Web Performance Patterns

Quick Guide: Bundle budgets: < 200KB main bundle gzipped. Core Web Vitals: LCP < 2.5s, INP < 200ms, CLS < 0.1. Profile before optimizing -- measure actual bottlenecks, don't guess. Lazy load routes and heavy libraries. Use React Compiler (React 19+) for automatic memoization; manual memo only when profiling proves a bottleneck. Monitor real users with web-vitals library, not just Lighthouse.

<critical_requirements>

CRITICAL: Before Optimizing Performance

(You MUST profile BEFORE optimizing - measure actual bottlenecks with browser DevTools, framework profiler, or Lighthouse)

(You MUST set performance budgets BEFORE building features - bundle size limits and Core Web Vitals targets)

(You MUST use named constants for ALL performance thresholds - no magic numbers like

200

2.5

(You MUST monitor Core Web Vitals in production - track LCP, INP, CLS for real users, not just lab metrics)

(You MUST lazy load route components and heavy libraries - code splitting prevents large initial bundles)

</critical_requirements>

Auto-detection: Core Web Vitals, bundle size optimization, LCP, INP, CLS, lazy loading, code splitting, memoization, React Compiler, performance monitoring, web-vitals library, bundle budget, virtualization, react-window, TanStack Virtual

When to use:

• Optimizing Core Web Vitals (LCP < 2.5s, INP < 200ms, CLS < 0.1)
• Setting and enforcing bundle size budgets (< 200KB main bundle)
• Implementing runtime performance patterns (strategic memo, lazy loading, virtualization)
• Monitoring performance with web-vitals library in production
• Code splitting and tree shaking to reduce initial bundle

When NOT to use:

• Before measuring (premature optimization adds complexity without benefit)
• For simple components (memoizing cheap renders adds overhead)
• Internal admin tools with < 10 users (ROI too low)
• Prototypes and MVPs (optimize after validating product-market fit)

Key patterns covered:

• Core Web Vitals targets and improvement strategies (LCP, INP, CLS)
• Bundle size budgets (< 200KB main, < 500KB total initial load)
• Strategic memoization (profile first; React Compiler handles most cases)
• Code splitting (route-based lazy loading, dynamic imports, tree shaking)
• Image optimization (modern formats, lazy loading, responsive images)

Detailed Resources:

• examples/core.md - React memoization, virtual scrolling, debouncing
• examples/code-splitting.md - Lazy loading, tree shaking, bundle budgets
• examples/web-vitals.md - LCP, INP, CLS patterns and monitoring
• examples/image-optimization.md - Image formats, lazy loading, responsive images
• reference.md - Decision frameworks and anti-patterns

Philosophy

Performance is a feature, not an afterthought. Fast applications improve user experience, conversion rates, and SEO rankings. Performance optimization requires measurement before action, budgets before building, and monitoring in production.

Core principles:

• Measure first, optimize second - Profile actual bottlenecks, don't guess
• Set budgets early - Define bundle size limits and Core Web Vitals targets before building
• Monitor real users - Lab metrics (Lighthouse) differ from real-world performance (RUM)
• Optimize strategically - Memoize expensive operations, not everything
• Lazy load by default - Load code when needed, not upfront

Core Patterns

Pattern 1: Bundle Size Budgets

Set and enforce bundle size limits to prevent bloat. Main bundle should be < 200KB gzipped for fast downloads on 3G networks.

// constants/bundle-budgets.ts
export const BUNDLE_SIZE_BUDGETS_KB = {
  MAIN_BUNDLE_GZIPPED: 200,
  VENDOR_BUNDLE_GZIPPED: 150,
  ROUTE_BUNDLE_GZIPPED: 100,
  TOTAL_INITIAL_LOAD_GZIPPED: 500,
  MAIN_CSS_GZIPPED: 50,
  CRITICAL_CSS_INLINE: 14, // Fits in first TCP packet
} as const;

Why these limits: 200 KB ≈ 1 second download on 3G, faster Time to Interactive (TTI), better mobile performance

Recommended budgets:

• Main bundle: < 200 KB gzipped
• Vendor bundle: < 150 KB gzipped
• Route bundles: < 100 KB each gzipped
• Total initial load: < 500 KB gzipped
• Main CSS: < 50 KB gzipped
• Critical CSS: < 14 KB inlined (fits in first TCP packet)

For enforcement examples, see examples/code-splitting.md.

Pattern 2: Core Web Vitals Optimization

Optimize for Google's Core Web Vitals: LCP < 2.5s, INP < 200ms, CLS < 0.1. These metrics impact SEO and user experience.

// constants/web-vitals.ts
export const CORE_WEB_VITALS_THRESHOLDS = {
  LCP_SECONDS: 2.5, // Largest Contentful Paint
  INP_MS: 200, // Interaction to Next Paint
  CLS_SCORE: 0.1, // Cumulative Layout Shift
  FCP_SECONDS: 1.8, // First Contentful Paint
  TTI_SECONDS: 3.8, // Time to Interactive
  TBT_MS: 300, // Total Blocking Time
  TTFB_MS: 800, // Time to First Byte
} as const;

LCP (Largest Contentful Paint): < 2.5s

Measures loading performance -- when the largest visible element renders.

How to improve: Optimize images (modern formats, preload hero images), minimize render-blocking resources, use CDN for static assets, SSR or SSG for critical content.

INP (Interaction to Next Paint): < 200ms

Measures interactivity across ALL user interactions (replaced FID in March 2024). Includes input delay, processing time, and presentation delay.

How to improve: Minimize JavaScript execution time, code split to load less JS upfront, use web workers for heavy computation, break up long tasks (> 50ms) with

scheduler.yield()

setTimeout

CLS (Cumulative Layout Shift): < 0.1

Measures visual stability -- prevents unexpected layout shifts.

How to improve: Set explicit image/video dimensions, reserve space for dynamic content (ads, embeds), avoid injecting content above existing content, use

font-display: swap

size-adjust

For detailed examples and monitoring setup, see examples/web-vitals.md.

Pattern 3: Code Splitting and Lazy Loading

Lazy load route components and heavy libraries. Code splitting keeps the initial bundle small by loading code on demand.

import { lazy, Suspense } from 'react';

// Route-based splitting - each route is a separate chunk
const Dashboard = lazy(() => import('./pages/dashboard'));
const Reports = lazy(() => import('./pages/reports'));

export function App() {
  return (
    <Suspense fallback={<PageLoader />}>
      <Routes>
        <Route path="/dashboard" element={<Dashboard />} />
        <Route path="/reports" element={<Reports />} />
      </Routes>
    </Suspense>
  );
}

Why good: Splits bundle by route, loads components on demand, users only download what they navigate to

When to lazy load: Route components, heavy feature modules, modals/dialogs, below-fold content

When NOT to lazy load: Above-fold components, error boundaries, loading states

For tree shaking and bundle enforcement, see examples/code-splitting.md.

Pattern 4: Strategic Memoization

Profile before memoizing. React Compiler (v1.0, Oct 2025) auto-memoizes in most cases. Manual memo only when profiling proves a bottleneck.

// Only memoize when profiling shows > 5ms render time
const EXPENSIVE_RENDER_MS = 5;

// ✅ Expensive calculation with large dataset
const sortedRows = useMemo(
  () => [...rows].sort((a, b) => compareValues(a[sortColumn], b[sortColumn])),
  [rows, sortColumn],
);

// ❌ Trivial calculation - memo overhead exceeds cost
const doubled = useMemo(() => value * 2, [value]);

React Compiler (React 19+): Automatically memoizes components, values, and functions at build time. Manual

useMemo

useCallback

React.memo

For complete memoization patterns, see examples/core.md.

<red_flags>

RED FLAGS

High Priority Issues:

• No performance budgets defined -- bundle sizes grow unnoticed, Core Web Vitals degrade
• Memoizing everything without profiling -- adds overhead, increases complexity, premature optimization
• Not lazy loading routes -- massive initial bundles, slow Time to Interactive
• Importing entire libraries (

  import _ from 'lodash'

  ) -- bundles unused code, prevents tree shaking
• Not optimizing images -- images are 50%+ of page weight; modern formats reduce size 30-50%
• Blocking main thread with heavy computation -- causes high INP, use web workers or break up long tasks

Medium Priority Issues:

• Not monitoring Core Web Vitals in production -- lab metrics differ from real users
• Rendering 100+ items without virtualization -- DOM bloat, slow scrolling
• No bundle size enforcement in CI -- regressions slip through code review
• Using CommonJS imports (

  require()

  ) -- prevents tree shaking

Gotchas & Edge Cases:

• React.memo

  uses shallow comparison -- deep object props always trigger re-render

• useMemo

  /

  useCallback

  have overhead -- only use for expensive operations (> 5ms)
• React Compiler (v1.0) handles memoization automatically -- manual memo is rarely needed
• Lighthouse scores differ from real users -- always monitor RUM with web-vitals
• Code splitting increases total bundle size slightly (runtime overhead) -- net win for initial load
• Virtual scrolling breaks browser find-in-page (Cmd+F)
• Lazy loading doesn't work in SSR -- components load on client mount
• AVIF support is ~95% (2026) -- always provide WebP fallbacks
• Bundle size budgets should account for gzip/brotli compression

</red_flags>

<critical_reminders>

CRITICAL REMINDERS

(You MUST profile BEFORE optimizing - measure actual bottlenecks with browser DevTools, framework profiler, or Lighthouse)

(You MUST set performance budgets BEFORE building features - bundle size limits and Core Web Vitals targets)

(You MUST use named constants for ALL performance thresholds - no magic numbers like

200

2.5

(You MUST monitor Core Web Vitals in production - track LCP, INP, CLS for real users, not just lab metrics)

(You MUST lazy load route components and heavy libraries - code splitting prevents large initial bundles)

Failure to follow these rules will result in slow applications, poor Core Web Vitals, large bundles, and degraded user experience.

</critical_reminders>