This article explains how to control request frequency in Python using the ratelimiter library for synchronous and asynchronous code, and introduces SlowAPI as a flexible solution for FastAPI and Flask, helping backend developers reduce server load and protect APIs from abuse.
This comprehensive guide explores Redis internals, covering its core data types and their low‑level implementations, persistence mechanisms like RDB and AOF, performance tricks, common pitfalls such as cache avalanche and split‑brain, distributed locking strategies, expiration and eviction policies, high‑availability architectures, and rate‑limiting techniques.
This guide walks you through installing Alibaba Sentinel’s dashboard, configuring a Spring Boot client, defining flow rules, and verifying that request throttling works, providing all necessary commands, Maven snippets, YAML settings, and UI screenshots for a complete end‑to‑end rate‑limiting solution.
This article introduces a straightforward Java implementation of a sliding‑window rate‑limiting algorithm for single‑machine use, explains its core logic, demonstrates its behavior with sample output, and visualizes the step‑by‑step process of how timestamps are managed within the window.
Sentinel implements rate‑limiting through a chain‑of‑responsibility where annotated methods trigger SphU.entry, creating a Context, Node, and Entry; slots such as NodeSelectorSlot, ClusterBuilderSlot, StatisticSlot, and FlowSlot update sliding‑window metrics via LeapArray and enforce QPS thresholds, throwing BlockException on violations.
This article examines the challenges of building a flash‑sale (秒杀) system—such as overselling, high concurrency, request flooding, URL security, and database strain—and presents a comprehensive backend design using separate databases, Redis clustering, dynamic URLs, static pages, Nginx, rate‑limiting, asynchronous order processing, and service degradation strategies.
This article introduces the fundamentals of API gateways, explains why Haodf replaced Nginx with Kong, details Kong's architecture, plugins, traffic segmentation, rate‑limiting strategies, monitoring, and operational considerations for deploying Kong in a micro‑service environment.
This article uses a sci‑fi story set on the A‑731 e‑commerce planet to illustrate the core principles of flash‑sale systems, including single‑responsibility services, independent deployment, stock pre‑heating, request encryption, static‑dynamic separation, traffic shaping, rate limiting, queue‑based load smoothing, and the role of Redis, Nginx, CDN and Redisson in handling massive order spikes.
This article examines the challenges of building a flash‑sale (秒杀) system—such as overselling, high concurrency, request throttling, and database strain—and presents a comprehensive backend design using separate databases, dynamic URLs, static pages, Redis clustering, Nginx, token‑bucket rate limiting, asynchronous order processing, and service degradation strategies.
This article examines the inherent risks of microservice architectures and presents practical patterns—such as graceful degradation, change management, health checks, self‑healing, fallback caching, retries, rate limiting, bulkheads, and circuit breakers—to build highly available, fault‑tolerant services.
This article explains how Spring Cloud Gateway uses a token‑bucket algorithm backed by Redis and a Lua script to perform distributed rate limiting, reviews common limiting algorithms, provides detailed Java and Lua code examples, and analyzes each step of the implementation for high‑concurrency systems.
This article explains the principles and practical techniques for achieving high availability, scalability, isolation, decoupling, rate limiting, degradation, and circuit breaking in modern software systems, providing concrete examples, algorithms, and deployment patterns to improve reliability and performance.
This article examines the critical issues of overselling, high concurrency, request throttling, and database design in flash‑sale systems, then outlines a comprehensive backend architecture with Redis clustering, Nginx load balancing, dynamic URLs, rate‑limiting, asynchronous order processing, and Java code examples to achieve a resilient high‑throughput solution.
This article explains the principles of leaky‑bucket and token‑bucket rate‑limiting algorithms, compares their behavior in high‑concurrency e‑commerce scenarios, and provides a complete Python implementation to illustrate how token‑bucket can handle burst traffic while maintaining system stability.
This article explains how Nginx can be used for static file serving, various load‑balancing strategies, rate limiting with the leaky‑bucket algorithm, caching mechanisms, and black‑/white‑list access control, providing practical guidance for improving web service performance and reliability.
This article explains how to improve a fixed QPS mock interface in Java by adding a compensation thread that adjusts request timing using a thread‑safe counter, reducing error beyond the original 5% tolerance.
This guide explains how Nginx implements rate limiting using the leaky‑bucket algorithm, walks through basic and advanced configurations—including limit_req_zone, limit_req, burst, nodelay, whitelisting, multiple limits, logging, and custom status codes—while providing concrete code examples and practical tips.
This article examines the motivations, resource granularity, rule definition, and calculation logic behind implementing rate limiting and circuit breaking in microservice architectures, using examples like Sentinel and Hystrix, and outlines a step-by-step design for integrating these controls with API gateways.
This article explains the critical importance of securing RESTful APIs—covering data protection, DoS risks, and business impact—and outlines practical measures such as authentication, API keys, access control, rate limiting, and input validation with code examples.
This article explains Nginx rate‑limiting fundamentals and advanced configurations, covering the leaky‑bucket algorithm, basic directives, burst and nodelay handling, whitelisting with geo/map, multiple limit rules, logging details, and custom error responses to protect servers and mitigate attacks.
This article explains Nginx's rate‑limiting feature, covering the leaky‑bucket algorithm, basic and advanced directives like limit_req_zone, limit_req, burst, nodelay, whitelist handling, logging, and custom error codes to control request flow and protect services.
This article explains the principles of rate limiting, compares the leaky bucket and token bucket algorithms, and demonstrates how Guava's RateLimiter (including SmoothBursty and SmoothWarmingUp implementations) works in Java with detailed code examples and execution flow.
This article explains the concept of high traffic, compares common mitigation techniques such as caching, degradation and rate limiting, and then details four classic rate‑limiting algorithms—counter, sliding window, leaky bucket and token bucket—followed by a practical Guava RateLimiter example and a brief note on distributed scenarios.
This article explains the purpose of flow control, compares various rate‑limiting algorithms—including simple window, sliding window, leaky bucket, token bucket, and sliding log—provides Java interface definitions and code examples, discusses their complexity, precision, smoothness, and suitability for single‑machine and distributed scenarios, and offers practical deployment tips using Sentinel, Nginx, Guava, Tair, and Redis.
This tutorial shows how to use the open‑source APISIX API gateway—built on OpenResty and etcd—to create a dynamic rate‑limiting plugin in Lua, configure route matching with a radix tree, and demonstrate the feature with a test endpoint that enforces one request per second and returns HTTP 503 when exceeded.
This article provides a comprehensive introduction to microservice architecture, covering service decomposition, registration and discovery methods, client‑driven load balancing, rate‑limiting and circuit‑breaking strategies, and the design of a self‑built application delivery platform for cloud‑native environments.
This article explains how to overcome the difficulty of isolating external service performance during load testing by creating a fixed‑QPS mock endpoint using Java's Semaphore and a custom Moco ResponseHandler, includes code samples, accuracy observations, and practical guidelines.
This article explains the fundamentals of rate limiting for high‑concurrency systems, covering why it is needed, the conditions that trigger it, and detailed introductions to the three main algorithms—counter (fixed and sliding windows), leaky bucket, and token bucket—along with their advantages, drawbacks, and sample pseudo‑code implementations.
This article presents a comprehensive distributed retry system that leverages a distributed scheduling mechanism to ensure eventual consistency, reduce manual recovery costs, and provide flexible retry strategies, automatic recovery detection, visual management, rate limiting, and intelligent retry for backend services.
This article outlines essential API security mechanisms—including encryption, signing, timestamps, AppId authentication, rate limiting, blacklisting, and data validation—and provides practical Java implementation examples and code snippets.
This article explores the key challenges of designing a flash‑sale (秒杀) backend—such as overselling, high concurrency, request throttling, and database bottlenecks—and presents a complete solution that includes database schema, dynamic URLs, static page rendering, Redis clustering, Nginx load balancing, SQL optimization, token‑bucket rate limiting, asynchronous order processing, and service degradation strategies.
This article explores the cat‑and‑mouse battle between crawlers and API endpoints, detailing how dynamic signatures, token‑based authentication, time‑bound hashes, rate‑limiting, and code obfuscation can be used to defend against scraping while also showing how attackers can reverse‑engineer and bypass these defenses.
This article explores the challenges of building a flash‑sale (秒杀) backend—such as overselling, massive concurrent requests, URL protection, and database bottlenecks—and presents a comprehensive architecture that leverages Redis clustering, token‑bucket rate limiting, static page rendering, asynchronous order processing, and service degradation techniques.
This article introduces Alibaba's distributed‑tools component, explaining how to configure Redis/Tair cache, implement fixed‑window rate limiting, use a high‑performance distributed lock template, and apply idempotency mechanisms with annotations and templates to ensure reliable microservice operations.
The article explains why distributed systems must stay alive despite upstream or downstream failures, emphasizing rate limiting and circuit breaking as essential practices to prevent fault propagation and ensure service reliability, and it invites developers to assess their own safeguards.
Drawing on Google SRE principles, Bilibili’s technical director outlines a systematic, cloud‑native framework for high‑quality service architecture during traffic peaks, covering frontend and internal load balancing, distributed rate limiting, controlled retries, fail‑fast timeouts, and comprehensive failure‑mitigation strategies.
This article outlines key security mechanisms for public APIs—including data encryption, signing, timestamp validation, AppId authentication, rate limiting, blacklist handling, and data validation—and provides practical Java code examples for each technique.
This article explains the principles of token‑bucket and leaky‑bucket rate‑limiting algorithms, shows how Nginx implements them with the limit_req and limit_conn modules, and provides detailed configuration examples—including burst, nodelay, and custom status codes—to control request rates and concurrent connections.
This article explains Sentinel's role in microservice rate limiting, details a Docker-specific bug where CPU utilization is incorrectly reported, clarifies the difference between CPU load and utilization, and outlines the code fix and its remaining limitations.
This article examines the challenges of building a flash‑sale (秒杀) system—such as overselling, massive concurrent requests, URL exposure, and database overload—and presents a comprehensive backend architecture that leverages Redis clustering, dynamic URLs, Nginx load balancing, rate‑limiting, asynchronous order processing, and service degradation techniques to achieve robust, scalable performance.
This article introduces the background and necessity of rate limiting, explains the leaky bucket and token bucket algorithms with visual diagrams, and provides a comprehensive Java implementation using Guava's RateLimiter, custom annotations, AOP interception, and controller integration to protect high‑traffic applications.
This article explains how to implement token‑bucket rate limiting with Guava's RateLimiter, compare it to leaky‑bucket algorithms, and combine it with optimistic and pessimistic locking techniques to prevent overselling in high‑concurrency flash‑sale applications.
To keep systems highly available under sudden traffic spikes, developers employ three core strategies—rate limiting, circuit breaking, and service degradation—each controlling request flow, isolating failures, and gracefully reducing functionality to maintain stability, with practical examples and algorithmic approaches explained.
The article explains Envoy's circuit‑breaking mechanisms, global rate‑limiting integration, and TLS support, detailing configuration options, limits, and example YAML snippets for deploying secure and resilient service‑mesh proxies.
Youzan’s Task Scheduling Platform (TSP) unifies its legacy cron and timeout systems into a modular, extensible framework that supports both timed and delayed tasks, offering per‑task rate limiting, sharding, isolation, retry policies, and a rich SDK, while its roadmap adds end‑to‑end monitoring, workflow orchestration, domain‑level clustering, transactional messaging, and dynamic task registration.
This article explains how caching, degradation, and various rate‑limiting strategies—including semaphore‑based concurrency control, token‑bucket algorithms, Guava RateLimiter, custom annotations, Redis interceptors, and Nginx modules—protect high‑concurrency distributed systems, with practical Java code samples and configuration snippets.
The article explains the principles of traffic shaping through leaky‑bucket and token‑bucket algorithms, details how Google Guava's RateLimiter implements token‑bucket rate limiting, and provides Java code examples illustrating token generation, acquisition, and practical usage in high‑concurrency backend systems.
This article presents Bilibili's exploration of microservice governance, detailing the challenges of service splitting and large‑scale management, the design and evolution of its Go‑based Discovery service discovery framework, advanced load‑balancing algorithms, adaptive rate‑limiting, circuit‑breaking strategies, and future directions for resilient backend systems.
This article explains the three essential tools for building high‑concurrency systems—caching, service degradation, and rate limiting—detailing their purposes, common algorithms such as counter, leaky bucket and token bucket, and providing concrete Java and Nginx configuration examples.
This article explores common pitfalls and best‑practice design patterns for SMS verification, covering cross‑origin issues, rate‑limiting, captcha integration, IP/Cookie restrictions, and alert mechanisms to balance security and user experience across web and mobile platforms.
This article outlines a systematic approach to dependency governance in high‑traffic backend services, covering service classification, rate limiting, Dubbo, HTTP, database, and message‑queue management to enhance availability, reduce failure impact, and improve overall system stability.
This article examines the key challenges of flash‑sale (秒杀) systems—overselling, massive concurrency, URL exposure, and database coupling—and presents a complete backend design featuring dedicated databases, dynamic URLs, static pages, Redis clustering, Nginx proxy, rate‑limiting, token‑bucket control, asynchronous order queues, and service degradation strategies.
This article examines the challenges of designing a robust flash‑sale backend—preventing oversell, handling massive concurrent requests, securing URLs, isolating databases, and applying techniques such as Redis clustering, Nginx load balancing, rate limiting, asynchronous processing, and service degradation—to enable stable, low‑latency sales spikes.
This article demonstrates how to create a custom @AccessLimit annotation, implement a Spring MVC interceptor that uses Redis to count requests, register the interceptor in a Spring Boot application, and apply the annotation to controller methods to achieve rate‑limiting functionality.
The article explains how Youku’s engineers mitigate extreme traffic spikes during large‑scale events by applying three core techniques—staggering requests across time, enforcing client‑side rate limits, and cutting peaks through minimum intervals, probabilistic sending and fairness algorithms—to lower maximum QPS, improve stability, and preserve user experience.
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.
