api-rate-limiting

API Rate Limiting

Table of Contents

• [Overview](#overview)
• [When to Use](#when-to-use)
• [Quick Start](#quick-start)
• [Reference Guides](#reference-guides)
• [Best Practices](#best-practices)

Overview

Protect APIs from abuse and manage traffic using various rate limiting algorithms with per-user, per-IP, and per-endpoint strategies.

When to Use

• Protecting APIs from brute force attacks
• Managing traffic spikes
• Implementing tiered service plans
• Preventing DoS attacks
• Fairness in resource allocation
• Enforcing quotas and usage limits

Quick Start

Minimal working example:

// Token Bucket Rate Limiter
class TokenBucket {
  constructor(capacity, refillRate) {
    this.capacity = capacity;
    this.tokens = capacity;
    this.refillRate = refillRate; // tokens per second
    this.lastRefillTime = Date.now();
  }

  refill() {
    const now = Date.now();
    const timePassed = (now - this.lastRefillTime) / 1000;
    const tokensToAdd = timePassed * this.refillRate;

    this.tokens = Math.min(this.capacity, this.tokens + tokensToAdd);
    this.lastRefillTime = now;
  }

  consume(tokens = 1) {
    this.refill();

    if (this.tokens >= tokens) {
      this.tokens -= tokens;
      return true;
    }
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

| Guide | Contents | |---|---| | [Token Bucket Algorithm](references/token-bucket-algorithm.md) | Token Bucket Algorithm | | [Sliding Window Algorithm](references/sliding-window-algorithm.md) | Sliding Window Algorithm | | [Redis-Based Rate Limiting](references/redis-based-rate-limiting.md) | Redis-Based Rate Limiting | | [Tiered Rate Limiting](references/tiered-rate-limiting.md) | Tiered Rate Limiting | | [Python Rate Limiting (Flask)](references/python-rate-limiting-flask.md) | Python Rate Limiting (Flask) | | [Response Headers](references/response-headers.md) | Response Headers |

Best Practices

✅ DO

• Include rate limit headers in responses
• Use Redis for distributed rate limiting
• Implement tiered limits for different user plans
• Set appropriate window sizes and limits
• Monitor rate limit metrics
• Provide clear retry guidance
• Document rate limits in API docs
• Test under high load

❌ DON'T

• Use in-memory storage in production
• Set limits too restrictively
• Forget to include Retry-After header
• Ignore distributed scenarios
• Make rate limits public (security)
• Use simple counters for distributed systems
• Forget cleanup of old data