Design 1: Designing a Chat System like 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.