# Performance Engineering
## Purpose
Define comprehensive performance engineering requirements for high-load, enterprise-grade systems. This section establishes performance budgets, optimization strategies, and engineering practices to ensure systems meet demanding performance requirements.
## Prerequisites
- Technical architecture and infrastructure requirements defined
- SRE framework and SLI/SLO requirements established
- Functional requirements and user experience goals understood
- Scale and load expectations documented
## Section Structure & Requirements
### 1. Performance Engineering Strategy
**Objective**: Define overall approach to performance engineering
**Required Elements:**
- **Performance Philosophy**: Approach to performance engineering and optimization
- **Performance Goals**: Specific performance objectives and targets
- **Performance Budget Framework**: How performance budgets are defined and managed
- **Performance Engineering Process**: How performance is engineered throughout development
- **Performance Culture**: How performance awareness is built into team culture
**Quality Criteria:**
- Strategy aligns with business objectives and user needs
- Goals are specific, measurable, and achievable
- Budget framework enables performance decision-making
- Process integrates performance into development lifecycle
**Template:**
## Performance Engineering Strategy
### Performance Philosophy
[Overall approach to performance engineering and system optimization]
### Performance Goals
- **User Experience Goals**: [Response time, throughput, availability targets]
- **Business Goals**: [Cost efficiency, scalability, competitive advantage]
- **Technical Goals**: [Resource utilization, system efficiency, maintainability]
### Performance Budget Framework
- **Response Time Budgets**: [Allocated time for different system components]
- **Resource Budgets**: [CPU, memory, network, storage allocations]
- **Cost Budgets**: [Infrastructure cost targets and constraints]
- **Complexity Budgets**: [Acceptable levels of system complexity]
### Performance Engineering Process
1. **Requirements Analysis**: [How performance requirements are analyzed]
2. **Architecture Review**: [How architecture is reviewed for performance]
3. **Implementation Guidelines**: [Performance guidelines for development]
4. **Testing & Validation**: [How performance is tested and validated]
5. **Monitoring & Optimization**: [Ongoing performance monitoring and optimization]
### Performance Culture
[How performance awareness is built into team culture and practices]
### 2. High-Load System Patterns
**Objective**: Define patterns and strategies for high-load system design
**Required Elements:**
- **Caching Strategies**: Multi-level caching and cache management
- **Database Scaling**: Sharding, replication, and database optimization
- **Load Balancing**: Traffic distribution and load balancing strategies
- **Content Delivery**: CDN and edge computing strategies
- **Asynchronous Processing**: Background processing and queue management
**Template:**
## High-Load System Patterns
### Caching Strategies
**Cache Levels**:
- **Browser Cache**: [Client-side caching strategies]
- **CDN Cache**: [Content delivery network caching]
- **Application Cache**: [In-memory application caching]
- **Database Cache**: [Database query result caching]
**Cache Patterns**:
- **Cache-Aside**: [Application manages cache directly]
- **Write-Through**: [Cache updated synchronously with database]
- **Write-Behind**: [Cache updated asynchronously]
- **Refresh-Ahead**: [Cache refreshed before expiration]
**Cache Management**:
- **Cache Invalidation**: [How cached data is invalidated]
- **Cache Warming**: [How caches are pre-populated]
- **Cache Monitoring**: [How cache performance is monitored]
### Database Scaling Patterns
**Horizontal Scaling**:
- **Read Replicas**: [Read-only database replicas for query distribution]
- **Sharding**: [Data partitioning across multiple databases]
- **Federation**: [Database splitting by function]
**Vertical Scaling**:
- **Resource Optimization**: [CPU, memory, storage optimization]
- **Query Optimization**: [Database query performance tuning]
- **Index Optimization**: [Database indexing strategies]
**CQRS & Event Sourcing**:
- **Command Query Separation**: [Separate read and write models]
- **Event Store**: [Event-based data persistence]
- **Read Model Optimization**: [Optimized read-only data models]
### Load Balancing Strategies
**Load Balancer Types**:
- **Layer 4 (Transport)**: [TCP/UDP load balancing]
- **Layer 7 (Application)**: [HTTP/HTTPS load balancing]
- **Global Load Balancing**: [Geographic traffic distribution]
**Load Balancing Algorithms**:
- **Round Robin**: [Sequential request distribution]
- **Least Connections**: [Route to least busy server]
- **Weighted Routing**: [Route based on server capacity]
- **Health-Based Routing**: [Route only to healthy servers]
### Content Delivery Networks (CDN)
- **CDN Strategy**: [How content is distributed globally]
- **Edge Computing**: [Processing at edge locations]
- **Cache Policies**: [What content is cached and for how long]
- **Origin Protection**: [How origin servers are protected]
### 3. Capacity Planning & Resource Management
**Objective**: Define capacity planning methodologies and resource optimization
**Required Elements:**
- **Capacity Planning Process**: Systematic approach to capacity planning
- **Resource Forecasting**: How future resource needs are predicted
- **Auto-Scaling Strategies**: Automatic resource scaling policies
- **Resource Optimization**: Strategies for efficient resource utilization
- **Cost Optimization**: Balancing performance with cost efficiency
**Template:**
## Capacity Planning & Resource Management
### Capacity Planning Process
1. **Baseline Measurement**: [Current resource utilization and performance]
2. **Growth Projection**: [Expected growth in users, data, and transactions]
3. **Resource Modeling**: [Mathematical models for resource requirements]
4. **Scenario Planning**: [Planning for different growth scenarios]
5. **Capacity Provisioning**: [How additional capacity is provisioned]
### Resource Forecasting
**Forecasting Methods**:
- **Trend Analysis**: [Historical trend-based forecasting]
- **Seasonal Modeling**: [Accounting for seasonal variations]
- **Business-Driven Forecasting**: [Based on business growth plans]
- **Machine Learning Models**: [ML-based capacity prediction]
**Forecasting Metrics**:
- **CPU Utilization**: [Processor usage forecasting]
- **Memory Usage**: [Memory consumption forecasting]
- **Storage Growth**: [Data storage growth forecasting]
- **Network Bandwidth**: [Network usage forecasting]
### Auto-Scaling Strategies
**Horizontal Auto-Scaling**:
- **Scale-Out Triggers**: [When to add more instances]
- **Scale-In Triggers**: [When to remove instances]
- **Scaling Policies**: [How quickly to scale up/down]
- **Minimum/Maximum Limits**: [Scaling boundaries]
**Vertical Auto-Scaling**:
- **Resource Adjustment**: [CPU, memory scaling policies]
- **Performance Thresholds**: [When to scale resources]
- **Scaling Windows**: [When scaling is allowed]
**Predictive Scaling**:
- **Traffic Prediction**: [Anticipating traffic patterns]
- **Pre-Scaling**: [Scaling before demand increases]
- **Schedule-Based Scaling**: [Scaling based on known patterns]
### Resource Optimization
- **Right-Sizing**: [Matching resources to actual needs]
- **Resource Pooling**: [Sharing resources across services]
- **Spot Instance Usage**: [Using discounted cloud resources]
- **Reserved Capacity**: [Long-term resource commitments]
### Cost Optimization
[Strategies for balancing performance with cost efficiency]
### 4. Performance Testing & Validation
**Objective**: Define comprehensive performance testing framework
**Required Elements:**
- **Performance Testing Strategy**: Overall approach to performance testing
- **Testing Types**: Different types of performance tests
- **Test Environment Management**: How test environments are managed
- **Performance Test Automation**: Automated performance testing
- **Performance Regression Testing**: Preventing performance regressions
**Template:**
## Performance Testing & Validation
### Performance Testing Strategy
[Overall approach to performance testing throughout development lifecycle]
### Performance Testing Types
**Load Testing**:
- **Normal Load**: [Testing under expected load conditions]
- **Peak Load**: [Testing under maximum expected load]
- **Sustained Load**: [Testing under prolonged load conditions]
**Stress Testing**:
- **Breaking Point**: [Finding system failure points]
- **Recovery Testing**: [Testing system recovery after failure]
- **Resource Exhaustion**: [Testing under resource constraints]
**Spike Testing**:
- **Traffic Spikes**: [Testing sudden traffic increases]
- **Load Ramp-Up**: [Testing gradual load increases]
- **Load Ramp-Down**: [Testing load decreases]
**Volume Testing**:
- **Data Volume**: [Testing with large data sets]
- **User Volume**: [Testing with many concurrent users]
- **Transaction Volume**: [Testing high transaction rates]
### Test Environment Management
- **Environment Parity**: [Matching production environment characteristics]
- **Test Data Management**: [Managing test data sets]
- **Environment Provisioning**: [Creating and managing test environments]
- **Environment Monitoring**: [Monitoring test environment health]
### Performance Test Automation
- **Automated Test Execution**: [Running tests automatically]
- **Performance CI/CD**: [Integrating performance tests into pipelines]
- **Automated Analysis**: [Automatic performance test result analysis]
- **Regression Detection**: [Automatically detecting performance regressions]
### Performance Benchmarking
[Establishing and maintaining performance benchmarks]
### 5. Performance Monitoring & Optimization
**Objective**: Define ongoing performance monitoring and optimization practices
**Required Elements:**
- **Performance Monitoring Strategy**: How performance is continuously monitored
- **Performance Metrics**: Key performance metrics and KPIs
- **Performance Alerting**: When and how performance alerts are triggered
- **Performance Analysis**: How performance issues are analyzed
- **Continuous Optimization**: Ongoing performance improvement processes
### 6. Performance SLA Engineering
**Objective**: Define performance-related SLAs and engineering practices
**Required Elements:**
- **Performance SLIs**: Service Level Indicators for performance
- **Performance SLOs**: Service Level Objectives for performance
- **Performance SLAs**: Customer-facing performance commitments
- **Performance Error Budgets**: How performance error budgets are managed
- **Performance Incident Response**: How performance incidents are handled
## Information Gathering Requirements
### Performance Context Needed:
- Expected load and scale requirements
- Performance requirements and constraints
- Current performance baseline and bottlenecks
- Available performance testing tools and infrastructure
- Team performance engineering experience
### Validation Requirements:
- Performance engineering team review
- Load testing validation of requirements
- Infrastructure team validation of capacity plans
- Business stakeholder validation of performance SLAs
## Cross-Reference Requirements
### Must Reference:
- SRE framework and SLI/SLO requirements
- Technical architecture and infrastructure
- User experience requirements and expectations
- Business objectives and cost constraints
### Must Support:
- System architecture and design decisions
- Infrastructure planning and provisioning
- Operational monitoring and alerting
- Incident response and problem resolution
## Common Pitfalls to Avoid
### Performance Engineering Pitfalls:
- **Premature optimization**: Optimizing before understanding bottlenecks
- **Over-engineering**: Building more performance than needed
- **Ignoring user experience**: Focusing on technical metrics over user impact
- **Performance debt**: Deferring performance work until it becomes critical
### Testing Pitfalls:
- **Unrealistic testing**: Testing scenarios that don't match production
- **Insufficient test data**: Not testing with production-like data volumes
- **Environment differences**: Testing in environments unlike production
- **Manual testing only**: Not automating performance testing
## Edge Case Considerations
### When Performance Requirements are Extreme:
- Implement comprehensive performance engineering practices
- Use advanced optimization techniques and technologies
- Plan for extensive performance testing and validation
- Consider specialized performance engineering expertise
### When Resources are Constrained:
- Focus on highest-impact performance optimizations
- Use cost-effective performance improvement strategies
- Prioritize performance work based on business impact
- Consider performance vs. cost trade-offs carefully
## Validation Checkpoints
### Before Finalizing Section:
- [ ] Performance strategy aligns with business objectives
- [ ] High-load patterns are appropriate for scale requirements
- [ ] Capacity planning methodology is comprehensive
- [ ] Performance testing framework is thorough
- [ ] Monitoring and optimization processes are defined
### Cross-Section Validation:
- [ ] Performance requirements align with SRE framework
- [ ] Capacity plans support technical architecture
- [ ] Performance SLAs align with business commitments
- [ ] Testing strategy supports quality assurance
## Output Quality Standards
- Performance engineering strategy is comprehensive and practical
- High-load patterns are appropriate for scale requirements
- Capacity planning is systematic and data-driven
- Performance testing is thorough and automated
- Monitoring and optimization are continuous and effective