This article explains why rate limiting is essential for microservice stability, compares leaky‑bucket and token‑bucket algorithms, shows how to implement local and distributed throttling with Java's AtomicLong, Redis, and a control‑server architecture, and points to an open‑source project for practical use.
This article explains how to handle massive, bursty traffic in lottery‑style applications by designing a load‑balancing layer that filters invalid requests, optimizing Tomcat thread pools, and moving core business logic to Redis while using message queues to smooth gift‑distribution spikes.
The article explains how to handle massive simultaneous user requests in a flash‑sale scenario by using rate limiting, caching, asynchronous processing, distributed locks, load balancing, and anti‑cheat mechanisms, illustrated with the Sohu Focus online opening system architecture.
This article explores best‑practice patterns for microservice communication, covering synchronous and asynchronous protocols, serialization formats, API design, service discovery, versioning, rate limiting, circuit breakers, correlation IDs, distributed consistency, authentication, retries, and economic considerations.
This article explains how to extend the Moco API with a custom LimitHandler to restrict request frequency, provides the Java implementation of the handler, and demonstrates several overloads for configuring default and custom intervals for rate limiting in backend testing scenarios.
This article examines the limitations of Spring Cloud Gateway's native RequestRateLimiter, introduces Sentinel's flow control integration, provides Maven dependencies, configuration examples, and demonstrates dynamic rate‑limiting adjustments via Nacos, illustrating effective traffic protection for microservice gateways.
This article introduces Nginx’s key capabilities—including static file serving, various load‑balancing strategies, leaky‑bucket rate limiting, browser and proxy caching, and black‑/white‑list access control—explaining how each feature can be configured and applied in high‑concurrency web environments.
This article explores Redis beyond its traditional caching role, covering distributed locks, message queues, bitmaps, Bloom filters, geolocation, and rate limiting, while highlighting typical implementation patterns, code examples, and the inherent limitations of each scenario.
This article explains how to extend Moco's API mocking capabilities by creating a custom LimitHandler that tracks request timestamps and enforces a configurable interval, providing a reusable solution for preventing rapid repeated submissions in test services.
This article explains the concept of circuit breaking and rate limiting in micro‑service architectures, compares Hystrix and Istio implementations, details Istio's ConnectionPool and outlierDetection settings, maps their parameters to Envoy, and provides step‑by‑step command‑line examples that demonstrate how these controls behave in practice.
This article demonstrates how to build a distributed rate‑limiting component named shield‑ratelimiter using Redis’s INCR and TTL features, Spring‑Boot‑starter integration, Lua scripting for atomic operations, custom annotations, and AspectJ, providing a robust, configurable solution for limiting API calls in Java backend services.
This article outlines eight practical high‑availability techniques—multiple replicas, isolation, rate limiting, circuit breaking, degradation, gray releases with rollback, comprehensive monitoring, and proactive log alerting—to help engineers design systems that are both efficient and reliable under heavy load.
This article explains how the IoT business platform adopts Alibaba Sentinel to provide circuit breaking and rate‑limiting for Dubbo microservices, integrates with Apollo for centralized rule distribution, implements automatic downgrade, thread isolation, and cache‑based degradation to improve system reliability and resilience.
Rate limiting is essential for microservice resilience, preventing overloads and protecting business continuity, and can be implemented through various methods such as semaphores, thread‑pool isolation, fixed or sliding windows (using Redis ZSets or local memory), token‑bucket/leaky‑bucket algorithms, each with trade‑offs in accuracy, performance, clock synchronization, and deployment location.
This article explains a practical circuit‑breaker design for backend services, detailing detection logic, algorithm thresholds, time‑window statistics, recovery duration, manual overrides, a global switch, and how to monitor the breaker’s current state using Redis.
The article explains why interface-level failures occur, distinguishes internal and external causes, and presents four practical strategies—degradation, circuit breaking, rate limiting, and queuing—to keep core services running and protect most users under heavy load.
This tutorial explains how Sentinel, the distributed system traffic guardian, can be deployed and integrated into a Spring Cloud Alibaba application to provide flow control and circuit breaking, covering dashboard setup, Maven configuration, code examples, rule creation, and verification of rate‑limiting behavior.
This article explains what a message queue (MQ) is, why it is needed beyond in‑memory Java queues, and how it enables decoupling, asynchronous processing, peak‑shaving and rate‑limiting, while also discussing high‑availability, data‑loss, and consumer‑side considerations.
This article explains why rate limiting is essential for high‑availability systems, compares single‑node and distributed approaches, and provides a complete Redis + Lua implementation with Java integration, including code samples and architectural diagrams to help developers.
This guide explains why and how to use Nginx as an HTTPS reverse proxy for WeChat mini‑programs, covering kernel requirements, downloading, compiling with required modules, installation steps, starting the server, core configuration details, enabling SSL, and applying rate‑limiting rules.
This article explains the difference between rate limiting and circuit breaking, shows how to determine system capacity, compares fixed‑window, sliding‑window, leaky‑bucket and token‑bucket algorithms with code examples, and offers best‑practice guidance for applying them in distributed backend systems.
This comprehensive guide explains the differences between rate limiting and circuit breaking, outlines how to determine system capacity, details four core throttling strategies (fixed window, sliding window, leaky bucket, token bucket), and offers practical best‑practice recommendations for distributed backend systems.
This article explains the evolution and classification of web crawlers, describes the design and components of the Mole distributed crawler—including scheduler, fetcher, processor, rate‑limiting, URL deduplication, and Elasticsearch storage optimization—and outlines common anti‑anti‑crawling strategies.
With Hystrix no longer receiving new features, this article explains how developers can smoothly transition to Alibaba’s Sentinel by comparing the two libraries, detailing command migration, thread‑pool vs semaphore isolation, circuit‑breaker configurations, annotation support, and dynamic rule management for robust microservice resilience.
Ensuring stability for sites handling up to 100,000 requests per minute requires a combination of configuration management, feature toggles, phased deployment, robust error handling, comprehensive logging, real-time monitoring, traffic-aware throttling, service degradation, and disaster-recovery tactics, all of which are detailed in this guide.
This article explains how to use Nginx’s built‑in limit_req and limit_conn modules to implement request‑rate and connection‑based throttling, covering configuration directives, execution flow, burst handling, delay modes, whitelist setup with geo and map modules, and practical examples for IP and domain limits.
This article describes the shortcomings of a legacy log‑collection architecture, proposes a streamlined real‑time design that centralises configuration in Etcd, uses a single Go‑based logagent to tail files, applies per‑service rate limiting, and forwards logs to Kafka for downstream processing.
This article shares practical insights on handling high‑concurrency traffic, explaining what constitutes large traffic, common mitigation strategies such as caching, downgrade, and focusing on rate‑limiting techniques—including counters, sliding windows, leaky‑bucket and token‑bucket algorithms—and demonstrates using Guava’s RateLimiter for Java applications.
This article explains four common rate‑limiting algorithms—fixed‑window counter, sliding window, leaky bucket, and token bucket—detailing how each works, their pseudo‑code implementations, and when to use them to protect high‑traffic systems such as flash‑sale platforms.
This guide explains how Spring Cloud Gateway replaces Zuul and provides Redis‑based multi‑dimensional rate limiting, covering Maven dependencies, YAML configuration, custom key resolver beans, stress testing, Redis key monitoring, core Java implementation, and the underlying Lua script.
The article examines the technical challenges of high‑concurrency flash‑sale events such as Double 11, and presents a backend‑centric architecture employing rate limiting, cache (Redis), message‑queue peak‑shaving, and asynchronous processing to ensure scalability and prevent system avalanches.
This article shares practical guidance on designing, protecting, and continuously improving high‑concurrency backend services—covering baseline capacity, rate limiting, data‑structure optimization, stateless architecture, and horizontal scaling—to help engineers evolve small systems into robust, production‑grade platforms.
This article describes how JD built a high‑concurrency price‑protection service for the 618 promotion, covering rate‑limiting, degradation, CDN and data caching, database sharding with smooth expansion, and a multi‑stage, fault‑tolerant task‑processing workflow to achieve unlimited scalability.
This article discusses practical approaches to handling massive traffic spikes—defining high‑traffic scenarios, common mitigation methods such as caching, degradation, and various rate‑limiting algorithms (counter, sliding window, leaky bucket, token bucket), including Guava's RateLimiter and brief notes on distributed implementations.
This article explains why rate limiting is essential for flash‑sale (seckill) systems, compares token‑bucket and leaky‑bucket algorithms, and provides concrete Tomcat, Nginx, OpenResty, and Guava configurations along with code snippets and load‑testing results to help engineers implement robust throttling.
This guide explains how to implement global rate limiting for high‑traffic Spring Boot applications by using Redis caches and MySQL storage, covering request interception, Redis key structures for limits and counters, and dynamic configuration for adding, updating, or deleting limits.
This article explains why rate limiting is essential for flash‑sale (seckill) services, compares token‑bucket and leaky‑bucket algorithms, and provides practical configuration examples for Tomcat, Nginx, and OpenResty, along with testing methods and code snippets.
This article explains why rate limiting is essential for large‑scale flash‑sale (seckill) services, introduces token‑bucket and leaky‑bucket algorithms, and demonstrates practical implementations using Tomcat thread pools, Nginx, OpenResty, and Guava RateLimiter together with stress‑testing commands.
This article explains why high‑traffic scenarios like flash‑sale systems need rate limiting, compares token‑bucket and leaky‑bucket algorithms, and shows practical configurations for Tomcat, Nginx, and OpenResty to protect APIs and ensure system stability.
This article explains how modular design and service extraction can reduce system complexity and improve reusability, illustrates practical before‑and‑after examples for red‑packet and notification services, and details high‑concurrency techniques such as caching, asynchronous processing, rate limiting, service degradation, anti‑fraud measures, and concurrency‑safe database operations.
During high‑traffic scenarios, services can become unavailable, so implementing rate‑limiting techniques like token bucket, leaky bucket, and counter algorithms—illustrated with Java code examples using Guava RateLimiter, AtomicInteger, and Semaphore—helps smooth bursts, control concurrency, and prevent system overload.
The article explains how to design a backend architecture that can handle millions of concurrent requests by applying principles such as service decomposition, high availability, idempotent business logic, and various rate‑limiting algorithms—including sliding window, leaky bucket and token bucket—with a runnable Java demo.
This article analyzes the rapid growth challenges of Meituan‑Dianping's core payment flow, explains key availability metrics such as MTBF and MTTR, and presents a comprehensive set of architectural, operational, and tooling strategies—including dependency decoupling, timeout tuning, circuit breaking, and full‑link stress testing—to achieve stable, fault‑tolerant transactions.
This article introduces the fundamentals of rate limiting, compares common algorithms such as counter, leaky bucket, and token bucket, delves into Guava's RateLimiter design and its SmoothBursty and SmoothWarmingUp implementations, and provides practical Java code examples for quick integration.
This article explains service branch mode, its impact on availability, asynchronous messaging, shared‑data anti‑patterns, and essential microservice fault‑tolerance techniques such as compartment isolation, circuit breaking, rate limiting, and failover strategies.
Effective fault tolerance is crucial for microservice stability, and this article explores core design principles and classic patterns—such as timeout retries, rate limiting, bulkhead isolation, circuit breakers, and fallback strategies—guiding developers to choose and combine the right approaches for high‑availability systems.
This article discusses the definition of high traffic, common mitigation methods such as caching, degradation, and especially various rate‑limiting algorithms—including counters, sliding windows, leaky bucket, and token bucket—and demonstrates using Guava's RateLimiter for practical throttling.
This guide explains how Nginx's rate‑limiting works, covering the leaky‑bucket algorithm, basic directives, burst and nodelay options, whitelist handling, logging, custom error codes, and advanced configuration examples for securing and optimizing HTTP request traffic.
This article explains how dynamic rate limiting can automatically compute throttling thresholds using system load metrics such as CPU usage, load average, and response time, and demonstrates its effectiveness through real‑world testing on a flight‑price service.
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.
In this reflective piece, a senior Alibaba iDST engineer shares three years of backend and big‑data platform experience, detailing project challenges, distributed rate‑limiting solutions, personal growth, and ongoing AI research, while offering insights on technical leadership and career choices.
This article examines JD.com’s technical strategies for the 2023 Double‑11 shopping festival, detailing dynamic load‑balancing and rate‑limiting mechanisms, evolving fault‑drill practices, and AI‑powered product and marketing enhancements that together ensure high‑concurrency stability and improved user experience.
The article discusses how to achieve high availability for large‑scale internet services by adopting stateless architecture, service discovery and registration, heartbeat monitoring, idempotent design, retry mechanisms, rate limiting, caching, and micro‑service decomposition to handle machine failures, network glitches, and high concurrency.
To prevent excessive user actions such as rapid screenshot requests, this guide shows how to implement a backend rate‑limiting mechanism using a reusable Python decorator that enforces a default 0.3‑second interval, handling exceptions when the limit is exceeded.
The article describes how a high‑traffic e‑commerce transaction system improves availability through rate limiting, circuit breaking, gray‑release, JVM warm‑up, an online diff testing tool, the ARES regression platform, and a DAG‑driven asynchronous execution framework to boost throughput and reduce latency.
This guide explains how to use Nginx as an HTTPS reverse proxy for WeChat mini‑programs, covering kernel requirements, downloading the proper version, compiling with necessary modules, starting the server, configuring nginx.conf, and setting up rate limiting, all on a CentOS system.
This article details the architecture and key technologies of JD’s open platform, including a high‑performance API gateway, unified metadata configuration, TCP full‑duplex long‑connection channels, Protobuf data exchange, multi‑dimensional flow control, and a reliable message‑push system designed for massive 618‑scale traffic.
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 rate limiting is essential for service performance and data security, and demonstrates two practical Redis-based implementations—a Lua script approach and a Redis‑cell module method—complete with code examples, usage details, and a brief comparison.
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 explains microservice fault‑tolerance techniques—including timeout settings, retry strategies, circuit‑breaker logic, current limiting, resource isolation, and service degradation—from both micro and macro perspectives, illustrating how to design resilient service chains and avoid cascading failures.
The article explains how Meituan‑Dianping applies classic fault‑tolerance patterns—timeout and retry, rate limiting/load shedding, circuit breaker, bulkhead isolation, and fallback—to design for failure, prevent cascading service outages, and enhance system stability and high‑availability in a service‑oriented architecture.
The article introduces JEN, JD's extended Nginx platform that centralizes configuration, monitoring, traffic splitting, rate limiting and automated operations through a web console and Ansible integration, addressing the complexity, restart requirements, and scaling challenges of large‑scale Nginx deployments.
This article explains the design of JD’s flash‑sale (抢购) system, covering its business model, core Redis‑based inventory management, Lua scripting for atomic stock deduction, rate‑limiting, asynchronous logging with JMQ, and fail‑over strategies to ensure high‑concurrency reliability.
The article details the Qunar Order Center's architecture evolution—addressing fragmented order data, introducing unified query APIs, JSON‑based storage, merge/diff services, asynchronous I/O, Elasticsearch integration, data sharding, business isolation, rate limiting, adapter layers, micro‑service decomposition, and a centralized configuration system to improve scalability, reliability, and maintainability.
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 how to implement access‑layer rate limiting in Nginx using built‑in modules, OpenResty Lua extensions, and token‑bucket or leaky‑bucket algorithms, with detailed configuration snippets, execution flow, testing procedures, and log analysis for robust traffic control.
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.
This guide explains how to use simple Nginx configurations, enhanced cookie checks, and Lua scripts to create unforgeable tokens, limit request rates, and protect web services from CC attacks, scanning tools, and other malicious traffic while maintaining normal user access.
This article explains the concept of rate limiting to protect system stability, outlines when to apply it during high‑traffic events, and provides a concise Java implementation using Semaphore, including code snippets for acquiring, processing, and releasing permits.
This article explains how a rate‑limiting system can identify hot‑spot resources by using time‑window statistics, a concurrent LRU hash map, and cluster‑wide aggregation to efficiently track and evict low‑frequency keys while maintaining high throughput.
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.
This article explains what the pv (Pipe Viewer) tool is, its functions such as monitoring data flow and rate limiting, how to install it on various Linux distributions, and provides practical command‑line examples for measuring transfer speed and throttling bandwidth.
