Performance Engineering

Profile, analyze, and optimize application performance.

When to Use

• Application is slow or unresponsive
• User reports performance issues
• Before scaling infrastructure
• Optimizing critical paths
• Setting up monitoring

Optimization Process

1. Measure - Profile before optimizing
2. Identify - Find the biggest bottlenecks
3. Optimize - Fix highest-impact issues first
4. Verify - Confirm improvements with metrics

Profiling Commands

# Node.js profiling
node --prof app.js
node --prof-process isolate-*.log > profile.txt

# Python profiling
python -m cProfile -o output.prof script.py
python -m pstats output.prof

# Go profiling
go tool pprof http://localhost:6060/debug/pprof/profile

Common Bottlenecks

Database

• Missing indexes (add indexes on WHERE/JOIN columns)
• N+1 queries (use eager loading)
• Large result sets (add pagination)

Memory

• Memory leaks (check event listeners, closures)
• Large objects (stream instead of buffer)
• Cache without TTL (add expiration)

CPU

• Synchronous operations (make async)
• Complex algorithms (optimize or cache)
• Unnecessary computation (memoize)

Network

• Too many requests (batch/combine)
• Large payloads (compress, paginate)
• No caching (add CDN, browser cache)

Performance Budgets

Metric	Target
Load Time (3G)	<3s
Load Time (4G)	<1s
API Response	<200ms
Bundle Size	<500KB
LCP	<2.5s
FID	<100ms
CLS	<0.1

Output Format

## Performance Report

**Before:** [baseline metrics]
**After:** [improved metrics]
**Improvement:** [percentage]

### Bottlenecks Identified

1. [Issue] - Impact: High/Medium/Low

### Optimizations Applied

1. [Change] → [Result]

Examples

Input: "The API is slow" Action: Profile endpoints, identify slow queries, optimize, verify improvement

Input: "Page load is taking too long" Action: Analyze bundle, check network, optimize critical path, add caching