Scaling Systems: From Thousands to Millions of Users

Scalability Principles

Stateless Services Services should not maintain state. This allows horizontal scaling - add more instances to handle load.

Caching Layers Use caching at multiple levels to reduce database load: - Application cache (Redis, Memcached) - HTTP cache (CDN, reverse proxy) - Database query cache

Database Optimization - Indexing: Create indexes on frequently queried fields - Query optimization: Analyze slow queries, add indexes - Connection pooling: Reuse database connections - Read replicas: Scale read-heavy workloads

Asynchronous Processing Move long-running operations to background queues: - Message queues (RabbitMQ, Kafka) - Job processors (Celery, Bull) - Event streaming

Load Balancing Distribute traffic across instances: - Hardware load balancers (F5, Citrix) - Software load balancers (HAProxy, Nginx) - Cloud load balancers (AWS ELB, GCP LB)

Data Sharding Split data across multiple databases: - Range sharding: By date, ID range - Hash sharding: Hash of key determines shard - Geographic sharding: By region

Monitoring & Observability

Metrics Track: request latency, error rate, resource usage, business metrics

Logging Centralized logging for debugging issues across distributed systems

Tracing Distributed tracing to understand request flow through services

Alerting Proactive alerts on anomalies and SLO violations

Common Bottlenecks

• Database connections
• Network bandwidth
• CPU/memory limits
• I/O throughput

Scaling Patterns

• Vertical scaling: Bigger machines (temporary solution)
• Horizontal scaling: More machines (permanent solution)
• Geographic scaling: Distribute globally for lower latency

Cost Optimization

• Auto-scaling: Scale up during peak, down during off-peak
• Reserved capacity: Commit to usage for discounts
• Spot instances: Use for non-critical workloads
• Right-sizing: Match machine types to workload needs