rate-limiting-implementation

---
name: rate-limiting-implementation
description: >
license: MIT
---

# Rate Limiting Implementation

## Table of Contents

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

```typescript
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:

| Guide | Contents |
|---|---|
| [Token Bucket Algorithm (TypeScript)](references/token-bucket-algorithm-typescript.md) | Token Bucket Algorithm (TypeScript) |
| [Redis-Based Distributed Rate Limiter](references/redis-based-distributed-rate-limiter.md) | Redis-Based Distributed Rate Limiter |
| [Express Middleware](references/express-middleware.md) | Express Middleware |
| [Sliding Window Algorithm (Python)](references/sliding-window-algorithm-python.md) | Sliding Window Algorithm (Python) |
| [Tiered Rate Limiting](references/tiered-rate-limiting.md) | Tiered Rate Limiting |
| [Adaptive Rate Limiting](references/adaptive-rate-limiting.md) | Adaptive Rate Limiting |

## 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