Rate Limiting
AI Summary
In this video, the discussion focuses on the complexities of rate limiting, particularly in the context of service management. The presenter highlights the importance of rate limiting, showcasing scenarios like preventing spam in Twitch chats and securing login forms from brute-force attacks. Various algorithms for rate limiting are introduced, including:
Fixed Window Limiter: This algorithm allows a specific number of requests within a fixed time period. However, it can enable bursts up to double the limit, posing a risk as demonstrated by GitHub’s API example.
Sliding Window Limiter: This method smoothes out request distribution over time but is more complicated to implement due to the need for precise timestamp tracking.
Token Bucket: Here, a bucket fills with tokens at a set rate, allowing bursts but enforcing a lower average limit over time. This algorithm is flexible and well-suited for situations requiring both strict control and bursts of requests.
The video highlights the practical use of these algorithms in tech companies like Stripe and GitHub, emphasizing the need for a persistent data store for rate limiting, smart error handling to communicate limits to users, and considerations for scaling services. In conclusion, the presenter emphasizes the importance of selecting the right rate limiting method based on service needs, whether that be predictability, smoothing traffic, or flexibility with bursts.
Description
Rate limiting is hard, so I wanted to go over all the ways you can do it.
SOURCE
https://smudge.ai/blog/ratelimit-algorithms
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S/O Ph4se0n3 for the awesome edit 🙏