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Distributed Rate Limiter with Sliding Window Algorithm

distributed systems rate limiting microservices redis
Prompt
Design a distributed rate limiting system using Redis that implements a sliding window algorithm for microservice API protection. Create a mechanism that tracks request rates across multiple service instances, supports configurable rate limits per endpoint, and handles edge cases like burst traffic. Implement graceful degradation with 429 (Too Many Requests) responses and include comprehensive logging for monitoring and audit purposes.
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Python
Technology
Feb 28, 2026

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Tool
Ai Chat
Details
Category Pro
Purpose Coding
Platform Python
Industry Technology
Added Feb 28, 2026
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Use Cases
  • Managing API request limits for a cloud service.
  • Controlling user access to a web application.
  • Balancing load during peak traffic periods.
Tips for Best Results
  • Implement logging to monitor request patterns.
  • Adjust time window sizes based on traffic needs.
  • Combine with other rate limiting strategies for best results.

Frequently Asked Questions

What is a distributed rate limiter?
A distributed rate limiter controls the number of requests across multiple servers.
How does the sliding window algorithm work?
It tracks requests in fixed time intervals, allowing for more flexible rate limiting.
What are the benefits of using this algorithm?
It improves performance and fairness in request handling across distributed systems.

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