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

• Measure - Profile before optimizing

• Identify - Find the biggest bottlenecks

• Optimize - Fix highest-impact issues first

• 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