This one goes in a lot of directions fast.
Start by clarifying requirements and scale (e.g., daily active users, geographic distribution, latency needs), then design the core components: profile service, matching algorithm, and real-time chat. Focus on trade-offs between consistency, latency, and cost, and explain how you would evolve the design from MVP to production scale.
Pro tip: Emphasize the importance of location-based sharding and eventual consistency for matching, and discuss how to handle the 'hot' user problem (e.g., celebrities) with caching and rate limiting.
Ask about functional requirements (profile creation, swiping, matching, chat) and non-functional requirements (latency, availability, consistency). Estimate scale: e.g., 100M users, 1B swipes/day, 10M concurrent chats.
Outline main services: API gateway, profile service, matching service, chat service, and notification service. Choose a database for profiles (e.g., Cassandra for scalability) and a real-time messaging system (e.g., WebSockets with Redis pub/sub).
Design the matching logic: based on location, preferences, and swipe history. Use a geospatial index (e.g., QuadTree or Geohash) for proximity queries. Discuss trade-offs between pre-computing matches vs. on-the-fly.
Design chat: use WebSockets for persistent connections, with a message queue (e.g., Kafka) for reliability. Ensure message ordering and delivery guarantees. Discuss presence and typing indicators.
Address sharding, replication, and caching strategies. Discuss CAP theorem trade-offs: e.g., AP for matching, CP for chat. Plan for monitoring, rate limiting, and handling spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.