React Performance Optimizer
Expert in diagnosing and fixing React performance issues to achieve buttery-smooth 60fps experiences.
When to Use
✅ Use for:
Slow component re-renders Large lists (>100 items) causing lag Bundle size >500KB (gzipped) Time to Interactive >3 seconds Janky scrolling or animations Memory leaks from unmounted components
❌ NOT for:
Apps with <10 components (premature optimization) Backend API slowness (fix the API) Network latency (use caching/CDN) Non-React frameworks (use framework-specific tools) Quick Decision Tree Is your React app slow? ├── Profiler shows >16ms renders? → Use memoization ├── Lists with >100 items? → Use virtualization ├── Bundle size >500KB? → Code splitting ├── Lighthouse score <70? → Multiple optimizations └── Feels fast enough? → Don't optimize yet
Technology Selection Performance Tools (2024) Tool Purpose When to Use React DevTools Profiler Find slow components Always start here Lighthouse Overall performance score Before/after comparison webpack-bundle-analyzer Identify large dependencies Bundle >500KB why-did-you-render Unnecessary re-renders Debug re-render storms React Compiler (2024+) Automatic memoization React 19+
Timeline:
2018: React.memo, useMemo, useCallback introduced 2020: Concurrent Mode (now Concurrent Rendering) 2022: Automatic batching in React 18 2024: React Compiler (automatic optimization) 2025+: React Compiler expected to replace manual memoization Common Anti-Patterns Anti-Pattern 1: Premature Memoization
Novice thinking: "Wrap everything in useMemo for speed"
Problem: Adds complexity and overhead for negligible gains.
Wrong approach:
// ❌ Over-optimization
function UserCard({ user }) {
const fullName = useMemo(() => ${user.first} ${user.last}, [user]);
const age = useMemo(() => new Date().getFullYear() - user.birthYear, [user]);
return
Why wrong: String concatenation is faster than useMemo overhead.
Correct approach:
// ✅ Simple is fast
function UserCard({ user }) {
const fullName = ${user.first} ${user.last};
const age = new Date().getFullYear() - user.birthYear;
return
Rule of thumb: Only memoize if:
Computation takes >5ms (use Profiler to measure) Result used in dependency array Prevents child re-renders Anti-Pattern 2: Not Memoizing Callbacks
Problem: New function instance on every render breaks React.memo.
Wrong approach:
// ❌ Child re-renders on every parent render function Parent() { const [count, setCount] = useState(0);
return (
const Child = React.memo(({ onUpdate }) => { return ; });
Why wrong: Arrow function creates new reference → React.memo useless.
Correct approach:
// ✅ Stable callback reference function Parent() { const [count, setCount] = useState(0);
const handleUpdate = useCallback(() => { setCount(c => c + 1); // Updater function avoids dependency }, []);
return
const Child = React.memo(({ onUpdate }) => { return ; });
Anti-Pattern 3: Rendering Large Lists Without Virtualization
Problem: Rendering 1000+ DOM nodes causes lag.
Symptom: Scrolling feels janky, initial render slow.
Wrong approach:
// ❌ Renders all 10,000 items function UserList({ users }) { return (
Correct approach:
// ✅ Only renders visible items import { FixedSizeList } from 'react-window';
function UserList({ users }) {
return (
Impact: 10,000 items: 5 seconds → 50ms render time.
Anti-Pattern 4: No Code Splitting
Problem: 2MB bundle downloaded upfront, slow initial load.
Wrong approach:
// ❌ Everything in main bundle import AdminPanel from './AdminPanel'; // 500KB import Dashboard from './Dashboard'; import Settings from './Settings';
function App() {
return (
Correct approach:
// ✅ Lazy load routes import { lazy, Suspense } from 'react';
const AdminPanel = lazy(() => import('./AdminPanel')); const Dashboard = lazy(() => import('./Dashboard')); const Settings = lazy(() => import('./Settings'));
function App() {
return (
Impact: Initial bundle: 2MB → 300KB.
Anti-Pattern 5: Expensive Operations in Render
Problem: Heavy computation on every render.
Wrong approach:
// ❌ Sorts on every render (even when data unchanged) function ProductList({ products }) { const sorted = products.sort((a, b) => b.price - a.price);
return
Correct approach:
// ✅ Memoize expensive operation function ProductList({ products }) { const sorted = useMemo( () => [...products].sort((a, b) => b.price - a.price), [products] );
return
Implementation Patterns Pattern 1: React.memo for Pure Components // Prevent re-render when props unchanged const ExpensiveComponent = React.memo(({ data }) => { // Complex rendering logic return
// With custom comparison const UserCard = React.memo( ({ user }) =>
Pattern 2: useMemo for Expensive Calculations function DataTable({ rows, columns }) { const sortedAndFiltered = useMemo(() => { console.log('Recomputing...'); // Only logs when rows/columns change
return rows
.filter(row => row.visible)
.sort((a, b) => a.timestamp - b.timestamp);
}, [rows, columns]);
return
Pattern 3: useCallback for Stable References function SearchBox({ onSearch }) { const [query, setQuery] = useState('');
// Stable reference, doesn't break child memoization const handleSubmit = useCallback(() => { onSearch(query); }, [query, onSearch]);
return (
); }Pattern 4: Virtualization (react-window) import { VariableSizeList } from 'react-window';
function MessageList({ messages }) { const getItemSize = (index) => { // Dynamic heights based on content return messages[index].text.length > 100 ? 80 : 50; };
return (
Pattern 5: Code Splitting with React.lazy // Route-based splitting const routes = [ { path: '/home', component: lazy(() => import('./Home')) }, { path: '/about', component: lazy(() => import('./About')) }, { path: '/contact', component: lazy(() => import('./Contact')) } ];
// Component-based splitting const HeavyChart = lazy(() => import('./HeavyChart'));
function Dashboard() { const [showChart, setShowChart] = useState(false);
return (
{showChart && (
<Suspense fallback={<Spinner />}>
<HeavyChart />
</Suspense>
)}
</div>
); }
Production Checklist □ Profiler analysis completed (identified slow components) □ Large lists use virtualization (>100 items) □ Routes code-split with React.lazy □ Heavy components lazy-loaded □ Callbacks memoized with useCallback □ Expensive computations use useMemo □ Pure components wrapped in React.memo □ Bundle analyzed (no duplicate dependencies) □ Tree-shaking enabled (ESM imports) □ Images optimized and lazy-loaded □ Lighthouse score >90 □ Time to Interactive <3 seconds
When to Use vs Avoid Scenario Optimize? Rendering 1000+ list items ✅ Yes - virtualize Sorting/filtering large arrays ✅ Yes - useMemo Passing callbacks to memoized children ✅ Yes - useCallback String concatenation ❌ No - fast enough Simple arithmetic ❌ No - don't memoize 10-item list ❌ No - premature optimization References /references/profiling-guide.md - How to use React DevTools Profiler /references/bundle-optimization.md - Reduce bundle size strategies /references/memory-leaks.md - Detect and fix memory leaks Scripts scripts/performance_audit.ts - Automated performance checks scripts/bundle_analyzer.sh - Analyze and visualize bundle
This skill guides: React performance optimization | Memoization | Virtualization | Code splitting | Bundle optimization | Profiling
← 返回排行榜