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react-performance-optimization skill

/skills/react-performance-optimization

This skill helps you optimize React performance by applying memoization, code splitting, and virtualization to speed renders and reduce bundle size.

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---
name: react-performance-optimization
description: React performance optimization patterns using memoization, code splitting, and efficient rendering strategies. Use when optimizing slow React applications, reducing bundle size, or improving user experience with large datasets.
---

# React Performance Optimization

Expert guidance for optimizing React application performance through memoization, code splitting, virtualization, and efficient rendering strategies.

## When to Use This Skill

- Optimizing slow-rendering React components
- Reducing bundle size for faster initial load times
- Improving responsiveness for large lists or data tables
- Preventing unnecessary re-renders in complex component trees
- Optimizing state management to reduce render cascades
- Improving perceived performance with code splitting
- Debugging performance issues with React DevTools Profiler

## Core Concepts

### React Rendering Optimization
React re-renders components when props or state change. Unnecessary re-renders waste CPU cycles and degrade user experience. Key optimization techniques:
- **Memoization**: Cache component renders and computed values
- **Code splitting**: Load code on demand for faster initial loads
- **Virtualization**: Render only visible list items
- **State optimization**: Structure state to minimize render cascades

### When to Optimize
1. **Profile first**: Use React DevTools Profiler to identify actual bottlenecks
2. **Measure impact**: Verify optimization improves performance
3. **Avoid premature optimization**: Don't optimize fast components

## Quick Reference

Load detailed patterns and examples as needed:

| Topic | Reference File |
| --- | --- |
| React.memo, useMemo, useCallback patterns | `skills/react-performance-optimization/references/memoization.md` |
| Code splitting with lazy/Suspense, bundle optimization | `skills/react-performance-optimization/references/code-splitting.md` |
| Virtualization for large lists (react-window) | `skills/react-performance-optimization/references/virtualization.md` |
| State management strategies, context splitting | `skills/react-performance-optimization/references/state-management.md` |
| useTransition, useDeferredValue (React 18+) | `skills/react-performance-optimization/references/concurrent-features.md` |
| React DevTools Profiler, performance monitoring | `skills/react-performance-optimization/references/profiling-debugging.md` |
| Common pitfalls and anti-patterns | `skills/react-performance-optimization/references/common-pitfalls.md` |

## Optimization Workflow

### 1. Identify Bottlenecks
```bash
# Open React DevTools Profiler
# Record interaction → Analyze flame graph → Find slow components
```

**Look for:**
- Components with yellow/red bars (slow renders)
- Unnecessary renders (same props/state)
- Expensive computations on every render

### 2. Apply Targeted Optimizations

**For unnecessary re-renders:**
- Wrap component with `React.memo`
- Use `useCallback` for stable function references
- Check for inline objects/arrays in props

**For expensive computations:**
- Use `useMemo` to cache results
- Move calculations outside render when possible

**For large lists:**
- Implement virtualization with react-window
- Ensure proper unique keys (not index)

**For slow initial load:**
- Add code splitting with `React.lazy`
- Analyze bundle size with webpack-bundle-analyzer
- Use dynamic imports for heavy dependencies

### 3. Verify Improvements
```bash
# Record new Profiler session
# Compare before/after metrics
# Ensure optimization actually helped
```

## Common Patterns

### Memoize Expensive Components
```jsx
import { memo } from 'react';

const ExpensiveList = memo(({ items, onItemClick }) => {
  return items.map(item => (
    <Item key={item.id} data={item} onClick={onItemClick} />
  ));
});
```

### Cache Computed Values
```jsx
import { useMemo } from 'react';

function DataTable({ items, filters }) {
  const filteredItems = useMemo(() => {
    return items.filter(item => filters.includes(item.category));
  }, [items, filters]);

  return <Table data={filteredItems} />;
}
```

### Stable Function References
```jsx
import { useCallback } from 'react';

function Parent() {
  const handleClick = useCallback((id) => {
    console.log('Clicked:', id);
  }, []);

  return <MemoizedChild onClick={handleClick} />;
}
```

### Code Split Routes
```jsx
import { lazy, Suspense } from 'react';

const Dashboard = lazy(() => import('./Dashboard'));
const Reports = lazy(() => import('./Reports'));

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

### Virtualize Large Lists
```jsx
import { FixedSizeList } from 'react-window';

function VirtualList({ items }) {
  return (
    <FixedSizeList
      height={600}
      itemCount={items.length}
      itemSize={80}
      width="100%"
    >
      {({ index, style }) => (
        <div style={style}>{items[index].name}</div>
      )}
    </FixedSizeList>
  );
}
```

## Common Mistakes

1. **Over-memoization**: Don't memoize simple, fast components (adds overhead)
2. **Inline objects/arrays**: New references break memoization (`config={{ theme: 'dark' }}`)
3. **Missing dependencies**: Stale closures in useCallback/useMemo
4. **Index as key**: Breaks reconciliation when list order changes
5. **Single large context**: Causes widespread re-renders on any update
6. **No profiling**: Optimizing without measuring wastes time

## Performance Checklist

Before optimizing:
- [ ] Profile with React DevTools to identify bottlenecks
- [ ] Measure baseline performance metrics

Optimization targets:
- [ ] Memoize expensive components with stable props
- [ ] Cache computed values with useMemo (if actually expensive)
- [ ] Use useCallback for functions passed to memoized children
- [ ] Implement code splitting for routes and heavy components
- [ ] Virtualize lists with >100 items
- [ ] Provide stable keys for list items (unique IDs, not index)
- [ ] Split state by update frequency
- [ ] Use concurrent features (useTransition, useDeferredValue) for responsiveness

After optimizing:
- [ ] Profile again to verify improvements
- [ ] Check bundle size reduction (if applicable)
- [ ] Ensure no regressions in functionality

## Resources

- **React Docs - Performance**: https://react.dev/learn/render-and-commit
- **React DevTools**: Browser extension for profiling
- **react-window**: https://github.com/bvaughn/react-window
- **Bundle analyzers**: webpack-bundle-analyzer, rollup-plugin-visualizer
- **Lighthouse**: Chrome DevTools performance audit

Overview

This skill delivers practical React performance optimization patterns centered on memoization, code splitting, virtualization, and efficient rendering strategies. It helps identify real bottlenecks, reduce bundle size, and improve responsiveness for large datasets and complex component trees. Use it to guide targeted changes and verify measurable improvements.

How this skill works

The skill inspects rendering hotspots using profiling guidance and recommends targeted fixes: component memoization, stable callbacks, and cached computations to prevent unnecessary re-renders. It also recommends code splitting and lazy loading to shrink initial bundles, and virtualization for large lists to render only visible items. Each recommendation is paired with verification steps using the React DevTools Profiler and bundle analysis.

When to use it

  • When components render slowly or UI janks under interaction
  • When initial page load is heavy and bundle size needs reduction
  • When displaying large lists, tables, or infinite scrolls
  • When many child components re-render unnecessarily
  • When state updates cause broad render cascades
  • When you need measurable improvements before shipping

Best practices

  • Profile first with React DevTools Profiler and measure baseline metrics
  • Apply targeted optimizations only to identified hotspots to avoid premature work
  • Use React.memo, useMemo, and useCallback where props or computations are expensive
  • Virtualize lists (react-window) for lists > ~100 items and provide stable unique keys
  • Code-split routes and heavy components with React.lazy and Suspense
  • Split context/state by update frequency to limit re-render scope

Example use cases

  • Wrap a heavy list-rendering component in React.memo and provide stable callbacks to stop frequent re-renders
  • Introduce react-window for a table of thousands of rows to improve scroll performance
  • Replace a monolithic context with multiple smaller contexts so unrelated updates don’t re-render the whole tree
  • Add React.lazy to route-level components and analyze bundle size reduction with a bundle analyzer
  • Use useMemo to cache an expensive filter or sort operation and confirm improvement via Profiler

FAQ

How do I know which components to optimize first?

Profile interactions with React DevTools Profiler and target components with long render durations or frequent unnecessary renders.

When is memoization harmful?

Avoid memoizing simple, fast components; memoization adds overhead and can hurt performance when the render cost is low.