Scaling applications requires distributing load effectively across multiple instances. This guide covers load balancing strategies, scaling patterns, and high availability architectures.
Load Balancing Fundamentals
Load Balancing Algorithms
Round Robin
├── Request 1 → Server A
├── Request 2 → Server B
├── Request 3 → Server C
└── Request 4 → Server A (cycles back)
Least Connections
├── Server A: 10 connections
├── Server B: 5 connections ← Next request
└── Server C: 8 connections
Weighted Round Robin
├── Server A (weight: 3) → Gets 3x traffic
├── Server B (weight: 2) → Gets 2x traffic
└── Server C (weight: 1) → Gets 1x traffic
IP Hash
└── Same client IP → Always same server (session affinity)
NGINX Configuration
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HAProxy Configuration
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Horizontal Scaling
Stateless Application Design
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Kubernetes Horizontal Pod Autoscaler
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Database Scaling
Read Replicas
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Connection Pooling with PgBouncer
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Caching Layer
Multi-Level Caching
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Health Checks
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Best Practices
- Design for failure: Assume any component can fail
- Use health checks: Let load balancers route around failures
- Implement graceful shutdown: Drain connections before stopping
- Monitor everything: Metrics, logs, and traces
- Test at scale: Load test before production
- Plan for capacity: Know your limits
Conclusion
Effective scaling requires stateless design, proper load balancing, and careful attention to data consistency. Start with horizontal scaling for web servers, add caching layers, and scale databases with read replicas. Monitor continuously and adjust based on real traffic patterns.