Performance Optimizer
Make applications fast, scalable, and cost-efficient.
Core Principle
Measure first, optimize second. Don't guess at bottlenecks—profile, measure, then fix the slowest parts.
Performance Budget
Web Vitals (Target Metrics)
yaml
Core Web Vitals: Largest Contentful Paint (LCP): < 2.5s # Main content visible First Input Delay (FID): < 100ms # Interaction responsiveness Cumulative Layout Shift (CLS): < 0.1 # Visual stability Additional Metrics: First Contentful Paint (FCP): < 1.8s # First content rendered Time to Interactive (TTI): < 3.8s # Fully interactive Total Blocking Time (TBT): < 200ms # Main thread blocked Speed Index: < 3.4s # Visual progress Backend Metrics: API Response Time (P95): < 500ms Database Query Time (P95): < 100ms Server Response Time (TTFB): < 600ms
Phase 1: Profiling & Measurement
Goal: Identify actual bottlenecks, not perceived ones
Frontend Profiling
Chrome DevTools:
javascript
// 1. Performance tab → Record → Reload page // 2. Analyze: // - Main thread activity // - Network waterfall // - JavaScript execution time // - Rendering time // 3. Lighthouse audit // Run: chrome://lighthouse or `npm i -g lighthouse` lighthouse https://yoursite.com --view
React DevTools Profiler:
javascript
// Wrap component to profile
import { Profiler } from 'react'
function onRenderCallback(id, phase, actualDuration) {
console.log(`${id} (${phase}) took ${actualDuration}ms`)
}
;<Profiler id="ExpensiveComponent" onRender={onRenderCallback}>
<ExpensiveComponent />
</Profiler>
Backend Profiling
Node.js Profiling:
bash
# Generate CPU profile node --prof app.js # Process profile node --prof-process isolate-0x*.log > processed.txt # Flame graphs (better visualization) npm i -g 0x 0x app.js
Python Profiling:
python
import cProfile
import pstats
# Profile function
cProfile.run('slow_function()', 'output.prof')
# Analyze
p = pstats.Stats('output.prof')
p.sort_stats('cumulative').print_stats(20)
Database Profiling
PostgreSQL:
sql
-- Enable query logging ALTER DATABASE yourdb SET log_min_duration_statement = 100; -- Log queries >100ms -- Analyze query EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM users WHERE email = 'test@example.com'; -- Find slow queries SELECT query, mean_exec_time, calls FROM pg_stat_statements ORDER BY mean_exec_time DESC LIMIT 20;
MongoDB:
javascript
// Enable profiling
db.setProfilingLevel(1, { slowms: 100 })
// View slow queries
db.system.profile.find({ millis: { $gt: 100 } }).sort({ ts: -1 })
// Explain query
db.collection.find({ email: 'test@example.com' }).explain('executionStats')
Phase 2: Database Optimization
Add Strategic Indexes
sql
-- Before: Table scan (slow) SELECT * FROM users WHERE email = 'user@example.com'; -- Execution time: 2000ms on 1M rows -- After: Index scan (fast) CREATE INDEX idx_users_email ON users(email); SELECT * FROM users WHERE email = 'user@example.com'; -- Execution time: 5ms -- Composite index for multi-column queries CREATE INDEX idx_posts_user_date ON posts(user_id, created_at DESC); SELECT * FROM posts WHERE user_id = 123 ORDER BY created_at DESC; -- Partial index for filtered queries CREATE INDEX idx_active_users ON users(created_at) WHERE is_active = true;
Eliminate N+1 Queries
typescript
// ❌ Bad: N+1 query problem (101 database queries)
const users = await User.findAll() // 1 query
for (const user of users) {
user.posts = await Post.findAll({ where: { userId: user.id } }) // N queries
}
// ✅ Good: Eager loading (2 queries)
const users = await User.findAll({
include: [{ model: Post }]
})
// ✅ Better: DataLoader (batching + caching)
const userLoader = new DataLoader(async userIds => {
const users = await User.findAll({ where: { id: userIds } })
return userIds.map(id => users.find(u => u.id === id))
})
Query Optimization
sql
-- Avoid SELECT * -- ❌ Bad SELECT * FROM users WHERE id = 1; -- ✅ Good SELECT id, name, email FROM users WHERE id = 1; -- Use LIMIT -- ❌ Bad SELECT * FROM posts ORDER BY created_at DESC; -- ✅ Good SELECT * FROM posts ORDER BY created_at DESC LIMIT 20; -- Avoid functions in WHERE clause -- ❌ Bad (can't use index) SELECT * FROM users WHERE LOWER(email) = 'user@example.com'; -- ✅ Good (can use index) SELECT * FROM users WHERE email = 'user@example.com'; -- Store email as lowercase, or use generated column + index
Connection Pooling
typescript
// PostgreSQL connection pool
import { Pool } from 'pg'
const pool = new Pool({
max: 20, // Maximum connections
min: 5, // Minimum connections
idleTimeoutMillis: 30000, // Close idle connections after 30s
connectionTimeoutMillis: 2000 // Error if can't connect in 2s
})
// Always release connections
const client = await pool.connect()
try {
const result = await client.query('SELECT * FROM users')
return result.rows
} finally {
client.release()
}
Phase 3: Caching Strategy
Multi-Layer Caching
code
Browser Cache (HTTP headers) ↓ CDN Cache (Cloudflare, CloudFront) ↓ Application Cache (Redis, Memcached) ↓ Database Query Cache ↓ Database
Redis Caching
typescript
import Redis from 'ioredis'
const redis = new Redis({
maxRetriesPerRequest: 3,
enableReadyCheck: true
})
async function getUser(id: string): Promise<User> {
const cacheKey = `user:${id}`
// 1. Check cache
const cached = await redis.get(cacheKey)
if (cached) {
return JSON.parse(cached)
}
// 2. Cache miss - fetch from database
const user = await db.users.findById(id)
// 3. Store in cache (expire in 1 hour)
await redis.setex(cacheKey, 3600, JSON.stringify(user))
return user
}
// Cache invalidation
async function updateUser(id: string, data: Partial<User>) {
await db.users.update(id, data)
await redis.del(`user:${id}`) // Invalidate cache
}
HTTP Caching Headers
typescript
// Express middleware
app.use((req, res, next) => {
// Static assets: cache for 1 year
if (req.url.match(/\.(js|css|png|jpg|jpeg|gif|svg|woff|woff2)$/)) {
res.setHeader('Cache-Control', 'public, max-age=31536000, immutable')
}
// HTML: no cache (always revalidate)
if (req.url.endsWith('.html') || req.url === '/') {
res.setHeader('Cache-Control', 'no-cache, must-revalidate')
}
// API responses: cache for 5 minutes
if (req.url.startsWith('/api/')) {
res.setHeader('Cache-Control', 'public, max-age=300')
res.setHeader('ETag', generateETag(req.url))
}
next()
})
CDN Configuration
yaml
Static Assets to CDN: - Images: /images/** - JavaScript: /js/** - CSS: /css/** - Fonts: /fonts/** CDN Settings: - Cache duration: 1 year (with versioned URLs) - Gzip/Brotli compression: enabled - Image optimization: WebP conversion - Purge on deploy: yes (via API) Recommended CDNs: - Cloudflare (free tier excellent) - CloudFront (AWS integration) - Fastly (enterprise, very fast)
Phase 4: Frontend Optimization
Code Splitting & Lazy Loading
typescript
// React lazy loading
import { lazy, Suspense } from 'react'
// ❌ Bad: Load everything upfront
import Dashboard from './Dashboard'
import AdminPanel from './AdminPanel'
// ✅ Good: Lazy load routes
const Dashboard = lazy(() => import('./Dashboard'))
const AdminPanel = lazy(() => import('./AdminPanel'))
function App() {
return (
<Suspense fallback={<LoadingSpinner />}>
<Routes>
<Route path="/dashboard" element={<Dashboard />} />
<Route path="/admin" element={<AdminPanel />} />
</Routes>
</Suspense>
)
}
// Next.js dynamic imports
import dynamic from 'next/dynamic'
const HeavyComponent = dynamic(() => import('./HeavyComponent'), {
loading: () => <LoadingSpinner />,
ssr: false // Skip SSR for this component
})
Image Optimization
jsx
// Next.js Image component (automatic optimization)
import Image from 'next/image'
<Image
src="/photo.jpg"
width={800}
height={600}
alt="Description"
loading="lazy" // Lazy load off-screen images
placeholder="blur" // Blur placeholder while loading
quality={75} // 75% quality (good balance)
/>
// WebP format with fallback
<picture>
<source srcset="image.webp" type="image/webp" />
<source srcset="image.jpg" type="image/jpeg" />
<img src="image.jpg" alt="Description" loading="lazy" />
</picture>
// Responsive images
<img
srcset="
small.jpg 480w,
medium.jpg 768w,
large.jpg 1200w
"
sizes="(max-width: 480px) 480px, (max-width: 768px) 768px, 1200px"
src="medium.jpg"
alt="Description"
/>
Bundle Size Optimization
bash
# Analyze bundle
npm run build -- --analyze
# Reduce bundle size:
# 1. Remove unused dependencies
npm uninstall unused-package
# 2. Use tree-shaking compatible imports
# ❌ Bad
import _ from 'lodash'
# ✅ Good
import debounce from 'lodash/debounce'
# 3. Dynamic imports for large libraries
const moment = await import('moment')
# 4. Minification (automatic in production builds)
# Vite/Next.js handle this automatically
React Performance
typescript
// 1. Memoize expensive calculations
import { useMemo } from 'react'
function DataTable({ data }) {
const sortedData = useMemo(
() => data.sort((a, b) => a.name.localeCompare(b.name)),
[data]
)
return <Table data={sortedData} />
}
// 2. Memoize components
import { memo } from 'react'
const ExpensiveComponent = memo(function ExpensiveComponent({ data }) {
// Only re-renders if data changes
return <div>{/* expensive rendering */}</div>
})
// 3. useCallback for stable function references
import { useCallback } from 'react'
function Parent() {
const handleClick = useCallback(() => {
console.log('Clicked')
}, [])
return <ExpensiveChild onClick={handleClick} />
}
// 4. Virtualize long lists
import { FixedSizeList } from 'react-window'
<FixedSizeList
height={600}
itemCount={10000}
itemSize={50}
>
{({ index, style }) => (
<div style={style}>Row {index}</div>
)}
</FixedSizeList>
Phase 5: Backend Optimization
Async Background Processing
typescript
// ❌ Bad: Synchronous (slow response)
app.post('/send-email', async (req, res) => {
await sendEmail(req.body) // 3 seconds
res.json({ success: true })
})
// ✅ Good: Queue job (fast response)
import Bull from 'bull'
const emailQueue = new Bull('emails', 'redis://localhost:6379')
app.post('/send-email', async (req, res) => {
await emailQueue.add('send', req.body)
res.json({ success: true, message: 'Email queued' })
})
// Process jobs in background worker
emailQueue.process('send', async job => {
await sendEmail(job.data)
})
API Response Optimization
typescript
// 1. Compression
import compression from 'compression'
app.use(compression()) // Gzip responses
// 2. Pagination
app.get('/api/posts', async (req, res) => {
const page = parseInt(req.query.page) || 1
const limit = parseInt(req.query.limit) || 20
const posts = await db.posts.findAll({
offset: (page - 1) * limit,
limit: limit
})
res.json({
data: posts,
pagination: {
page,
limit,
total: await db.posts.count()
}
})
})
// 3. Field filtering (GraphQL-style)
app.get('/api/users/:id', async (req, res) => {
const fields = req.query.fields?.split(',') || ['id', 'name', 'email']
const user = await db.users.findById(req.params.id, {
attributes: fields
})
res.json(user)
})
Rate Limiting
typescript
import rateLimit from 'express-rate-limit'
// General API rate limit
const apiLimiter = rateLimit({
windowMs: 15 * 60 * 1000, // 15 minutes
max: 100, // 100 requests per window
message: 'Too many requests, please try again later'
})
app.use('/api/', apiLimiter)
// Stricter limit for expensive endpoints
const authLimiter = rateLimit({
windowMs: 60 * 60 * 1000, // 1 hour
max: 5, // 5 requests per hour
skipSuccessfulRequests: true
})
app.post('/api/auth/login', authLimiter, loginHandler)
Phase 6: Monitoring & Alerting
Application Performance Monitoring (APM)
Tools:
- •Sentry: Error tracking + performance
- •New Relic: Full-stack APM
- •Datadog: Infrastructure + APM
- •Vercel Analytics: Next.js optimized
Custom Monitoring:
typescript
// Track response times
app.use((req, res, next) => {
const start = Date.now()
res.on('finish', () => {
const duration = Date.now() - start
// Log to monitoring service
metrics.recordResponseTime(req.path, duration)
// Alert on slow requests
if (duration > 1000) {
logger.warn(`Slow request: ${req.path} took ${duration}ms`)
}
})
next()
})
// Track database query times
db.on('query', (query, duration) => {
if (duration > 100) {
logger.warn(`Slow query: ${query} took ${duration}ms`)
}
})
Performance Dashboards
yaml
Key Metrics to Track: - Response time (P50, P95, P99) - Throughput (requests/second) - Error rate (%) - Database query times - Cache hit ratio - Memory usage - CPU usage Alerting Thresholds: - P95 response time > 1s - Error rate > 1% - Cache hit ratio < 80% - Memory usage > 80%
Optimization Checklist
Frontend ✅
- • Lighthouse score > 90
- • LCP < 2.5s
- • FID < 100ms
- • CLS < 0.1
- • Bundle size < 200KB (initial)
- • Images optimized (WebP, lazy loading)
- • Code splitting implemented
- • Critical CSS inlined
Backend ✅
- • P95 response time < 500ms
- • Database queries indexed
- • N+1 queries eliminated
- • Connection pooling enabled
- • Background jobs async
- • Rate limiting configured
- • API responses compressed
Database ✅
- • Slow query log enabled
- • All queries < 100ms (P95)
- • Indexes on foreign keys
- • Indexes on WHERE/ORDER BY columns
- • Query explain plans reviewed
- • Connection pool sized correctly
Caching ✅
- • Redis/Memcached configured
- • CDN for static assets
- • HTTP cache headers set
- • Cache hit ratio > 80%
- • Cache invalidation strategy
Infrastructure ✅
- • Auto-scaling configured
- • Load balancer healthy
- • Monitoring/alerting active
- • Logs centralized
- • Backups automated
Related Resources
Related Skills:
- •
deployment-advisor- For infrastructure optimization - •
frontend-builder- For React performance patterns - •
api-designer- For API optimization
Related Patterns:
- •
META/DECISION-FRAMEWORK.md- Scaling decisions - •
STANDARDS/architecture-patterns/caching-patterns.md- Caching strategies (when created)
Related Playbooks:
- •
PLAYBOOKS/optimize-database-performance.md- DB optimization steps (when created) - •
PLAYBOOKS/frontend-performance-audit.md- Frontend audit procedure (when created)