flow from comment creation to delivery.
Design the data model for comments (e.g., comment ID, post ID, user ID, content, timestamp, parent ID for threading). Choose storage: a distributed SQL (like MySQL) for consistency or NoSQL (like Cassandra) for scalability, and discuss indexing for efficient retrieval.
Detail how to push comments to clients in real-time using WebSockets and a pub/sub system (e.g., Kafka, Redis Pub/Sub). Discuss scaling connections with load balancers and handling fan-out to millions of subscribers.
Discuss trade-offs: consistency vs. availability, latency vs. cost, and push vs. pull. Optimize with caching (e.g., Redis for hot comments), sharding by post ID, rate limiting, and moderation pipelines.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.