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