Scalability & Performance¶
Guidelines and tools for building scalable, high-performance cloud applications that can handle varying workloads efficiently.
| Name | Description | Link |
|---|---|---|
| AWS Auto Scaling | Automatically adjusts your Amazon EC2 capacity to maintain performance and control costs. | AWS Auto Scaling |
| Google Cloud Autoscaler | Dynamically resizes virtual machine instances to match demand in Google Cloud. | Google Cloud Autoscaler |
| OpenStack Autoscaling | Theory and implementation guide for auto-scaling in OpenStack environments. | OpenStack Autoscaling |
Scalability Principles¶
Horizontal vs Vertical Scaling¶
- Horizontal (Scale Out) - Add more instances
- Better fault tolerance
- Unlimited scaling potential
-
More complex architecture
-
Vertical (Scale Up) - Increase instance size
- Simpler implementation
- Limited by instance size
- Potential downtime during scaling
Scaling Patterns¶
- Reactive scaling - Scale based on current metrics
- Predictive scaling - Scale based on forecasted demand
- Scheduled scaling - Scale based on known patterns
- Manual scaling - Human-triggered scaling events
Performance Strategies¶
Application Layer¶
- Code optimization - Efficient algorithms and data structures
- Asynchronous processing - Non-blocking operations
- Connection pooling - Reuse database connections
- Lazy loading - Load resources only when needed
Caching Strategies¶
- In-memory caching - Redis, Memcached for fast data access
- CDN caching - Cache static content at edge locations
- Database query caching - Cache frequent query results
- Application-level caching - Cache computed results
Database Optimization¶
- Indexing - Optimize query performance
- Query optimization - Efficient SQL queries
- Read replicas - Distribute read traffic
- Sharding - Partition data across databases
Network Optimization¶
- Content delivery networks - Serve content from edge locations
- Load balancing - Distribute traffic efficiently
- Connection optimization - HTTP/2, connection keep-alive
- Compression - Reduce data transfer size
Architecture Patterns¶
Microservices Architecture¶
- Independent scaling - Scale services independently
- Fault isolation - Failures don't cascade
- Technology diversity - Use best tools for each service
- Deployment flexibility - Deploy services independently
Event-Driven Architecture¶
- Asynchronous processing - Decouple components
- Message queues - Buffer between services
- Event sourcing - Track all state changes
- CQRS - Separate read and write operations
Serverless Architecture¶
- Automatic scaling - Platform handles scaling
- Pay-per-use - Cost-efficient for variable workloads
- No server management - Focus on code
- Event-driven - Natural fit for reactive systems
Performance Measurement¶
Key Metrics¶
- Latency - Response time experienced by users
- Throughput - Requests or transactions per second
- Error rate - Failed requests or operations
- Resource utilization - CPU, memory, and I/O usage
Load Testing¶
- Baseline testing - Understand normal performance
- Stress testing - Identify breaking points
- Spike testing - Handle sudden traffic increases
- Soak testing - Long-running performance behavior
Have any suggestions, additions, best-practices or references? Please contribute to help others learn!