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Performance Optimization Guide

Overview

This guide outlines performance optimizations implemented and recommended for the GitHub PR Dashboard.

Code Splitting & Lazy Loading

Implementation

// Components are lazy loaded
const LazyDashboard = lazy(() => import('../pages/Dashboard'));
const LazyIssuesPage = lazy(() => import('../pages/IssuesPage'));

Benefits

  • Reduced initial bundle size
  • Faster time to first meaningful paint
  • Better user experience on slow connections

React Query Optimization

Configuration

const queryClient = new QueryClient({
  defaultOptions: {
    queries: {
      staleTime: 5 * 60 * 1000, // 5 minutes
      retry: (failureCount, error) => {
        if (error?.status === 401 || error?.status === 403) return false;
        return failureCount < 3;
      },
      retryDelay: (attemptIndex) => Math.min(1000 * 2 ** attemptIndex, 30000),
    },
  },
});

Benefits

  • Intelligent caching reduces API calls
  • Background refetching keeps data fresh
  • Optimistic updates improve perceived performance

Rate Limiting

Current Implementation

  • Configurable requests per minute limit
  • Queue-based request management
  • Exponential backoff for retries
  • Priority handling for urgent requests

Metrics

  • Default: 200 requests per minute
  • Adjustable via environment variable
  • Automatic cleanup of old timestamps

Bundle Optimization

Recommendations

  1. Tree Shaking: Ensure unused code is eliminated
  2. Code Splitting: Route-based splitting implemented
  3. Compression: Enable gzip/brotli compression
  4. CDN: Use CDN for static assets

Build Analysis

npm run analyze  # Analyze bundle size

Memory Management

Best Practices

  • AbortController for cancelled requests
  • Cleanup intervals in rate limiter
  • Proper useEffect cleanup
  • Intersection Observer for conditional loading

Memory Leaks Prevention

useEffect(() => {
  const controller = new AbortController();
  
  fetchData({ signal: controller.signal });
  
  return () => controller.abort();
}, []);

Virtualization

For Large Lists

When displaying many PRs/issues, consider implementing:

import { FixedSizeList as List } from 'react-window';

const VirtualizedList = ({ items }) => (
  <List
    height={600}
    itemCount={items.length}
    itemSize={100}
  >
    {({ index, style }) => (
      <div style={style}>
        <PullRequestCard pr={items[index]} />
      </div>
    )}
  </List>
);

Image Optimization

Avatar Loading

  • Use appropriate image sizes
  • Implement lazy loading for avatars
  • Consider WebP format support
  • Add loading states

Network Optimization

API Calls

  • Batch related requests
  • Use proper caching headers
  • Implement request deduplication
  • Optimize payload sizes

Preloading

<link rel="preconnect" href="https://api.github.com" />
<link rel="preconnect" href="https://avatars.githubusercontent.com" />

Performance Monitoring

Metrics to Track

  • Time to First Byte (TTFB)
  • First Contentful Paint (FCP)
  • Largest Contentful Paint (LCP)
  • Cumulative Layout Shift (CLS)
  • First Input Delay (FID)

Tools

  • React DevTools Profiler
  • Chrome Lighthouse
  • Web Vitals extension
  • Bundle analyzer

Performance Budget

Targets

  • Bundle size: < 1MB compressed
  • First load: < 3 seconds
  • Subsequent loads: < 1 second
  • API response time: < 500ms

Monitoring

npm run build           # Build and check sizes
npm run analyze         # Analyze bundle

Optimization Checklist

Code Level

  • Implement React.memo for expensive components
  • Use useMemo for expensive calculations
  • Implement useCallback for stable references
  • Lazy load non-critical components
  • Optimize re-renders with proper dependencies

Network Level

  • Enable HTTP/2
  • Use compression (gzip/brotli)
  • Implement proper caching strategies
  • Optimize API payload sizes
  • Use CDN for static assets

Bundle Level

  • Code splitting implemented
  • Tree shaking enabled
  • Dead code elimination
  • Proper source maps for debugging
  • Bundle size monitoring

Performance Testing

Load Testing

  • Test with large datasets (1000+ PRs)
  • Simulate slow network conditions
  • Test on low-end devices
  • Monitor memory usage over time

Benchmarking

// Performance measurement
const startTime = performance.now();
await fetchData();
const endTime = performance.now();
console.log(`Operation took ${endTime - startTime} milliseconds`);