Design 1: Designing a Chat System like WhatsApp
Let’s walk through the step-by-step system design of a messaging platform similar to WhatsApp.
Step 1: Define Functional Requirements
- Real-time messaging (text, voice, and video)
- Group chat support
- Status sharing (text, photo, video)
- End-to-end encryption
- Voice and video calls
- File/media/document sharing
Step 2: Define Non-Functional Requirements
- Scalability: System must handle billions of users
- Reliability: High uptime and minimal failure
- Security: Data encryption and secure communication
- Low Latency: Near-instant message delivery
- Cross-Platform Support: Web, Android, iOS, etc.
- Massive data storage and retrieval
Step 3: Estimate Traffic
- User base: 2 billion
- Active users: ~500 million (25%)
- Average messages/user/day: 30
- Total messages/day: 500M × 30 = 1.5 billion
Step 4: Estimate Storage
- Average message size: 50 KB
- Total storage/day: 1.5B × 50 KB = 75 PB
- Undelivered message storage (10%): 7.5 PB/day
- Retention (30 days): 7.5 PB × 30 = 225 PB
Step 5: Define API Services
- User Authentication (login, registration, OTP, profile)
- Message CRUD operations
- Media upload/download APIs
- Search and filtering APIs
- Push notification APIs
Step 6: High-Level Architecture
- Client App: Sends and receives messages using persistent WebSocket connections
- API Gateway: Manages requests for user authentication, message history, and media operations
- Auth Service: Handles registration, login, and token validation
- Messaging Service: Orchestrates chat flow and forwards real-time messages via WebSocket
- Presence Service: Tracks online/offline status and last seen
- Media Service: Uploads, stores, and serves media using object storage (e.g., AWS S3)
- Message Queue: (Kafka/RabbitMQ) Enables async delivery and retry handling
- NoSQL Database: Stores chat messages and metadata (e.g., DynamoDB or Cassandra)
- Search Index: Enables message and user search (e.g., Elasticsearch)
Step 7: Key Architectural Decisions
- Database: Use scalable NoSQL DB (e.g., Cassandra, DynamoDB) for messages
- Media Storage: Use object storage like AWS S3 or GCP Cloud Storage
- Real-Time: Use WebSockets or MQTT for real-time messaging
- Encryption: End-to-end encryption using protocols like Signal
- Scalability: Use load balancers, CDN, and horizontal scaling for services
Step 8: Additional Considerations
- Message queues (e.g., Kafka, RabbitMQ) for asynchronous processing
- Rate limiting and spam detection
- Monitoring and logging with observability stack
- Data retention and GDPR compliance
Conclusion
Designing a large-scale chat system requires careful planning of architecture, scalability, data management, and real-time performance. Every decision has trade-offs, and understanding the scale and use case is crucial.