Python Rate Limiting: Algorithms to Production
Rate limiting protects APIs from abuse, prevents resource exhaustion, and ensures fair access across clients. In Python, you will encounter rate limiting from both sides: implementing it in your own APIs and handling it when consuming external services. The algorithms, data stores, and patterns differ significantly between these use cases.
This learning path covers the core algorithms (token bucket, sliding window, fixed window), framework-specific implementations for FastAPI and Flask, async throttling patterns, Redis-backed distributed limiting, and strategies for gracefully handling 429 responses from third-party APIs.
Algorithms and Concepts
4 articlesPython API Rate Limiting: Token Bucket Algorithm
How the token bucket algorithm works, implementing it in Python, and tuning bucket size and refill rate.
Python API Rate Limiting with Redis Sliding Window
Implementing sliding window rate limiting with Redis sorted sets for distributed, accurate limiting.
Fixed Window vs Sliding Window vs Token Bucket
Side-by-side comparison of the three major rate limiting algorithms -- trade-offs, accuracy, and implementation complexity.
Adaptive Rate Limiting in Python to Prevent DDoS and Abuse
Dynamic rate limits that adjust based on traffic patterns, threat signals, and client behavior.
Framework Implementations
3 articlesFastAPI Rate Limiter Middleware
Building rate limiting middleware for FastAPI with Redis backends and per-route configuration.
Flask Rate Limiting with Flask-Limiter and Redis
Setting up rate limiting in Flask using Flask-Limiter, decorators, and Redis storage.
requests-ratelimiter: Throttle Python HTTP Requests
Client-side rate limiting for outbound requests using the requests-ratelimiter library.
Async and Client-Side Patterns
4 articlesPython asyncio Rate Limiting: Throttle Concurrent Requests
Using semaphores and custom limiters to control concurrency in async Python code.
Handle Rate Limits in Async Python with Semaphores
Implementing backpressure and rate-aware request scheduling in asyncio applications.
Python Rate Limiting for OpenAI API: Tokens and Requests
Managing both token-based and request-based rate limits when consuming the OpenAI API.
Handle 429 Too Many Requests with Exponential Backoff
Implementing retry logic with exponential backoff, jitter, and circuit breaker patterns for 429 responses.