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

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