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Performance Engineering Standards

This document outlines the standards and best practices for performance engineering at Bayat.

Table of Contents

  1. Introduction
  2. Performance Goals and Metrics
  3. Performance Testing
  4. Frontend Performance
  5. Backend Performance
  6. Database Performance
  7. Network Performance
  8. Mobile Performance
  9. Cloud Performance
  10. Monitoring and Observability
  11. Performance Culture
  12. Tools and Resources

Introduction

Performance engineering is a systematic approach to designing, building, and maintaining systems that meet performance requirements. This document provides guidelines for implementing performance engineering practices throughout the software development lifecycle.

Why Performance Matters

  • Improved user experience and satisfaction
  • Higher conversion rates and engagement
  • Lower infrastructure costs
  • Better scalability
  • Reduced energy consumption
  • Competitive advantage
  • Higher search engine rankings

Performance Engineering Principles

  • Shift Left: Address performance early in the development lifecycle
  • Measure First: Base decisions on data, not assumptions
  • Continuous Improvement: Regularly assess and optimize performance
  • Holistic Approach: Consider all system components
  • User-Centric: Focus on metrics that impact user experience
  • Automation: Automate performance testing and monitoring
  • Collaboration: Involve all stakeholders in performance engineering

Performance Goals and Metrics

Key Performance Indicators (KPIs)

User Experience Metrics

  • Core Web Vitals:
    • Largest Contentful Paint (LCP): < 2.5 seconds
    • First Input Delay (FID): < 100 milliseconds
    • Cumulative Layout Shift (CLS): < 0.1
  • Additional Frontend Metrics:
    • First Contentful Paint (FCP): < 1.8 seconds
    • Time to Interactive (TTI): < 3.8 seconds
    • Total Blocking Time (TBT): < 300 milliseconds
    • Speed Index: < 3.4 seconds

Backend Metrics

  • Response Time:
    • API response time: < 100 milliseconds (p95)
    • Service response time: < 500 milliseconds (p95)
  • Throughput:
    • Requests per second (RPS)
    • Transactions per second (TPS)
  • Error Rate: < 0.1% of requests

Infrastructure Metrics

  • Resource Utilization:
    • CPU: < 70% average utilization
    • Memory: < 80% utilization
    • Disk I/O: < 70% utilization
    • Network: < 60% bandwidth utilization
  • Scalability:
    • Linear scaling with added resources
    • Autoscaling efficiency

Setting Performance Budgets

  • Define budgets for key metrics (e.g., page weight, time to interactive)
  • Allocate budgets to different components (JS, CSS, images, fonts)
  • Implement automated budget monitoring in CI/CD
  • Define actions when budgets are exceeded

Performance Requirements

  • Include specific, measurable performance requirements in user stories
  • Define acceptance criteria for performance
  • Document performance SLAs for different components
  • Specify performance requirements for different conditions (peak load, normal load)

Performance Testing

Types of Performance Tests

  • Load Testing: Verify system behavior under expected load
  • Stress Testing: Find breaking points under extreme conditions
  • Soak Testing: Verify system stability over extended periods
  • Spike Testing: Test system response to sudden load increases
  • Capacity Testing: Determine maximum capacity of the system
  • Scalability Testing: Verify system scaling capabilities
  • Volume Testing: Test with large data volumes
  • Isolation Testing: Measure performance of specific components

Performance Testing Process

  1. Define Test Objectives: Determine what to test and why
  2. Identify Key Scenarios: Select critical user journeys
  3. Define Test Environment: Set up representative environment
  4. Determine Metrics: Select metrics to measure
  5. Create Test Scripts: Develop automated test scripts
  6. Execute Tests: Run tests and collect data
  7. Analyze Results: Interpret data and identify issues
  8. Optimize: Address performance bottlenecks
  9. Retest: Verify improvements

Test Environment Considerations

  • Use production-like environments for accurate results
  • Consider data volume and variety
  • Simulate realistic network conditions
  • Account for third-party services
  • Use representative hardware configurations
  • Consider geographic distribution

Performance Testing in CI/CD

  • Integrate performance tests into CI/CD pipeline
  • Run baseline performance tests for every build
  • Conduct comprehensive tests for major releases
  • Implement performance regression detection
  • Automate performance test reporting
  • Define performance gates for deployment

Frontend Performance

JavaScript Optimization

  • Minimize and optimize JavaScript
  • Use code splitting and lazy loading
  • Implement tree shaking
  • Avoid render-blocking JavaScript
  • Optimize third-party scripts
  • Use web workers for CPU-intensive tasks
  • Implement efficient event handling
  • Optimize framework-specific code

Rendering Optimization

  • Minimize DOM size and nesting
  • Reduce layout thrashing
  • Optimize CSS selectors
  • Use CSS containment
  • Implement virtualization for long lists
  • Optimize animations (use CSS transitions, requestAnimationFrame)
  • Minimize main thread work
  • Implement server-side rendering or static generation when appropriate

Asset Optimization

  • Optimize images (format, size, compression)
  • Implement responsive images
  • Use modern image formats (WebP, AVIF)
  • Optimize fonts (subsetting, font-display)
  • Minify CSS and HTML
  • Implement resource hints (preload, prefetch, preconnect)
  • Use appropriate caching strategies
  • Implement HTTP/2 or HTTP/3

Mobile Web Optimization

  • Implement responsive design
  • Optimize for touch interactions
  • Consider reduced CPU/GPU capabilities
  • Optimize for variable network conditions
  • Implement offline capabilities
  • Consider battery usage
  • Test on actual mobile devices

Performance Monitoring

  • Implement Real User Monitoring (RUM)
  • Track Core Web Vitals
  • Monitor JavaScript execution
  • Track resource loading performance
  • Implement error tracking
  • Use performance observers
  • Analyze user-centric performance metrics

Backend Performance

API Optimization

  • Design efficient API endpoints
  • Implement appropriate caching
  • Use pagination for large data sets
  • Implement request batching
  • Optimize serialization/deserialization
  • Use compression for responses
  • Implement efficient error handling
  • Consider GraphQL for flexible data fetching

Concurrency and Parallelism

  • Implement asynchronous processing
  • Use thread pools effectively
  • Consider non-blocking I/O
  • Implement task partitioning
  • Use parallel processing for CPU-intensive tasks
  • Implement proper synchronization
  • Consider actor model for concurrent systems
  • Use appropriate concurrency patterns

Memory Management

  • Implement proper resource cleanup
  • Avoid memory leaks
  • Use object pooling for expensive objects
  • Implement appropriate caching strategies
  • Monitor memory usage
  • Consider garbage collection impact
  • Use memory-efficient data structures
  • Implement pagination for large data sets

Caching Strategies

  • Implement multi-level caching
  • Use distributed caching for scalability
  • Implement cache invalidation strategies
  • Consider cache coherence in distributed systems
  • Use appropriate cache expiration policies
  • Implement cache warming
  • Monitor cache hit rates
  • Optimize cache key design

Service Communication

  • Minimize inter-service communication
  • Use efficient serialization formats
  • Implement circuit breakers
  • Consider service mesh for complex systems
  • Use appropriate communication patterns (sync vs. async)
  • Implement retries with backoff
  • Monitor service dependencies
  • Consider data locality

Database Performance

Query Optimization

  • Write efficient queries
  • Use appropriate indexing
  • Avoid N+1 query problems
  • Implement query caching
  • Use database-specific optimization features
  • Consider denormalization for read-heavy workloads
  • Implement pagination for large result sets
  • Use query hints when appropriate

Schema Design

  • Design for query patterns
  • Use appropriate data types
  • Implement proper constraints
  • Consider partitioning for large tables
  • Use appropriate normalization level
  • Document schema design decisions
  • Consider read vs. write optimization
  • Implement versioning strategy

Connection Management

  • Use connection pooling
  • Monitor connection usage
  • Implement appropriate timeouts
  • Handle connection errors gracefully
  • Consider connection multiplexing
  • Implement proper transaction management
  • Monitor connection leaks
  • Consider connection load balancing

Database Scaling

  • Implement read replicas for read scaling
  • Consider sharding for write scaling
  • Implement proper data distribution
  • Use caching to reduce database load
  • Consider NoSQL for specific use cases
  • Implement database federation
  • Monitor replication lag
  • Design for eventual consistency when appropriate

Network Performance

Protocol Optimization

  • Use HTTP/2 or HTTP/3 when possible
  • Implement TLS 1.3
  • Optimize TCP settings
  • Consider UDP for specific use cases
  • Implement WebSockets for real-time communication
  • Use gRPC for service communication
  • Optimize DNS resolution
  • Implement proper header compression

Content Delivery

  • Use Content Delivery Networks (CDNs)
  • Implement edge caching
  • Consider edge computing for latency-sensitive operations
  • Optimize for global distribution
  • Implement geo-routing
  • Consider multi-CDN strategies
  • Monitor CDN performance
  • Implement origin shielding

Bandwidth Optimization

  • Implement compression (Gzip, Brotli)
  • Optimize payload size
  • Use streaming for large responses
  • Implement delta updates
  • Consider binary protocols
  • Optimize image and video delivery
  • Implement adaptive bitrate streaming
  • Monitor bandwidth usage

Latency Reduction

  • Minimize round trips
  • Implement connection keep-alive
  • Use DNS prefetching
  • Consider server location
  • Implement request prioritization
  • Reduce time to first byte (TTFB)
  • Use service workers for offline capabilities
  • Implement predictive prefetching

Mobile Performance

Native App Performance

  • Optimize app startup time
  • Implement efficient layouts
  • Use appropriate threading
  • Optimize memory usage
  • Implement efficient image loading
  • Consider battery impact
  • Optimize animations and transitions
  • Implement proper state management

Network Considerations

  • Implement offline capabilities
  • Optimize for variable network conditions
  • Minimize payload size
  • Implement efficient API communication
  • Use appropriate caching
  • Implement background synchronization
  • Consider data usage impact
  • Optimize for high latency

Resource Management

  • Implement efficient resource loading
  • Optimize battery usage
  • Consider thermal impact
  • Manage memory constraints
  • Implement proper background processing
  • Optimize for different device capabilities
  • Consider storage limitations
  • Implement proper cleanup

Testing and Profiling

  • Test on actual devices
  • Use platform-specific profiling tools
  • Monitor app size
  • Implement crash reporting
  • Test under various conditions
  • Consider fragmentation (Android)
  • Implement performance monitoring
  • Test on low-end devices

Cloud Performance

Cloud Architecture Optimization

  • Design for elasticity
  • Implement appropriate service tiers
  • Use managed services when appropriate
  • Consider serverless for specific workloads
  • Implement proper auto-scaling
  • Design for fault tolerance
  • Consider multi-region deployment
  • Optimize for cost-performance balance

Container Optimization

  • Optimize container images
  • Implement efficient orchestration
  • Consider resource allocation
  • Optimize startup time
  • Implement proper health checks
  • Consider container placement
  • Optimize for density vs. isolation
  • Implement proper logging and monitoring

Serverless Optimization

  • Optimize cold start times
  • Implement proper memory allocation
  • Consider execution duration
  • Optimize dependencies
  • Implement connection reuse
  • Consider state management
  • Optimize for concurrency
  • Implement proper error handling

Cost Optimization

  • Implement right-sizing
  • Use spot/preemptible instances when appropriate
  • Implement auto-scaling
  • Consider reserved instances for stable workloads
  • Optimize storage tiers
  • Implement proper resource cleanup
  • Monitor and analyze costs
  • Consider multi-cloud strategies

Monitoring and Observability

Performance Monitoring

  • Implement end-to-end monitoring
  • Use Real User Monitoring (RUM)
  • Implement synthetic monitoring
  • Monitor all system components
  • Set up alerting for performance issues
  • Implement trend analysis
  • Consider business impact monitoring
  • Use appropriate visualization

Metrics Collection

  • Collect user-centric metrics
  • Implement system metrics collection
  • Consider custom business metrics
  • Use appropriate sampling rates
  • Implement distributed tracing
  • Consider high-cardinality metrics
  • Implement proper metric aggregation
  • Consider metric retention policies

Log Management

  • Implement structured logging
  • Consider log levels and filtering
  • Implement centralized log collection
  • Use appropriate log rotation
  • Consider log analysis tools
  • Implement log correlation
  • Consider compliance requirements
  • Optimize log volume

Alerting and Dashboards

  • Implement meaningful alerts
  • Avoid alert fatigue
  • Create actionable dashboards
  • Implement proper on-call procedures
  • Consider alert prioritization
  • Implement runbooks for common issues
  • Create executive dashboards
  • Implement historical trend analysis

Performance Culture

Team Organization

  • Designate performance champions
  • Consider dedicated performance teams
  • Implement cross-functional performance reviews
  • Define clear performance responsibilities
  • Implement performance knowledge sharing
  • Consider performance communities of practice
  • Implement performance mentoring
  • Define escalation paths for performance issues

Education and Training

  • Provide performance engineering training
  • Share performance best practices
  • Implement performance workshops
  • Create performance documentation
  • Consider certification programs
  • Implement performance brown bags
  • Share case studies and success stories
  • Create performance learning paths

Process Integration

  • Include performance in definition of done
  • Implement performance reviews in code reviews
  • Consider performance impact in architecture reviews
  • Include performance in user stories
  • Implement performance retrospectives
  • Consider performance in sprint planning
  • Implement performance debt tracking
  • Include performance in technical debt management

Continuous Improvement

  • Implement regular performance assessments
  • Track performance metrics over time
  • Celebrate performance wins
  • Learn from performance incidents
  • Implement A/B testing for performance
  • Consider performance experimentation
  • Share performance learnings
  • Implement performance benchmarking

Tools and Resources

Recommended Tools

  • Frontend Performance:

    • Lighthouse
    • WebPageTest
    • Chrome DevTools
    • PageSpeed Insights
    • Core Web Vitals tools

  • Backend Performance:

    • JMeter
    • Gatling
    • k6
    • Locust
    • Artillery

  • Profiling Tools:

    • Node.js Profiler
    • Java Flight Recorder
    • .NET Profiler
    • Python cProfile
    • Go pprof

  • Monitoring Tools:

    • New Relic
    • Datadog
    • Dynatrace
    • Prometheus
    • Grafana

  • Database Tools:

    • Explain Plan analyzers
    • Index analyzers
    • Query monitors
    • Schema analyzers
    • Connection pool monitors

Performance Testing Templates

  • Load test plan template
  • Performance test report template
  • Performance requirements template
  • Performance budget template
  • Performance review checklist

Reference Materials

  • Performance engineering handbook
  • Web performance optimization guide
  • Database performance tuning guide
  • Cloud performance optimization guide
  • Mobile performance optimization guide