zevaverbach / studying-rate-limiting

I'm getting my mind around rate limiting, starting with some in-app implementations.

Purpose

This repository contains my implementations and explorations of rate limiting, drawn initially from the book System Design Interview.

I'm embarking on this in order to get a great software engineering (probably back-end) job, and at the moment I have Happy Scribe in mind since I have an interview with them on August 24th, directly after our family vacation in Greece.

Over a year ago (early 2022) I did a job search, and some of the interview processes ended right before or after the system design phase, so it's obviously something I need in my portfolio to truly be considered for senior dev roles. While I'd love to get a job as a junior or mid-level, my salary requirements and age (45) push me towards senior. That, and maybe the fact that I'm a decent programmer by now.

TODO

• implement token bucket
  • in-app
    • in-memory, lazy refill
    • redis, process to refill
• implement leaky bucket
  • in-app
    • redis
    • redis cluster
    • Flask middleware - https://flask.palletsprojects.com/en/2.1.x/quickstart/#hooking-in-wsgi-middleware
  • NGINX - https://leandromoreira.com/2019/01/25/how-to-build-a-distributed-throttling-system-with-nginx-lua-redis/ - https://www.nginx.com/blog/rate-limiting-nginx/
  • AWS API Gateway
  • HAProxy Stick Tables - https://www.haproxy.com/blog/introduction-to-haproxy-stick-tables
  • Cloudflare (Spectrum?)
• implement expiring tokens
• implement fixed window counter
• implement sliding window log
• implement sliding window counter
• use session IDs or API keys instead of IP address
• set headers appropriately in each case: https://www.ietf.org/archive/id/draft-polli-ratelimit-headers-02.html#name-ratelimit-headers-currently
• implement different rate limiting for each endpoint, using a cost variable for a given task