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Distributed Rate Limiter with Sliding Window Algorithm

redis distributed-systems rate-limiting scalability
Prompt
Implement a distributed rate limiting system using Redis that supports a sliding window rate limiting algorithm. Design the solution to handle concurrent requests across multiple server instances, track request rates per user/IP, and dynamically adjust thresholds. Include mechanisms for atomic increments, expiration tracking, and horizontal scaling considerations. Provide a configurable interface that can limit requests per second, minute, and hour with minimal performance overhead.
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Python
Technology
Feb 28, 2026

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Use Cases
  • Managing API request limits for web applications.
  • Preventing denial-of-service attacks on servers.
  • Ensuring fair resource allocation among users.
Tips for Best Results
  • Monitor traffic patterns to adjust limits effectively.
  • Implement alerts for unusual request spikes.
  • Test the system under load to ensure reliability.

Frequently Asked Questions

What is a distributed rate limiter?
It's a system that controls the rate of requests to a service across multiple servers.
How does the sliding window algorithm work?
It allows for dynamic request limits based on time intervals.
What are the benefits of using this tool?
It helps prevent server overload and ensures fair usage among users.
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