System Design: Designing Distributed Rate Limiters with Redis & Token Bucket
Kowshik Valipireddy
Full Stack Developer & AI Engineer
Uncontrolled API usage can exhaust database connection pools and compromise service availability. Distributed rate limiters throttle traffic deterministically.
1. Why Rate Limiting is Critical
Rate limiting protects authentication endpoints against credential stuffing, prevents API scraping, and enforces fair resource allocation across tenants.
2. Comparing Core Algorithms
Token Bucket handles bursts gracefully, Fixed Window is simple but vulnerable to boundary spikes, and Sliding Window Logs provide absolute precision at higher memory costs.
3. Atomic Redis Lua Implementation
Executing rate limit evaluation inside atomic Redis Lua scripts prevents race conditions in highly concurrent distributed server clusters.
Related Topics & Technologies
Kowshik Valipireddy
AuthorFull Stack Developer & AI Engineer
Full Stack Developer specializing in React, Next.js, Node.js, and AI workflows. Passionate about building fast, accessible, and SEO-optimized web experiences.
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