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performance-optimization
by meeezus
⭐ 0🍴 0📅 Jan 10, 2026
SKILL.md
name: performance-optimization description: Guides performance analysis and optimization for any application. Use when diagnosing slowness, optimizing code, improving load times, or when asked about performance.
Performance Optimization Skill
Performance Analysis Process
1. Measure First
Never optimize without data. Always profile before changing code.
# Node.js profiling
node --prof app.js
node --prof-process isolate*.log > profile.txt
# Python profiling
python -m cProfile -o profile.stats app.py
python -m pstats profile.stats
# Web performance
lighthouse https://example.com --output=json
2. Identify Bottlenecks
Common Bottleneck Categories
| Category | Symptoms | Tools |
|---|---|---|
| CPU | High CPU usage, slow computation | Profiler, flame graphs |
| Memory | High RAM, GC pauses, OOM | Heap snapshots, memory profiler |
| I/O | Slow disk/network, waiting | strace, network inspector |
| Database | Slow queries, lock contention | Query analyzer, EXPLAIN |
3. Apply Optimizations
Frontend Optimizations
Bundle Size
// ❌ Import entire library
import _ from 'lodash';
// ✅ Import only needed functions
import debounce from 'lodash/debounce';
// ✅ Use dynamic imports for code splitting
const HeavyComponent = lazy(() => import('./HeavyComponent'));
Rendering
// ❌ Render on every parent update
function Child({ data }) {
return <ExpensiveComponent data={data} />;
}
// ✅ Memoize when props don't change
const Child = memo(function Child({ data }) {
return <ExpensiveComponent data={data} />;
});
// ✅ Use useMemo for expensive computations
const processed = useMemo(() => expensiveCalc(data), [data]);
Images
<!-- ❌ Unoptimized -->
<img src="large-image.jpg" />
<!-- ✅ Optimized -->
<img
src="image.webp"
srcset="image-300.webp 300w, image-600.webp 600w"
sizes="(max-width: 600px) 300px, 600px"
loading="lazy"
decoding="async"
/>
Backend Optimizations
Database Queries
-- ❌ N+1 Query Problem
SELECT * FROM users;
-- Then for each user:
SELECT * FROM orders WHERE user_id = ?;
-- ✅ Single query with JOIN
SELECT u.*, o.*
FROM users u
LEFT JOIN orders o ON u.id = o.user_id;
-- ✅ Or use pagination
SELECT * FROM users LIMIT 100 OFFSET 0;
Caching Strategy
// Multi-layer caching
const getUser = async (id) => {
// L1: In-memory cache (fastest)
let user = memoryCache.get(`user:${id}`);
if (user) return user;
// L2: Redis cache (fast)
user = await redis.get(`user:${id}`);
if (user) {
memoryCache.set(`user:${id}`, user, 60);
return JSON.parse(user);
}
// L3: Database (slow)
user = await db.users.findById(id);
await redis.setex(`user:${id}`, 3600, JSON.stringify(user));
memoryCache.set(`user:${id}`, user, 60);
return user;
};
Async Processing
// ❌ Blocking operation
app.post('/upload', async (req, res) => {
await processVideo(req.file); // Takes 5 minutes
res.send('Done');
});
// ✅ Queue for background processing
app.post('/upload', async (req, res) => {
const jobId = await queue.add('processVideo', { file: req.file });
res.send({ jobId, status: 'processing' });
});
Algorithm Optimizations
Time Complexity Improvements
// ❌ 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;
}
// ✅ O(n) - hash map
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 [...duplicates];
}
Performance Metrics
Web Vitals (Target Values)
| Metric | Good | Needs Work | Poor |
|---|---|---|---|
| LCP | < 2.5s | 2.5-4s | > 4s |
| FID | < 100ms | 100-300ms | > 300ms |
| CLS | < 0.1 | 0.1-0.25 | > 0.25 |
| TTFB | < 800ms | 800ms-1.8s | > 1.8s |
API Performance (Target Values)
| Metric | Target |
|---|---|
| P50 Latency | < 100ms |
| P95 Latency | < 500ms |
| P99 Latency | < 1s |
| Error Rate | < 0.1% |
Score
Total Score
50/100
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