implementing-api-rate-limiting-and-throttling

# Implementing API Rate Limiting and Throttling ## When to Use - Protecting authentication endpoints against brute force and credential stuffing attacks - Preventing API abuse and resource exhaustion from automated scripts and bots - Implementing fair usage quotas for different API consumer tiers (free, premium, enterprise) - Defending against denial-of-service attacks at the application layer - Meeting compliance requirements that mandate API abuse prevention controls **Do not use** rate limiting as the sole defense against attacks. Combine with authentication, authorization, and WAF rules. ## Prerequisites - Redis 6.0+ for distributed rate limit counters (or in-memory for single-instance deployments) - API framework (Express.js, FastAPI, Spring Boot, or Django REST Framework) - Monitoring system for rate limit metrics (Prometheus, CloudWatch, Datadog) - Understanding of the API's normal traffic patterns and peak usage - Load testing tool (k6, Gatling, or Locust) for validating rate limit behavior ## Workflow ### Step 1: Rate Limiting Strategy Design Define rate limits per endpoint category and user tier: ```python # Rate limit configuration RATE_LIMITS = { # Authentication endpoints (most restrictive) "auth": { "login": {"requests": 5, "window_seconds": 60, "by": "ip"}, "register": {"requests": 3, "window_seconds": 300, "by": "ip"}, "forgot_password": {"requests": 3, "window_seconds": 3600, "by": "ip"}, "verify_mfa": {"reques...