Chapter 13: Scalability Patterns
Chapter 13 of 15
Chapter 13: Scalability Patterns
13.1 Horizontal Scaling
Horizontal scaling adds more servers to handle increased load. Design applications to support horizontal scaling.
Horizontal vs Vertical Scaling:
- Horizontal: Add more servers (scale out)
- Vertical: Upgrade server hardware (scale up)
- Horizontal is more cost-effective and flexible
- Requires stateless application design
Stateless Design:
- Don't store session state on server
- Use external session storage (Redis, database)
- Store files in object storage (S3, Cloud Storage)
- Use shared databases
Database Scaling:
- Read Replicas: Distribute read queries
- Sharding: Partition data across databases
- Caching: Reduce database load
- Connection Pooling: Efficient connection management
13.2 Load Balancing
Load balancers distribute requests across multiple servers, improving performance and availability.
Load Balancing Algorithms:
- Round Robin: Distribute requests sequentially
- Least Connections: Route to server with fewest connections
- IP Hash: Route based on client IP
- Weighted: Assign weights to servers
Load Balancer Types:
- Application Load Balancer: Layer 7, content-based routing
- Network Load Balancer: Layer 4, high performance
- Classic Load Balancer: Basic load balancing
Health Checks:
- Monitor server health
- Remove unhealthy servers from pool
- Automatic recovery when server becomes healthy
- Prevent routing to failed servers
13.3 Caching for Scalability
Caching reduces load on servers and databases, improving scalability.
- Cache frequently accessed data
- Use CDN for static assets
- Implement application-level caching
- Cache database query results
- Use distributed caching (Redis cluster)
13.4 Database Optimization
Optimize databases to handle increased load.
- Create indexes on frequently queried columns
- Optimize slow queries
- Use connection pooling
- Implement read replicas
- Partition large tables
- Archive old data