Scaling a Chat App to 1M Users
Real-time messaging architecture that handles massive scale with sub-second latency.
Problem Statement
Design a real-time chat application that can scale to handle 1 million concurrent users while maintaining sub-second message delivery latency.
Context: Social messaging platform with real-time features, group chats, and media sharing.
Architecture Overview
Microservices architecture with WebSocket connections, Redis for caching, and message queues for reliability
Message Flow
How messages flow through the system from sender to recipient
Scaling Strategy
Multi-dimensional scaling approach for handling massive user loads
Key Components
Core architectural components and their responsibilities
WebSocket Manager
- • Connection pooling and management
- • Load balancing across instances
- • Connection state tracking
- • Graceful connection handling
Redis Cluster
- • Session storage and management
- • Real-time presence tracking
- • Message caching and delivery
- • Pub/Sub for notifications
Message Queue
- • Asynchronous message processing
- • Guaranteed message delivery
- • Dead letter queue handling
- • Message persistence
Data Sharding
- • User-based sharding strategy
- • Geographic distribution
- • Consistent hashing
- • Shard rebalancing
Performance Optimizations
Techniques to achieve sub-second latency at scale
Connection Pooling
Reuse WebSocket connections to reduce connection overhead and improve response times.
Message Batching
Batch multiple messages together to reduce network round trips and improve throughput.
Read Replicas
Use read replicas for message history queries to reduce load on primary databases.
Edge Caching
Cache frequently accessed data at edge locations to reduce latency for global users.
Monitoring & Metrics
Key metrics to track for performance and reliability
Performance Metrics
- • Message delivery latency (P50, P95, P99)
- • WebSocket connection count
- • Message throughput (msg/sec)
- • API response times
Reliability Metrics
- • Connection success rate
- • Message delivery success rate
- • Service uptime
- • Error rates by service
Results & Metrics
Expected outcomes and success metrics