FB(Meta) System Design - rnakidi/dsa GitHub Wiki
Having spent years working on distributed systems, I wanted to share a detailed breakdown of Facebook's impressive architecture that serves billions of users daily.
🏗️ Core Architecture Components:
- Frontend Layer:
- Client interface connects to multiple services through DNS
- Load balancers distribute traffic across API gateways
- CDN optimization for static content delivery
- Data Processing Pipeline:
- Sophisticated write/read server separation for optimal performance
- Multiple API gateways handle request routing and load distribution
- Dedicated video/image processing service with worker pools
- Feed generation tasks run asynchronously through dedicated queues
- Storage Architecture:
- Multi-tiered caching system reducing database load
- Directory-based partitioning for efficient data distribution
- Master-slave database configuration enabling: • High availability • Read scalability • Disaster recovery
- Shard manager handling data partitioning and replication
- Real-Time Features:
- Dedicated notification service with queue management
- Search functionality with results aggregators
- Elastic search implementation with caching layer
- Like service integration with feed generation
- Performance Optimizations:
- Strategic cache placement at multiple levels
- Asynchronous processing for compute-heavy tasks
- Horizontal scaling capabilities at every tier
- Specialized workers for media processing
🔍 Technical Deep-Dive:
The architecture demonstrates several critical patterns:
- Microservices decomposition for independent scaling
- Event-driven design for real-time updates
- Polyglot persistence with different storage solutions
- Circuit breakers and fault isolation
- Eventually consistent data model
⚡ Performance Considerations:
- Read/write splitting reduces contention
- Caching at multiple layers minimizes latency
- Async processing prevents blocking operations
- Partitioning enables infinite horizontal scaling
- CDN integration optimizes content delivery globally
🛡️ Reliability Features:
- Multiple API gateways prevent single points of failure
- Slave DB replicas ensure data redundancy
- Sharding enables better fault isolation
- Queue-based design handles traffic spikes
- Worker pools manage resource utilization
📈 Scaling Strategies:
- Horizontal scaling across all services
- Partition tolerance through sharding
- Load balancing at multiple levels
- Stateless services for easy replication
- Cache hierarchies for performance
🎯 Key Engineering Decisions:
- Separating read/write paths
- Implementing content-aware routing
- Using specialized processing queues
- Maintaining data consistency through careful service design
- Employing multiple layers of caching
💡 Learning Points:
- How to handle web-scale data processing
- Balancing consistency vs availability
- Managing real-time features at scale
- Implementing efficient content delivery
- Designing for fault tolerance Activate to view larger image,