rate limiting | BestHub

IT Services Circle

Jun 13, 2025 · Information Security

Defending Against Million‑QPS Attacks: Rate Limiting, Fingerprinting & Real‑Time Rules

This article explains how to protect systems from massive malicious traffic reaching millions of queries per second by combining gateway rate limiting, distributed circuit breaking, device fingerprinting, behavior analysis, dynamic rule engines, and real‑time risk scoring, illustrated with Nginx‑Lua, Sentinel, Drools, and Flink examples.

DDoS mitigationbackend securitydevice fingerprinting

0 likes · 15 min read

Defending Against Million‑QPS Attacks: Rate Limiting, Fingerprinting & Real‑Time Rules

Architecture and Beyond

Jun 8, 2025 · Backend Development

Designing Queueing and Rate Limiting for Scalable AIGC Services

This article explains why queueing systems and rate‑limiting strategies are essential for AIGC platforms, describes the user‑facing product behaviors they produce, outlines design considerations, compares technical options, and provides practical implementation guidance to keep services stable, cost‑effective, and user‑friendly.

AIGCBackendQueue

0 likes · 30 min read

Designing Queueing and Rate Limiting for Scalable AIGC Services

Mike Chen's Internet Architecture

Jun 4, 2025 · Backend Development

Understanding Spring Cloud Gateway’s Non‑Blocking Architecture for Million‑Level Concurrency

The article explains how Spring Cloud Gateway leverages a fully non‑blocking, reactive architecture built on Project Reactor and Netty to handle millions of concurrent requests, and discusses essential protection mechanisms such as rate limiting, circuit breaking, and degradation for high‑traffic scenarios.

BackendReactiveSpring Cloud Gateway

0 likes · 6 min read

Understanding Spring Cloud Gateway’s Non‑Blocking Architecture for Million‑Level Concurrency

Java Architect Essentials

May 27, 2025 · Backend Development

Simplifying API Rate Limiting in SpringBoot with the @RateLimiter Annotation

This article explains how the @RateLimiter annotation in SpringBoot can replace manual, verbose rate‑limiting code with a concise declarative approach, covering basic usage, custom strategies, real‑world examples, and the benefits of reduced complexity and improved system stability.

APIBackendJava

0 likes · 7 min read

Simplifying API Rate Limiting in SpringBoot with the @RateLimiter Annotation

Java Captain

May 14, 2025 · Backend Development

Redis Rate Limiter Implementation with AOP Annotation Extraction and Expiration Support

This article explains how to extract AOP annotation code, implements a Redis-based rate limiter using Redisson in Java, analyzes the underlying Lua script, and extends the utility to support automatic expiration of the limiter, providing complete code examples and detailed explanations.

AOPBackendJava

0 likes · 5 min read

Redis Rate Limiter Implementation with AOP Annotation Extraction and Expiration Support

Java Captain

May 14, 2025 · Backend Development

Rate Limiting in Java: Redis, Spring AOP, and Custom Annotations

This article explains the concept of rate limiting, introduces common algorithms such as token bucket and leaky bucket, and provides a complete Spring‑Boot implementation using Redis, custom annotations, AOP, and utility classes for distributed request throttling.

AOPAnnotationJava

0 likes · 21 min read

Rate Limiting in Java: Redis, Spring AOP, and Custom Annotations

Full-Stack Internet Architecture

May 7, 2025 · Backend Development

Understanding Java Semaphore: Methods, Usage, and Example Code

This article explains Java's Semaphore concurrency primitive, its core methods, fair vs. non‑fair modes, typical scenarios such as rate limiting and resource pooling, and provides two complete code examples demonstrating thread coordination and download throttling.

ConcurrencyJavabackend development

0 likes · 6 min read

Understanding Java Semaphore: Methods, Usage, and Example Code

macrozheng

Apr 29, 2025 · Backend Development

How to Tame a 100× Traffic Surge: Practical Strategies for Backend Engineers

This guide walks backend developers through a step‑by‑step approach to handle sudden 100‑fold traffic spikes, covering emergency response, traffic analysis, robust system design, scaling techniques, circuit breaking, message queuing, and stress testing to keep services resilient and performant.

Scalingbackend performancecircuit breaking

0 likes · 12 min read

How to Tame a 100× Traffic Surge: Practical Strategies for Backend Engineers

Selected Java Interview Questions

Apr 27, 2025 · Backend Development

Comprehensive Guide to Rate Limiting in Microservice Architecture Using Dubbo, Spring Cloud, Guava, Sentinel, Redis, and a Custom Spring Boot Starter

This article explains why rate limiting is critical for microservice stability, compares implementations across Dubbo, Spring Cloud, and gateway layers, details common algorithms such as token bucket, leaky bucket, and sliding window, and provides step‑by‑step code examples for Guava, Sentinel, Redis‑Lua, and a reusable Spring Boot starter to enforce rate limits in Java back‑end services.

BackendGuavaRedis

0 likes · 25 min read

Comprehensive Guide to Rate Limiting in Microservice Architecture Using Dubbo, Spring Cloud, Guava, Sentinel, Redis, and a Custom Spring Boot Starter

IT Services Circle

Apr 23, 2025 · Backend Development

Handling Sudden Traffic Spikes in Backend Systems

The article outlines a comprehensive approach for backend engineers to manage a sudden 100‑fold increase in traffic, covering emergency response, traffic analysis, robust system design, rate limiting, circuit breaking, scaling, sharding, pooling, caching, asynchronous processing, and stress testing to ensure system stability and performance.

CachingScalingasynchronous processing

0 likes · 13 min read

Handling Sudden Traffic Spikes in Backend Systems

Code Mala Tang

Apr 18, 2025 · Backend Development

Boost FastAPI Performance: Integrate Redis for Caching, Rate Limiting, and Queues

Learn how to set up Redis with Docker, integrate it into a FastAPI app using a custom caching decorator, and apply Redis for caching API responses, rate limiting, task queues, and distributed locks, complete with practical code examples and deployment tips.

CachingFastAPIPython

0 likes · 11 min read

Boost FastAPI Performance: Integrate Redis for Caching, Rate Limiting, and Queues

IT Services Circle

Apr 18, 2025 · Backend Development

Preventing Duplicate Orders under 100k QPS: Full‑Stack Strategies

This article explains how to prevent duplicate orders in a high‑traffic system handling 100,000 QPS by combining frontend safeguards such as button disabling and token mechanisms with backend techniques including NGINX rate limiting, gateway token validation, idempotent database design, sharding, distributed locks, optimistic locking, and comprehensive logging and monitoring.

High ConcurrencyIdempotencybackend design

0 likes · 7 min read

Preventing Duplicate Orders under 100k QPS: Full‑Stack Strategies

macrozheng

Apr 17, 2025 · Backend Development

Mastering Rate Limiting: 4 Proven Strategies to Protect Your Services

Facing a sudden 35% error rate in a payment API, the article explores why unprotected services crash, then details four common rate‑limiting algorithms—fixed window, sliding window, leaky bucket, token bucket—offering Java implementations, real‑world case studies, pitfalls, and performance tuning tips for production systems.

BackendDistributed SystemsJava

0 likes · 10 min read

Mastering Rate Limiting: 4 Proven Strategies to Protect Your Services

IT Services Circle

Apr 9, 2025 · Backend Development

Practical Guide to Rate Limiting: Algorithms, Implementation, and Production Cases

This article explains the fundamentals and practical implementations of common rate‑limiting algorithms—including fixed‑window, sliding‑window, leaky‑bucket, and token‑bucket—provides Java and Redis code samples, discusses their advantages, pitfalls, and real‑world production scenarios, and offers performance‑tuning tips.

Distributed SystemsJavaRedis

0 likes · 10 min read

Practical Guide to Rate Limiting: Algorithms, Implementation, and Production Cases

Java Captain

Apr 4, 2025 · Backend Development

High Concurrency: Principles, Impacts, and Practical Solutions for Backend Systems

This comprehensive guide explains the nature of high concurrency, distinguishes it from parallelism, outlines its potential consequences across application, database, and service layers, and presents a systematic set of mitigation strategies—including rate limiting, asynchronous processing, redundancy, caching, and queue‑based designs—supported by real‑world case studies and code examples.

CachingDatabase OptimizationHigh Concurrency

0 likes · 27 min read

High Concurrency: Principles, Impacts, and Practical Solutions for Backend Systems

Cognitive Technology Team

Apr 2, 2025 · Backend Development

Understanding Rate Limiting: Importance, Types, Algorithms, and Implementation

This article explains the concept of rate limiting in system design, covering its importance, common use cases, various types, popular algorithms such as token bucket and leaky bucket, implementation across different system layers, and the challenges associated with configuring and scaling rate‑limiting solutions.

Backendalgorithmperformance

0 likes · 10 min read

Understanding Rate Limiting: Importance, Types, Algorithms, and Implementation

Code Mala Tang

Mar 21, 2025 · Backend Development

How to Implement Rate Limiting in FastAPI with SlowAPI

This tutorial explains how to add request rate limiting to a FastAPI application using the SlowAPI library, covering both IP‑based limits and custom token‑based strategies, with installation steps, code examples, and best‑practice recommendations.

BackendFastAPIPython

0 likes · 5 min read

How to Implement Rate Limiting in FastAPI with SlowAPI

Selected Java Interview Questions

Mar 14, 2025 · Backend Development

Design and Implementation of a Business Rate‑Limiting Component Using Redis Lua Scripts and Kotlin Annotations

This article explains how to design and implement a business-specific rate‑limiting component in Kotlin using Redis Lua scripts and Spring AOP annotations, covering the motivation, rule definitions, counting strategies, Lua script details, annotation creation, and a practical usage example.

AnnotationBackendKotlin

0 likes · 10 min read

Design and Implementation of a Business Rate‑Limiting Component Using Redis Lua Scripts and Kotlin Annotations

Alibaba Cloud Infrastructure

Mar 11, 2025 · Cloud Native

Implementing Per‑User Rate Limiting with Alibaba Cloud Service Mesh (ASM) Traffic Scheduling Suite

This article explains how to use Alibaba Cloud Service Mesh (ASM) traffic‑scheduling suite to implement rich traffic‑control scenarios such as per‑user rate limiting, request queuing and priority scheduling in a Kubernetes environment, providing step‑by‑step deployment, configuration and verification instructions.

ASMcloud nativekubernetes

0 likes · 14 min read

Implementing Per‑User Rate Limiting with Alibaba Cloud Service Mesh (ASM) Traffic Scheduling Suite

FunTester

Mar 2, 2025 · Operations

Common Fault Propagation Patterns and Prevention Strategies in Distributed Systems

The article examines typical fault propagation scenarios such as avalanche effects, cascading failures, resource exhaustion, data pollution, and dependency cycles in distributed systems, and outlines proactive measures like rate limiting, circuit breaking, isolation, monitoring, and chaos engineering to prevent small issues from escalating into large-scale outages.

Distributed Systemschaos engineeringcircuit breaker

0 likes · 11 min read

Common Fault Propagation Patterns and Prevention Strategies in Distributed Systems