Rate Limiter System Design: Token Bucket, Leaky Bucket, Scaling
Rate limiters manage API request volumes by implementing algorithms like fixed window counting and token bucket to prevent system overload and ensure fair access, while addressing challenges like window boundary issues and maintaining minimal latency.
MAIN POINTS FROM TRANSCRIPT
- Rate limiters control API request volumes to prevent overload and ensure fair access.
- Fixed window counting resets request counters at set intervals, but has boundary issues.
- Token bucket algorithm allows request bursts while maintaining overall rate limits.
- In-memory shared data stores like Redis help manage counters across multiple servers.
TAKEAWAYS
- Rate limiters should reject excess requests with HTTP 429 and provide rate limit details.
- Minimal latency overhead is crucial for efficient rate limiting systems.
- Fixed window counting can lead to unintended request bursts at window boundaries.
- Token bucket algorithm is preferred by major companies for handling rate limits effectively.