I went straight to the data model and storage layer, which in hindsight was probably the wrong entry point.
Start by clarifying functional and non-functional requirements, then estimate scale to inform high-level design. Focus on core components like real-time messaging, storage, and delivery guarantees, and discuss trade-offs for scalability and reliability.
Pro tip: Emphasize the importance of message ordering and delivery guarantees, and how you would handle offline users and multi-device synchronization—these are critical for a chat system and often overlooked.
Ask questions to understand scope: 1:1 vs group chat, message types (text, media), real-time delivery, offline support, read receipts, etc. Also define non-functional requirements like latency, scalability, and availability.
Estimate daily active users, messages per user per day, peak load, storage needs, and bandwidth. This will guide decisions on sharding, replication, and caching.
Outline core components: client apps, API gateway, chat servers (WebSocket), message queue, storage (message DB, user DB), and notification service. Explain data flow for sending and receiving messages.
Detail message storage (e.g., Cassandra for messages, MySQL for users), message ID generation (e.g., Snowflake), delivery guarantees (at-least-once, deduplication), and real-time communication (WebSockets, long polling).
Discuss trade-offs: consistency vs availability, SQL vs NoSQL, push vs pull. Explain scaling strategies: sharding by user ID, caching, CDN for media, and handling failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.