Performance Engineer

Performance Engineer

Specialist in analyzing and optimizing application performance, identifying bottlenecks, and implementing efficiency improvements.

When This Skill Activates

Activates when you:

• Report performance issues
• Need performance optimization
• Mention "slow" or "latency"
• Want to improve efficiency

Performance Analysis Process

Phase 1: Identify the Problem

1. Define metrics

• What's the baseline?
• What's the target?
• What's acceptable?

1. Measure current performance ```bash # Response time curl -w "@curl-format.txt" -o /dev/null -s https://example.com/users

# Database query time

# Add timing logs to queries

# Memory usage

# Use profiler

3. **Profile the application**

# Node.js node --prof app.js

# Python python -m cProfile app.py

# Go go test -cpuprofile=cpu.prof

### Phase 2: Find the Bottleneck

Common bottleneck locations:

| Layer | Common Issues |
|-------|---------------|
| **Database** | N+1 queries, missing indexes, large result sets |
| **API** | Over-fetching, no caching, serial requests |
| **Application** | Inefficient algorithms, excessive logging |
| **Frontend** | Large bundles, re-renders, no lazy loading |
| **Network** | Too many requests, large payloads, no compression |

### Phase 3: Optimize

#### Database Optimization

**N+1 Queries:**

// Bad: N+1 queries const users = await User.findAll(); for (const user of users) { user.posts = await Post.findAll({ where: { userId: user.id } }); }

// Good: Eager loading const users = await User.findAll({ include: [{ model: Post, as: 'posts' }] });

**Missing Indexes:**

-- Add index on frequently queried columns CREATE INDEX idx_user_email ON users(email); CREATE INDEX idx_post_user_id ON posts(user_id);

#### API Optimization

**Pagination:**

// Always paginate large result sets const users = await User.findAll({ limit: 100, offset: page * 100 });

**Field Selection:**

// Select only needed fields const users = await User.findAll({ attributes: ['id', 'name', 'email'] });

**Compression:**

// Enable gzip compression app.use(compression());

#### Frontend Optimization

**Code Splitting:**

// Lazy load routes const Dashboard = lazy(() => import('./Dashboard'));

**Memoization:**

// Use useMemo for expensive calculations const filtered = useMemo(() => items.filter(item => item.active), [items] );

**Image Optimization:**
- Use WebP format
- Lazy load images
- Use responsive images
- Compress images

### Phase 4: Verify

1. **Measure again**
2. **Compare to baseline**
3. **Ensure no regressions**
4. **Document the improvement**

## Performance Targets

Derive targets from the service SLO, current baseline, workload shape, cost
budget, and critical user journey. The table below is an example starting point
only; never present it as the system's acceptance criteria without evidence or
owner agreement.

| Metric | Target | Critical Threshold |
|--------|--------|-------------------|
| API Response (p50) | < 100ms | < 500ms |
| API Response (p95) | < 500ms | < 1s |
| API Response (p99) | < 1s | < 2s |
| Database Query | < 50ms | < 200ms |
| Page Load (FMP) | < 2s | < 3s |
| Time to Interactive | < 3s | < 5s |
| Memory Usage | < 512MB | < 1GB |

## Common Optimizations

### Caching Strategy

// Cache expensive computations const cache = new Map();

async function getUserStats(userId: string) { if (cache.has(userId)) { return cache.get(userId); }

const stats = await calculateUserStats(userId); cache.set(userId, stats);

// Invalidate after 5 minutes setTimeout(() => cache.delete(userId), 5 60 1000);

return stats; }

### Batch Processing

// Bad: Individual requests for (const id of userIds) { await fetchUser(id); }

// Good: Batch request await fetchUsers(userIds);

### Debouncing/Throttling

// Debounce search input const debouncedSearch = debounce(search, 300);

// Throttle scroll events const throttledScroll = throttle(handleScroll, 100);

## Performance Monitoring

### Key Metrics

- **Response Time**: Time to process request
- **Throughput**: Requests per second
- **Error Rate**: Failed requests percentage
- **Memory Usage**: Heap/RAM used
- **CPU Usage**: Processor utilization

### Monitoring Tools

| Tool | Purpose |
|------|---------|
| Lighthouse | Frontend performance |
| New Relic | APM monitoring |
| Datadog | Infrastructure monitoring |
| Prometheus | Metrics collection |

## Scripts

Profile application:

python3 scripts/profile.py --name <service-name> --output perf-profile.txt

Generate performance report:

python3 scripts/perf_report.py --name <service-name> --output perf-report.md

## References

- `references/optimization.md` - Optimization techniques
- `references/monitoring.md` - Monitoring setup
- `references/checklist.md` - Performance checklist