This article explains why rate limiting is essential for high‑traffic services, describes the token‑bucket algorithm, shows how to use Guava's RateLimiter and Cache for single‑node limits, and presents a Redis‑based solution that works across distributed instances.
This article explains the design of a Redis‑based rate‑limiting system, covering core concepts, token‑bucket and window‑based algorithms, Java and Lua implementations, engineering choices, performance benchmarks, and practical lessons learned.
This article explains how to protect API servers from abuse by implementing rate limiting using the Token Bucket algorithm, discusses its drawbacks, and provides practical Java and Redis-based solutions with code examples and performance considerations.
This article explains why availability and reliability are critical for web APIs, outlines four common rate‑limiting techniques used at Stripe, describes how to choose and implement request, concurrent, usage‑based, and worker‑utilization limiters, and provides practical guidance for safely deploying them in production.
This article explains why availability and reliability are essential for web APIs, outlines four common rate‑limiting approaches used by Stripe—including request, concurrent, usage‑based, and worker‑utilization limiters—and provides practical guidance on implementing token‑bucket limiters with Redis while ensuring safe error handling and gradual rollout.
This article provides a comprehensive guide to rate limiting in high‑concurrency systems, covering core concepts, token‑bucket and leaky‑bucket algorithms, application‑level techniques with Guava, distributed implementations using Redis+Lua and Nginx+Lua, and edge‑layer controls via Nginx modules, complete with configuration examples and test results.
This article explains how caching, degradation, and especially rate limiting—using token bucket, leaky bucket, and counter‑based methods—protect high‑concurrency systems, covering algorithm basics, application‑level techniques, and distributed implementations with Redis+Lua and Nginx+Lua.
This article explains why caching, degradation, and rate limiting are essential for high‑concurrency systems, details token‑bucket and leaky‑bucket algorithms, shows application‑level, distributed, and edge‑level throttling techniques, and provides practical Java, Guava, Redis‑Lua, and Nginx‑Lua code examples.
The article explains how to protect high‑traffic systems during events like Double 11 by using token‑bucket rate limiting, describes the algorithm, compares Guava's SmoothBursty and SmoothWarmingUp implementations, and provides a simple Java TrafficShaper example with code.
