Scalability Principles
Vertical vs Horizontal Scaling
Vertical Scaling
Add more resources to a single machine (bigger CPU, more RAM).
Pros: Simple, no code changes Cons: Limits, expensive, downtime during upgrades
Horizontal Scaling
Add more machines to distribute load.
Pros: Unlimited, cost-effective, high availability Cons: Complex, requires distributed system design
Scaling Dimensions
Database Scaling
- Read replicas for read-heavy workloads
- Sharding for large datasets
- Caching layers (Redis, Memcached)
Application Scaling
- Stateless applications enable horizontal scaling
- Load balancing across instances
- Message queues for async processing
Content Scaling
- CDN for static content distribution
- Object storage (S3) for large files
- Image optimization and lazy loading
Bottleneck Identification
Measure First
- Profile database queries
- Monitor CPU and memory usage
- Track network bandwidth
- Analyze application logs
Common Bottlenecks
- Database queries
- Synchronous operations
- Inefficient algorithms
- Unoptimized frontend
Scalability Patterns
Microservices
Scale different services independently.
Caching
Reduce load on databases and external services.
Asynchronous Processing
Decouple components with message queues.
Database Sharding
Distribute data across multiple databases.
Design for Scale
- Use stateless applications
- Cache aggressively
- Optimize database queries
- Implement service discovery
- Use async patterns
- Monitor and measure
- Plan capacity ahead
Common Mistakes
- Premature optimization
- Not measuring before optimizing
- Over-caching causing stale data
- Not testing under load
- Ignoring network latency