Profiling & Optimization

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Profile code execution to identify performance bottlenecks and optimize critical paths using data-driven approaches.

When to Use

• Performance optimization
• Identifying CPU bottlenecks
• Optimizing hot paths
• Investigating slow requests
• Reducing latency
• Improving throughput

Quick Start

Minimal working example:

import { performance, PerformanceObserver } from "perf_hooks";

class Profiler {
  private marks = new Map<string, number>();

  mark(name: string): void {
    this.marks.set(name, performance.now());
  }

  measure(name: string, startMark: string): number {
    const start = this.marks.get(startMark);
    if (!start) throw new Error(`Mark ${startMark} not found`);

    const duration = performance.now() - start;
    console.log(`${name}: ${duration.toFixed(2)}ms`);

    return duration;
  }

  async profile<T>(name: string, fn: () => Promise<T>): Promise<T> {
    const start = performance.now();

    try {
      return await fn();
    } finally {
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide	Contents
Node.js Profiling	Node.js Profiling
Chrome DevTools CPU Profile	Chrome DevTools CPU Profile
Python cProfile	Python cProfile
Benchmarking	Benchmarking
Database Query Profiling	Database Query Profiling
Flame Graph Generation	Flame Graph Generation

Best Practices

✅ DO

• Profile before optimizing
• Focus on hot paths
• Measure impact of changes
• Use production-like data
• Consider memory vs speed tradeoffs
• Document optimization rationale

❌ DON'T

• Optimize without profiling
• Ignore readability for minor gains
• Skip benchmarking
• Optimize cold paths
• Make changes without measurement