phoroth Performance Optimizer
Find and fix performance bottlenecks. Measure, optimize, verify. Make it fast.
When to Use This Skill
- App is slow or laggy
- User complains about performance
- Page load times are high
- API responses are slow
- Database queries take too long
- User mentions "slow", "lag", "performance", or "optimize"
The Optimization Process
1. Measure First
Never optimize without measuring:
// Measure execution time
console.time('operation');
await slowOperation();
console.timeEnd('operation'); // operation: 2341ms
What to measure:
- Page load time
- API response time
- Database query time
- Function execution time
- Memory usage
- Network requests
2. Find the Bottleneck
Use profiling tools to find the slow parts:
Browser:
DevTools → Performance tab → Record → Stop
Look for long tasks (red bars)
Node.js:
node --prof app.js
node --prof-process isolate-*.log > profile.txt
Database:
EXPLAIN ANALYZE SELECT * FROM users WHERE email = 'test@example.com';
3. Optimize
Fix the slowest thing first (biggest impact).
Common Optimizations
Database Queries
Problem: N+1 Queries
// Bad: N+1 queries
const users = await db.users.find();
for (const user of users) {
user.posts = await db.posts.find({ userId: user.id }); // N queries
}
// Good: Single query with JOIN
const users = await db.users.find()
.populate('posts'); // 1 query
Problem: Missing Index
-- Check slow query
EXPLAIN SELECT * FROM users WHERE email = 'test@example.com';
-- Shows: Seq Scan (bad)
-- Add index
CREATE INDEX idx_users_email ON users(email);
-- Check again
EXPLAIN SELECT * FROM users WHERE email = 'test@example.com';
-- Shows: Index Scan (good)
**Problem: SELECT ***
// Bad: Fetches all columns
const users = await db.query('SELECT * FROM users');
// Good: Only needed columns
const users = await db.query('SELECT id, name, email FROM users');
Problem: No Pagination
// Bad: Returns all records
const users = await db.users.find();
// Good: Paginated
const users = await db.users.find()
.limit(20)
.skip((page - 1) * 20);
API Performance
Problem: No Caching
// Bad: Hits database every time
app.get('/api/stats', async (req, res) => {
const stats = await db.stats.calculate(); // Slow
res.json(stats);
});
// Good: Cache for 5 minutes
const cache = new Map();
app.get('/api/stats', async (req, res) => {
const cached = cache.get('stats');
if (cached && Date.now() - cached.time < 300000) {
return res.json(cached.data);
}
const stats = await db.stats.calculate();
cache.set('stats', { data: stats, time: Date.now() });
res.json(stats);
});
Problem: Sequential Operations
// Bad: Sequential (slow)
const user = await getUser(id);
const posts = await getPosts(id);
const comments = await getComments(id);
// Total: 300ms + 200ms + 150ms = 650ms
// Good: Parallel (fast)
const [user, posts, comments] = await Promise.all([
getUser(id),
getPosts(id),
getComments(id)
]);
// Total: max(300ms, 200ms, 150ms) = 300ms
Problem: Large Payloads
// Bad: Returns everything
res.json(users); // 5MB response
// Good: Only needed fields
res.json(users.map(u => ({
id: u.id,
name: u.name,
email: u.email
}))); // 500KB response
Frontend Performance
Problem: Unnecessary Re-renders
// Bad: Re-renders on every parent update
function UserList({ users }) {
return users.map(user => <UserCard user={user} />);
}
// Good: Memoized
const UserCard = React.memo(({ user }) => {
return <div>{user.name}</div>;
});
Problem: Large Bundle
// Bad: Imports entire library
import _ from 'lodash'; // 70KB
// Good: Import only what you need
import debounce from 'lodash/debounce'; // 2KB
Problem: No Code Splitting
// Bad: Everything in one bundle
import HeavyComponent from './HeavyComponent';
// Good: Lazy load
const HeavyComponent = React.lazy(() => import('./HeavyComponent'));
Problem: Unoptimized Images
<!-- Bad: Large image -->
<img src="photo.jpg" /> <!-- 5MB -->
<!-- Good: Optimized and responsive -->
<img
src="photo-small.webp"
srcset="photo-small.webp 400w, photo-large.webp 800w"
loading="lazy"
width="400"
height="300"
/> <!-- 50KB -->
Algorithm Optimization
Problem: Inefficient Algorithm
// Bad: 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;
}
// Good: O(n) - single pass 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);
}
Problem: Repeated Calculations
// Bad: Calculates every time
function getTotal(items) {
return items.reduce((sum, item) => sum + item.price * item.quantity, 0);
}
// Called 100 times in render
// Good: Memoized
const getTotal = useMemo(() => {
return items.reduce((sum, item) => sum + item.price * item.quantity, 0);
}, [items]);
Memory Optimization
Problem: Memory Leak
// Bad: Event listener not cleaned up
useEffect(() => {
window.addEventListener('scroll', handleScroll);
// Memory leak!
}, []);
// Good: Cleanup
useEffect(() => {
window.addEventListener('scroll', handleScroll);
return () => window.removeEventListener('scroll', handleScroll);
}, []);
Problem: Large Data in Memory
// Bad: Loads entire file into memory
const data = fs.readFileSync('huge-file.txt'); // 1GB
// Good: Stream it
const stream = fs.createReadStream('huge-file.txt');
stream.on('data', chunk => process(chunk));
Measuring Impact
Always measure before and after:
// Before optimization
console.time('query');
const users = await db.users.find();
console.timeEnd('query');
// query: 2341ms
// After optimization (added index)
console.time('query');
const users = await db.users.find();
console.timeEnd('query');
// query: 23ms
// Improvement: 100x faster!
Performance Budgets
Set targets:
Page Load: < 2 seconds
API Response: < 200ms
Database Query: < 50ms
Bundle Size: < 200KB
Time to Interactive: < 3 seconds
Tools
Browser:
- Chrome DevTools Performance tab
- Lighthouse (audit)
- Network tab (waterfall)
Node.js:
node --prof(profiling)clinic(diagnostics)autocannon(load testing)
Database:
EXPLAIN ANALYZE(query plans)- Slow query log
- Database profiler
Monitoring:
- New Relic
- Datadog
- Sentry Performance
Quick Wins
Easy optimizations with big impact:
- Add database indexes on frequently queried columns
- Enable gzip compression on server
- Add caching for expensive operations
- Lazy load images and heavy components
- Use CDN for static assets
- Minify and compress JavaScript/CSS
- Remove unused dependencies
- Use pagination instead of loading all data
- Optimize images (WebP, proper sizing)
- Enable HTTP/2 on server
Optimization Checklist
- Measured current performance
- Identified bottleneck
- Applied optimization
- Measured improvement
- Verified functionality still works
- No new bugs introduced
- Documented the change
When NOT to Optimize
- Premature optimization (optimize when it's actually slow)
- Micro-optimizations (save 1ms when page takes 5 seconds)
- Readable code is more important than tiny speed gains
- If it's already fast enough
Key Principles
- Measure before optimizing
- Fix the biggest bottleneck first
- Measure after to prove improvement
- Don't sacrifice readability for tiny gains
- Profile in production-like enprojectnment
- Consider the 80/20 rule (20% of code causes 80% of slowness)
Related Skills
@database-design- Query optimization@codebase-audit-pre-push- Code review@bug-hunter- Debugging
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for enprojectnment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.