This article walks through real‑world Go rate‑limiting strategies—from naive checks to fixed, sliding, and token‑bucket algorithms—explaining their pitfalls, implementation details, performance trade‑offs, and practical tips for choosing and deploying the right solution in production services.
This article explores how common engineering problems in Go projects—such as rate limiting, cache eviction, and task scheduling—are fundamentally algorithmic, explains why developers often overlook them, and shows where and how to apply practical algorithms to improve performance, stability, and scalability.
This article walks through the design and implementation of a lightweight API firewall embedded in a Spring Boot application, covering background pain points, architecture, core capabilities such as black‑white list, rate limiting, online configuration, detailed Java code, front‑end console, deployment, and practical use cases.
This article explains how Alibaba Cloud Knative, built on Kubernetes, works with the ASM service mesh to provide serverless capabilities, detailing its architecture, deployment steps, code examples, and advanced traffic management features such as rate limiting, circuit breaking, and priority scheduling.
The article explains interface‑level failures in business systems and presents four mitigation strategies—degradation, circuit breaking, rate limiting, and queuing—detailing their principles, implementation methods, and algorithmic choices such as fixed and sliding windows, token bucket and leaky bucket.
This article explains how high‑availability backend systems use traffic governance techniques—including circuit breakers, various isolation strategies, retry and timeout policies, degradation mechanisms, and rate‑limiting—to maintain balanced data flow, prevent cascading failures, and ensure performance, scalability, and reliability.
The article explains how traffic governance—including circuit breaking, isolation, retries, degradation, timeout handling, and rate limiting—maintains the three‑high goals of high performance, high availability, and easy scalability in microservice architectures, using practical examples and formulas.
This tutorial explains how to restrict client access to specific APIs within a time window using Spring Cloud Gateway and Redis, covering the concept, implementation options, and complete code examples for a Redis‑backed rate limiter.
This guide demonstrates how to build a Spring Cloud Gateway for microservices, covering system setup, CORS handling, rate‑limiting with the token‑bucket algorithm, password hashing with BCrypt, and secure JWT‑based authentication, complete with Maven dependencies, configuration files, and filter implementations.
This article explains the complete user login process, including mobile verification, token generation, token expiration policies, request‑rate limiting with Redis, anonymous request handling, blacklist management, and provides full Java Spring code examples for implementing these backend authentication mechanisms.
This article presents a comprehensive guide to microservice architecture evolution, covering routing, multi‑active deployment, gray releases, canary/rolling/blue‑green strategies, traffic shading, rate limiting, and practical implementation steps using cloud‑native gateways, service meshes, and Kubernetes labeling.
This guide explains how Spring Cloud Gateway's RequestRateLimiter works, how to configure Redis‑based token‑bucket throttling, customize key resolution, and implement a custom RateLimiter bean, providing detailed code examples and configuration tips for robust backend traffic control.
Spring Cloud Tencent integrates Tencent's Polaris platform to provide a comprehensive, out‑of‑the‑box microservice solution for Spring Boot and Spring Cloud users, covering service registration, configuration management, routing, rate limiting, circuit breaking, and proximity routing, while emphasizing low‑cost, high‑efficiency, and stable architecture for Java enterprises.
This tutorial demonstrates how to protect Spring Boot services from malicious requests by creating a custom interceptor that tracks request counts per IP and URL, uses Redis for distributed locking, and disables abusive IPs after a configurable threshold within a defined time window.
This article explores how to leverage Redis for a wide range of backend functionalities—including caching, distributed data sharing, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow relationships, and leaderboards—providing practical code examples and usage patterns.
This article walks through nine practical Redis use cases—including basic KV caching, distributed locking, delayed queues, rate limiting, service discovery, bitmap storage, HyperLogLog counting, roaring bitmaps, and Bloom filters—explaining the underlying concepts, configuration tips, and code examples for robust backend development.
This article explains the key challenges of flash‑sale systems—overselling, massive concurrency, URL exposure, and database pressure—and presents a complete backend design with isolated databases, Redis clustering, dynamic URLs, rate‑limiting, asynchronous order processing, and service degradation strategies.
This tutorial shows how to integrate Sentinel with Nacos to persist interface rate‑limiting rules in a Spring Cloud Alibaba application, covering dependency setup, configuration properties, sample code, Nacos rule definition, and verification steps.
The article explains the causes of service avalanche in distributed systems—especially cache avalanche—and presents comprehensive mitigation strategies such as diversified cache expiration, circuit‑breaker, isolation, and rate‑limiting techniques to keep applications resilient under load.
The article explains the need for rate limiting in high‑traffic scenarios, describes common algorithms such as token‑bucket and Guava RateLimiter, compares single‑node and distributed implementations, and outlines a Redis‑based solution for precise flow control across multiple service instances.
