Performance Optimization

Performance Optimization - Making Software Fast

When to use this skill

• Improving slow page load times and performance
• Reducing JavaScript bundle sizes
• Optimizing React component rendering with memoization
• Implementing code splitting and lazy loading
• Configuring browser and server-side caching
• Optimizing images with next/image or similar
• Profiling performance bottlenecks with DevTools
• Implementing virtual scrolling for large datasets
• Optimizing database queries and N+1 problems
• Improving Core Web Vitals (LCP, FID, CLS)
• Implementing progressive image loading
• Reducing Time to Interactive (TTI)

When to use this skill

• Applications are slow, users complain about lag, or you need to improve response times, throughput, or resource usage.
• When working on related tasks or features
• During development that requires this expertise

Use when: Applications are slow, users complain about lag, or you need to improve response times, throughput, or resource usage.

Core Principles

1. Measure First, Optimize Second - Never guess at bottlenecks
2. 80/20 Rule - 20% of code causes 80% of performance issues
3. Premature Optimization is Evil - Make it work, make it right, then make it fast
4. Profile, Don't Assume - Surprises await; your intuition is often wrong
5. Set Performance Budgets - Define acceptable limits before optimizing

Performance Measurement

Establish Baselines

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# Web Vitals (Frontend)
- FCP (First Contentful Paint): < 1.8s
- LCP (Largest Contentful Paint): < 2.5s
- FID (First Input Delay): < 100ms
- CLS (Cumulative Layout Shift): < 0.1
- TTFB (Time to First Byte): < 600ms

# Backend
- API Response Time: < 200ms (p95)
- Database Query Time: < 50ms (p95)
- Throughput: requests per second
- Error Rate: < 0.1%

Profiling Tools

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# Frontend
- Chrome DevTools Performance tab
- Lighthouse CI
- WebPageTest
- webpack-bundle-analyzer

# Backend
- Node.js: node --prof, clinic.js
- Python: cProfile, py-spy
- Database: EXPLAIN ANALYZE, slow query logs
- APM: New Relic, Datadog, Sentry Performance

# System
- top, htop (CPU/Memory)
- iostat (Disk I/O)
- netstat, iftop (Network)

Frontend Performance

1. Reduce JavaScript Bundle Size

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// Before - importing entire library
import _ from 'lodash'; // 70KB
import moment from 'moment'; // 230KB

// After - tree-shaking friendly imports
import debounce from 'lodash/debounce'; // 2KB
import { format } from 'date-fns'; // 13KB

// Code splitting - load on demand
const HeavyComponent = lazy(() => import('./HeavyComponent'));

// Dynamic imports
button.onclick = async () => {
  const module = await import('./analytics');
  module.trackEvent('button_click');
};

2. Optimize Images

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<!-- Before - unoptimized -->
<img src="photo.jpg" alt="Product" />

<!-- After - responsive & modern formats -->
<picture>
  <source srcset="photo.avif" type="image/avif">
  <source srcset="photo.webp" type="image/webp">
  <img 
    src="photo.jpg" 
    alt="Product"
    loading="lazy"
    width="800" 
    height="600"
    srcset="photo-400.jpg 400w, photo-800.jpg 800w, photo-1200.jpg 1200w"
    sizes="(max-width: 600px) 400px, (max-width: 1200px) 800px, 1200px"
  />
</picture>

<!-- Or use Next.js Image component -->
<Image
  src="/photo.jpg"
  alt="Product"
  width={800}
  height={600}
  placeholder="blur"
  quality={85}
/>

3. Lazy Load & Code Split

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// React - lazy load routes
const Dashboard = lazy(() => import('./Dashboard'));
const Settings = lazy(() => import('./Settings'));

function App() {
  return (
    <Suspense fallback={<Loading />}>
      <Routes>
        <Route path="/dashboard" element={<Dashboard />} />
        <Route path="/settings" element={<Settings />} />
      </Routes>
    </Suspense>
  );
}

// Next.js - automatic code splitting
// Just use dynamic imports
import dynamic from 'next/dynamic';

const DynamicChart = dynamic(() => import('./Chart'), {
  loading: () => <Spinner />,
  ssr: false // Don't render on server
});

4. Memoization & Caching

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// React - prevent unnecessary re-renders
const ExpensiveComponent = memo(({ data }) => {
  return <div>{/* Complex rendering */}</div>;
});

// Memoize expensive calculations
function ProductList({ products, filters }) {
  const filteredProducts = useMemo(() => {
    return products.filter(p => matchesFilters(p, filters));
  }, [products, filters]); // Only recalculate when dependencies change
  
  return <div>{filteredProducts.map(renderProduct)}</div>;
}

// Memoize callbacks to prevent child re-renders
function Parent() {
  const handleClick = useCallback(() => {
    console.log('clicked');
  }, []); // Stable function reference
  
  return <Child onClick={handleClick} />;
}

5. Virtualization for Long Lists

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// Before - renders 10,000 items (slow!)
function ProductList({ products }) {
  return (
    <div>
      {products.map(product => (
        <ProductCard key={product.id} product={product} />
      ))}
    </div>
  );
}

// After - only renders visible items
import { FixedSizeList } from 'react-window';

function ProductList({ products }) {
  return (
    <FixedSizeList
      height={600}
      itemCount={products.length}
      itemSize={100}
      width="100%"
    >
      {({ index, style }) => (
        <div style={style}>
          <ProductCard product={products[index]} />
        </div>
      )}
    </FixedSizeList>
  );
}

Backend Performance

1. Database Query Optimization

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-- Before - N+1 query problem
-- Fetches users, then makes separate query for each user's posts
SELECT * FROM users;
-- Then for each user:
SELECT * FROM posts WHERE user_id = ?;

-- After - join or eager loading
SELECT 
  users.*, 
  posts.id as post_id,
  posts.title as post_title
FROM users
LEFT JOIN posts ON posts.user_id = users.id;

-- Add indexes for frequently queried columns
CREATE INDEX idx_posts_user_id ON posts(user_id);
CREATE INDEX idx_posts_created_at ON posts(created_at);

-- Composite index for common query patterns
CREATE INDEX idx_posts_user_created 
ON posts(user_id, created_at DESC);

2. Caching Strategies

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// Memory cache for expensive computations
const cache = new Map();

async function getExpensiveData(key) {
  if (cache.has(key)) {
    return cache.get(key);
  }
  
  const data = await expensiveComputation(key);
  cache.set(key, data);
  
  // Expire after 5 minutes
  setTimeout(() => cache.delete(key), 5 * 60 * 1000);
  
  return data;
}

// Redis cache for distributed systems
import Redis from 'ioredis';
const redis = new Redis();

async function getCachedUserProfile(userId) {
  const cacheKey = `user:${userId}:profile`;
  
  // Try cache first
  const cached = await redis.get(cacheKey);
  if (cached) {
    return JSON.parse(cached);
  }
  
  // Cache miss - fetch from database
  const profile = await db.users.findById(userId);
  
  // Store in cache (expire after 1 hour)
  await redis.setex(cacheKey, 3600, JSON.stringify(profile));
  
  return profile;
}

// HTTP caching headers
app.get('/api/products', (req, res) => {
  res.set({
    'Cache-Control': 'public, max-age=300', // 5 minutes
    'ETag': generateETag(data)
  });
  res.json(products);
});

3. Database Connection Pooling

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// Before - new connection per query (slow!)
async function getUser(id) {
  const connection = await mysql.createConnection(config);
  const [rows] = await connection.execute('SELECT * FROM users WHERE id = ?', [id]);
  await connection.end();
  return rows[0];
}

// After - connection pool
import mysql from 'mysql2/promise';

const pool = mysql.createPool({
  host: 'localhost',
  user: 'root',
  database: 'mydb',
  waitForConnections: true,
  connectionLimit: 10,
  queueLimit: 0
});

async function getUser(id) {
  const [rows] = await pool.execute('SELECT * FROM users WHERE id = ?', [id]);
  return rows[0];
}

// NeonDB serverless - use @neondatabase/serverless
import { Pool } from '@neondatabase/serverless';

const pool = new Pool({ connectionString: process.env.DATABASE_URL });

4. Pagination & Limiting

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// Before - fetches all records (memory explosion!)
async function getProducts() {
  return await db.products.findAll(); // Could be millions of rows
}

// After - cursor-based pagination
async function getProducts({ cursor, limit = 20 }) {
  return await db.products.findMany({
    take: limit,
    skip: cursor ? 1 : 0,
    cursor: cursor ? { id: cursor } : undefined,
    orderBy: { createdAt: 'desc' }
  });
}

// Offset pagination (simpler but slower for deep pages)
async function getProducts({ page = 1, limit = 20 }) {
  const offset = (page - 1) * limit;
  return await db.products.findMany({
    take: limit,
    skip: offset,
    orderBy: { createdAt: 'desc' }
  });
}

5. Async Processing & Job Queues

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// Before - blocks request until email sent
app.post('/signup', async (req, res) => {
  const user = await createUser(req.body);
  await sendWelcomeEmail(user.email); // Blocks for 2-3 seconds!
  res.json({ success: true });
});

// After - queue job, respond immediately
import { Queue } from 'bullmq';

const emailQueue = new Queue('emails', {
  connection: { host: 'localhost', port: 6379 }
});

app.post('/signup', async (req, res) => {
  const user = await createUser(req.body);
  
  // Queue email to be sent asynchronously
  await emailQueue.add('welcome', {
    to: user.email,
    userId: user.id
  });
  
  res.json({ success: true }); // Fast response!
});

// Worker processes jobs in background
const worker = new Worker('emails', async (job) => {
  await sendEmail(job.data.to, 'welcome', { userId: job.data.userId });
});

Algorithm Optimization

Choose Right Data Structure

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// Before - O(n) lookup
const activeUsers = [];
function isActive(userId) {
  return activeUsers.includes(userId); // Linear search
}

// After - O(1) lookup
const activeUsers = new Set();
function isActive(userId) {
  return activeUsers.has(userId); // Constant time
}

// Before - O(n) for frequent insertions/deletions at start
const queue = [];
queue.unshift(item); // O(n) - shifts entire array

// After - O(1) with proper data structure
class Queue {
  constructor() {
    this.items = {};
    this.head = 0;
    this.tail = 0;
  }
  
  enqueue(item) {
    this.items[this.tail] = item;
    this.tail++;
  }
  
  dequeue() {
    const item = this.items[this.head];
    delete this.items[this.head];
    this.head++;
    return item;
  }
}

Reduce Computational Complexity

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// Before - O(n²) nested loops
function findDuplicates(arr) {
  const duplicates = [];
  for (let i = 0; i < arr.length; i++) {
    for (let j = i + 1; j < arr.length; j++) {
      if (arr[i] === arr[j]) {
        duplicates.push(arr[i]);
      }
    }
  }
  return duplicates;
}

// After - O(n) with Set
function findDuplicates(arr) {
  const seen = new Set();
  const duplicates = new Set();
  
  for (const item of arr) {
    if (seen.has(item)) {
      duplicates.add(item);
    }
    seen.add(item);
  }
  
  return Array.from(duplicates);
}

Monitoring & Alerting

Add Performance Metrics

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import { performance } from 'perf_hooks';

async function processOrder(order) {
  const startTime = performance.now();
  
  try {
    const result = await expensiveProcessing(order);
    
    const duration = performance.now() - startTime;
    
    // Log slow operations
    if (duration > 1000) {
      logger.warn('Slow order processing', { 
        orderId: order.id, 
        duration 
      });
    }
    
    // Send metrics to monitoring service
    metrics.histogram('order_processing_time', duration, {
      status: 'success'
    });
    
    return result;
  } catch (error) {
    const duration = performance.now() - startTime;
    metrics.histogram('order_processing_time', duration, {
      status: 'error'
    });
    throw error;
  }
}

Performance Checklist

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Frontend:
□ Bundle size < 200KB (gzipped)
□ Images optimized (WebP/AVIF)
□ Lazy loading for below-fold content
□ Code splitting for routes
□ Long lists virtualized
□ Expensive computations memoized
□ HTTP caching headers set
□ Critical CSS inlined

Backend:
□ Database queries indexed
□ N+1 queries eliminated
□ Connection pooling configured
□ Responses paginated
□ Heavy operations queued
□ Response caching implemented
□ Gzip compression enabled
□ CDN for static assets

General:
□ Performance budgets defined
□ Monitoring & alerting configured
□ Regular performance testing in CI
□ Profiling done on realistic data

Resources

• Web Vitals
• High Performance Browser Networking
• Database Indexing Explained
• React Performance Optimization

Remember: Fast software delights users. Measure, optimize bottlenecks, and monitor continuously.