Define metrics
- What's the baseline?
- What's the target?
- What's acceptable?

Measure current performance

# 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

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

Measure again
Compare to baseline
Ensure no regressions
Document the improvement

Performance Targets

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:

python scripts/profile.py

Generate performance report:

python scripts/perf_report.py

References

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

performance-engineer

Performance Engineer

When This Skill Activates

Performance Analysis Process

Phase 1: Identify the Problem