Rate Limiting Implementation

Rate Limiting Implementation

Table of Contents

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

Overview

Implement rate limiting and throttling mechanisms to protect your services from abuse, ensure fair resource allocation, and maintain system stability under load.

When to Use

• Protecting public APIs from abuse
• Preventing DOS/DDOS attacks
• Ensuring fair resource usage across users
• Implementing API quotas and billing tiers
• Managing system load and backpressure
• Enforcing SLA limits
• Controlling third-party API usage
• Database connection management

Quick Start

Minimal working example:

interface TokenBucketConfig {
  capacity: number;
  refillRate: number; // tokens per second
  refillInterval: number; // milliseconds
}

class TokenBucket {
  private tokens: number;
  private lastRefill: number;
  private readonly capacity: number;
  private readonly refillRate: number;
  private readonly refillInterval: number;
  private refillTimer?: NodeJS.Timeout;

  constructor(config: TokenBucketConfig) {
    this.capacity = config.capacity;
    this.tokens = config.capacity;
    this.refillRate = config.refillRate;
    this.refillInterval = config.refillInterval;
    this.lastRefill = Date.now();

    this.startRefill();
  }

  private startRefill(): void {
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Best Practices

✅ DO

• Use distributed rate limiting for multi-server deployments
• Implement multiple rate limit tiers (per second, minute, hour, day)
• Return proper HTTP status codes (429 Too Many Requests)
• Include Retry-After header in responses
• Log rate limit violations for monitoring
• Implement graceful degradation
• Use Redis or similar for persistence
• Consider cost-based rate limiting (expensive operations cost more)
• Implement burst allowances for legitimate traffic spikes
• Provide clear API documentation about limits

❌ DON'T

• Store rate limit data in application memory for distributed systems
• Use fixed window counters without considering edge cases
• Forget to clean up expired data
• Block all requests from an IP due to one bad actor
• Set limits too restrictive for legitimate use
• Ignore the impact of rate limiting on user experience
• Fail closed (deny all) when rate limiter fails