Web Performance Audit

Web Performance Audit

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Web performance audits measure load times, identify bottlenecks, and guide optimization efforts to create faster, better user experiences.

When to Use

• Regular performance monitoring
• After major changes
• User complaints about slowness
• SEO optimization
• Mobile optimization
• Performance baseline setting

Quick Start

Minimal working example:

Core Web Vitals (Google):

Largest Contentful Paint (LCP):
  Measure: Time to load largest visible element
  Good: <2.5 seconds
  Poor: >4 seconds
  Impacts: User perception of speed

First Input Delay (FID):
  Measure: Time from user input to response
  Good: <100 milliseconds
  Poor: >300 milliseconds
  Impacts: Responsiveness

Cumulative Layout Shift (CLS):
  Measure: Visual stability (unexpected layout shifts)
  Good: <0.1
  Poor: >0.25
  Impacts: User frustration

---

Additional Metrics:

First Contentful Paint (FCP):
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Best Practices

✅ DO

• Measure regularly (not just once)
• Use field data (real users) + lab data
• Focus on Core Web Vitals
• Set realistic targets
• Prioritize by impact
• Monitor continuously
• Setup performance budgets
• Test on slow networks
• Include mobile in testing
• Document improvements

❌ DON'T

• Ignore field data
• Focus on one metric only
• Set impossible targets
• Optimize without measurement
• Forget about images
• Ignore JavaScript costs
• Skip mobile performance
• Over-optimize prematurely
• Forget about monitoring
• Expect improvements without effort

Monitoring & Continuous Improvement

Monitoring & Continuous Improvement

// Setup performance monitoring

class PerformanceMonitoring {
  setupMonitoring() {
    return {
      tools: [
        "Google Analytics (Web Vitals)",
        "Datadog or New Relic",
        "Sentry for errors",
        "Custom monitoring",
      ],
      metrics: [
        "LCP (Largest Contentful Paint)",
        "FID (First Input Delay)",
        "CLS (Cumulative Layout Shift)",
        "FCP (First Contentful Paint)",
        "TTI (Time to Interactive)",
      ],
      frequency: "Real-time monitoring",
      alerts: {
        lcp_degradation: "Alert if >3 seconds",
        fid_degradation: "Alert if >200ms",
        cls_degradation: "Alert if >0.2",
      },
    };
  }

  defineBaselines(metrics) {
    return {
      baseline: {
        lcp: metrics.lcp,
        fid: metrics.fid,
        cls: metrics.cls,
      },
      targets: {
        lcp: metrics.lcp * 0.9, // 10% improvement
        fid: metrics.fid * 0.8,
        cls: metrics.cls * 0.8,
      },
      review_frequency: "Weekly",
      improvement_tracking: "Month-over-month trends",
    };
  }

  setupPerformanceBudget() {
    return {
      javascript: {
        target: "150KB gzipped",
        monitor: "Every build",
        alert: "If exceeds 160KB",
      },
      css: {
        target: "50KB gzipped",
        monitor: "Every build",
        alert: "If exceeds 55KB",
      },
      images: {
        target: "500KB total",
        monitor: "Every deployment",
        alert: "If exceeds 550KB",
      },
    };
  }
}

Optimization Strategies

Optimization Strategies

Performance Optimization Roadmap:

Quick Wins (1-2 days):
  - Enable gzip compression
  - Minify CSS/JavaScript
  - Compress images (lossless)
  - Remove unused CSS
  - Defer non-critical JavaScript
  - Preload critical fonts

Medium Effort (1-2 weeks):
  - Implement lazy loading
  - Code splitting (split routes)
  - Service worker for caching
  - Image optimization (WebP, srcset)
  - Critical CSS extraction
  - HTTP/2 server push

Long-term (1-3 months):
  - Migrate to faster framework
  - Database query optimization
  - Content delivery optimization
  - Architecture refactor
  - CDN implementation
  - Build process optimization

---

Optimization Checklist:

Network:
  [ ] Gzip compression enabled
  [ ] Brotli compression enabled
  [ ] HTTP/2 enabled
  [ ] CDN configured
  [ ] Browser caching configured
  [ ] Asset fingerprinting

JavaScript:
  [ ] Code split by route
  [ ] Unused code removed
  [ ] Minified and mangled
  [ ] Source maps generated
  [ ] Deferred non-critical

CSS:
  [ ] Critical CSS extracted
  [ ] Unused CSS removed
  [ ] Minified
  [ ] Preloaded fonts
  [ ] WOFF2 format used

Images:
  [ ] Optimized and compressed
  [ ] WebP with fallback
  [ ] Responsive srcset
  [ ] Lazy loading
  [ ] SVG where possible

Performance Analysis Process

Performance Analysis Process

# Conduct performance audit

class PerformanceAudit:
    def measure_performance(self, url):
        """Baseline measurements"""
        return {
            'desktop_metrics': self.run_lighthouse_desktop(url),
            'mobile_metrics': self.run_lighthouse_mobile(url),
            'field_data': self.get_field_data(url),  # Real user data
            'lab_data': self.run_synthetic_tests(url),  # Lab measurements
            'comparative': self.compare_to_competitors(url)
        }

    def identify_opportunities(self, metrics):
        """Find improvement areas"""
        opportunities = []

        if metrics['fcp'] > 1.8:
            opportunities.append({
                'issue': 'First Contentful Paint slow',
                'current': metrics['fcp'],
                'target': 1.8,
                'impact': 'High',
                'solutions': [
                    'Reduce CSS/JS for critical path',
                    'Preload critical fonts',
                    'Defer non-critical JavaScript'
                ]
            })

        if metrics['cls'] > 0.1:
            opportunities.append({
                'issue': 'Cumulative Layout Shift high',
                'current': metrics['cls'],
                'target': 0.1,
                'impact': 'High',
                'solutions': [
                    'Reserve space for dynamic content',
                    'Avoid inserting content above existing',
                    'Use transform for animations'
                ]
            })

        return sorted(opportunities, key=lambda x: x['impact'])

    def create_audit_report(self, metrics, opportunities):
        """Generate comprehensive report"""
        return {
            'overall_score': self.calculate_score(metrics),
            'current_metrics': metrics,
            'target_metrics': self.define_targets(metrics),
            'opportunities': opportunities,
            'quick_wins': self.identify_quick_wins(opportunities),
            'timeline': self.estimate_effort(opportunities),
            'recommendations': self.prioritize_recommendations(opportunities)
        }

Performance Metrics

Performance Metrics

Core Web Vitals (Google):

Largest Contentful Paint (LCP):
  Measure: Time to load largest visible element
  Good: <2.5 seconds
  Poor: >4 seconds
  Impacts: User perception of speed

First Input Delay (FID):
  Measure: Time from user input to response
  Good: <100 milliseconds
  Poor: >300 milliseconds
  Impacts: Responsiveness

Cumulative Layout Shift (CLS):
  Measure: Visual stability (unexpected layout shifts)
  Good: <0.1
  Poor: >0.25
  Impacts: User frustration

---

Additional Metrics:

First Contentful Paint (FCP):
  Measure: First visible content appears
  Target: <1.8 seconds

Time to Interactive (TTI):
  Measure: Page is fully interactive
  Target: <3.8 seconds

Total Blocking Time (TBT):
  Measure: JavaScript blocking time
  Target: <300ms

Interaction to Next Paint (INP):
  Measure: Latency of user interactions
  Target: <200ms

---

Measurement Tools:
  - Google PageSpeed Insights
  - Lighthouse (Chrome DevTools)
  - WebPageTest
  - New Relic
  - Datadog
  - GTmetrix