I built this after implementing rate limiting for the nth time and realizing there wasn't a good drop-in solution that balanced performance, operational simplicity, and developer experience.
Key characteristics:
- 50,000+ RPS with <2ms P95 latency
- Redis-backed token bucket algorithm with atomic Lua scripts
- 18 REST endpoints for management, monitoring, and benchmarking
- Automatic failover to in-memory backend (fail-open strategy)
- Per-key and pattern-based configuration (user:, api:premium:)
What makes it different from existing solutions:
- Production-first approach (complete K8s manifests, monitoring, runbooks)
- Built-in load testing and performance regression detection
- Comprehensive client examples in 5 languages
- 265+ tests including concurrent and integration testing
- Operational simplicity (single JAR, Docker, or K8s deployment)
The project documents architectural decisions in ADR format and includes everything needed for production deployment. MIT licensed.
Technical details and performance benchmarks in the README.
GitHub: https://github.com/uppnrise/distributed-rate-limiter
Key characteristics: - 50,000+ RPS with <2ms P95 latency - Redis-backed token bucket algorithm with atomic Lua scripts - 18 REST endpoints for management, monitoring, and benchmarking - Automatic failover to in-memory backend (fail-open strategy) - Per-key and pattern-based configuration (user:, api:premium:)
What makes it different from existing solutions: - Production-first approach (complete K8s manifests, monitoring, runbooks) - Built-in load testing and performance regression detection - Comprehensive client examples in 5 languages - 265+ tests including concurrent and integration testing - Operational simplicity (single JAR, Docker, or K8s deployment)
The project documents architectural decisions in ADR format and includes everything needed for production deployment. MIT licensed.
Technical details and performance benchmarks in the README.
GitHub: https://github.com/uppnrise/distributed-rate-limiter