/api-rate-limiting

name: api-rate-limiting
description: >
  Implement API rate limiting strategies using token bucket, sliding window, and
  fixed window algorithms. Use when protecting APIs from abuse, managing
  traffic, or implementing tiered rate limits.

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