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Distributed Rate Limiter – 50K+ RPS, Redis-Backed, Production-Ready

Hacker News

Distributed Rate Limiter – 50K+ RPS, Redis-Backed, Production-Ready

Hi HN! I built a distributed rate limiter using the token bucket algorithm with Redis backing. Key highlights: • 50,000+ requests/second throughput with <2ms P95 latency • Redis-backed distributed state for multi-instance deployments • 18 REST API endpoints for rate limiting, config, and monitoring • 265+ tests including load tests and integration tests • Docker/Kubernetes ready with comprehensive documentation Built with Java 21 + Spring Boot. Perfect for protecting APIs, microservices, or SaaS platforms from abuse. The pain point I solved: existing solutions were either too simple (in-memory only) or too complex (enterprise-only). This fills the gap with a production-ready, easy-to-deploy solution. Try it: Download the JAR, start Redis, and you're rate limiting in 30 seconds. Would love feedback on the API design and performance characteristics! GitHub: https://github.com/uppnrise/distributed-rate-limiter

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, 000 · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: dock, using, apis · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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