This article explains why a custom business rate‑limiting component is needed, outlines the required rules, chooses Redis + Lua for counting, and provides complete Kotlin/Spring code—including a Detect annotation, Lua scripts, and usage examples—to enforce daily, hourly, and combined limits with real‑time adjustments.
This article explains the most frequent API security weaknesses—including information disclosure, broken object‑level and function‑level authorization, authentication bypass, over‑exposure of data, missing rate limits, mass‑assignment, misconfiguration, injection, asset mismanagement, and business‑logic flaws—providing detection techniques and illustrative code examples.
This article provides an in‑depth tutorial on Alibaba Sentinel, covering its core concepts, installation of the dashboard, version selection, flow‑control configuration, degrade rules, hotspot parameter limiting, system adaptive protection, custom block handlers, blacklist/whitelist settings, rule persistence with Nacos, push‑mode synchronization, and cluster flow‑control setup for microservice applications.
This article outlines essential API security mechanisms—including encryption, signing, timestamp validation, AppId authentication, rate limiting, blacklist handling, and data validation—and provides practical Java implementation examples for each technique.
The article describes a data application platform that enables business users to configure custom data selection rules for opportunities, create scheduled tasks, perform large‑scale data comparison, handle task dispatch with Redis queues, and implement rate‑limiting using sliding windows to ensure reliable processing.
This article outlines a comprehensive backend architecture for flash‑sale (秒杀) systems, covering traffic‑spike handling, rate‑limiting, Nginx static‑resource separation, Redis locking and caching, message‑queue throttling, database sharding, asynchronous order processing, and captcha‑based anti‑bot measures.
The article explains how Redis cache expiration can cause a cache avalanche, describes typical scenarios, and presents mitigation techniques such as semaphore-based rate limiting and fault tolerance, accompanied by a Java service example demonstrating lock, semaphore, and cache handling.
This article explains the architecture of a flash‑sale system from seven dimensions, covering characteristics, overall design, peak‑limiting, security, page optimization, asynchronous processing, hotspot isolation, Nginx configuration, Redis clustering, message‑queue throttling, database design, and captcha mechanisms to handle massive concurrent requests.
This article explains three core rate‑limiting algorithms—Leaky Bucket, Token Bucket, and Sliding Window—detailing their principles, suitable scenarios, and provides complete Go implementations to help developers choose and integrate the right strategy for handling traffic spikes and protecting backend resources.
This article explains Java rate‑limiting fundamentals, covering time‑window and resource dimensions, common algorithms such as token bucket, leaky bucket and sliding window, and practical solutions using Guava, Nginx, Redis, Sentinel, and Tomcat for both single‑node and distributed environments.
This article explains the fundamental concepts of rate limiting, including time and resource dimensions, various rule types such as QPS, connection count, bandwidth, black/white lists, and distributed considerations, then details common algorithms like token bucket, leaky bucket, sliding window, and practical implementations using Nginx, Guava, Redis, and Sentinel.
This article explains the fundamental concepts of rate limiting, compares popular algorithms such as token bucket, leaky bucket, and sliding window, and reviews practical implementation methods including Nginx, middleware, Redis, Guava, and Tomcat configurations for both single‑machine and distributed environments.
This article provides a thorough overview of rate limiting, covering its basic concepts, common algorithms such as token bucket, leaky bucket and sliding window, and practical implementation methods across Nginx, Tomcat, Guava, Redis, Sentinel and other middleware for both single‑machine and distributed systems.
This article reviews the fundamentals of token‑bucket rate limiters, identifies precision, cascading compensation, and TCP‑loss sensitivity issues, and details two major improvements—port‑loan backpressure and the Carousel algorithm—while outlining future directions for more reliable network traffic shaping.
This article provides a comprehensive overview of rate limiting, covering basic concepts, common algorithms such as token bucket, leaky bucket, and sliding window, and practical implementation methods across Nginx, Tomcat, Guava, Redis, and Sentinel for distributed backend systems.
This article explains three Redis‑based rate‑limiting techniques—using SETNX, sorted sets, and a token‑bucket with lists—providing code examples, discussing their advantages and drawbacks, and showing how to integrate them into Java Spring applications to protect high‑concurrency services.
This comprehensive guide explores core distributed system concepts—including the CAP theorem and its trade‑offs, BASE consistency, various distributed lock strategies, multiple transaction patterns such as 2PC, 3PC, TCC and Seata, as well as consensus algorithms Paxos and Raft, while also covering idempotency and rate‑limiting techniques.
The article outlines essential high‑availability techniques for internet‑scale systems, covering availability metrics, microservice modularization, database redundancy, load balancing, rate limiting, circuit breaking, isolation, retry strategies, rollback plans, stress testing, monitoring, and on‑call procedures.
This article explains how to configure and implement request throttling in Django Rest Framework, walks through the relevant source code in throttling.py and views.py, demonstrates testing the limit, and shows how to trigger custom DingTalk alerts when the limit is exceeded.
This article explains the principles and practical implementations of rate limiting in backend systems, covering real‑world scenarios, strategies such as circuit breaking, service degradation, delayed and privileged handling, common algorithms like counter, leaky‑bucket and token‑bucket, and code examples using Guava and Nginx + Lua.
This article explains how rate limiting protects system availability by controlling traffic flow, introduces common patterns such as circuit breaking, service degradation, delay and privilege handling, compares cache, degradation, and rate limiting, and details popular algorithms and practical code implementations for both single‑node and distributed environments.
This article outlines fifteen practical techniques—including horizontal scaling, microservice decomposition, database sharding, connection pooling, caching, CDN, message queues, Elasticsearch, circuit breaking, rate limiting, and load testing—to help engineers build robust, high‑concurrency systems that can handle massive traffic spikes.
When a product suddenly surges in demand, this guide explains how to safeguard e‑commerce APIs using rate‑limiting algorithms (leaky bucket, token bucket, sliding window), Nginx and Java semaphore controls, distributed throttling with message queues, service degradation strategies, and caching techniques to maintain stability.
This article presents a detailed case study of RocketMQ's fast‑failure mechanism, explains the root causes of a massive message‑write surge, analyzes broker thread models, and proposes topic‑level rate‑limiting solutions with concrete implementation and code examples.
This article explains a comprehensive backend user login flow, covering token generation, expiration policies, request validation, logout handling, anonymous access strategies, rate‑limiting with authorized tokens, path‑regex filtering, blacklist management, and includes a Spring‑Redis implementation example.
This article explains why a custom business rate‑limiting (risk‑control) component is needed for AI‑driven services, compares open‑source solutions, and provides a complete implementation using Redis+Lua scripts, Kotlin code, and Spring AOP annotations, including testing and deployment tips.
This article provides a comprehensive overview of software architecture, explaining its definition, discussing distributed systems, clustering, caching, messaging, multithreading, rate limiting, security vulnerabilities, and common design mistakes, while emphasizing the need for practical experience and continuous learning.
The article explains how to protect high‑traffic e‑commerce APIs by using caching, rate‑limiting (leaky‑bucket, token‑bucket, sliding‑window), local semaphore controls, distributed message‑queue buffering, service degradation tactics, and recovery plans to ensure stability during sudden traffic spikes.
This article explains how e‑commerce systems handle sudden traffic spikes by applying caching, various rate‑limiting algorithms (leaky bucket, token bucket, sliding window), Nginx ingress controls, Java Semaphore concurrency limits, distributed queue buffering, service degradation tactics, and cache‑consistency techniques for high‑availability backend services.
This article explains how to design and operate resilient microservice systems by using patterns such as graceful degradation, change management, health checks, self‑healing, failover caching, retry logic, rate limiting, circuit breakers, and testing failures to minimize downtime and improve reliability.
This guide explains the fundamental dimensions of rate limiting, compares token‑bucket, leaky‑bucket, and sliding‑window algorithms, and details practical implementations using Guava, Nginx, Redis, and Sentinel for both single‑node and distributed systems.
The article explains how to handle sudden traffic spikes in e‑commerce systems by applying rate‑limiting algorithms, Nginx and Java semaphore controls, distributed queue buffering, service degradation tactics, and multi‑layer caching techniques to maintain high availability and data consistency.
This article discusses how to handle sudden traffic spikes in e‑commerce APIs by employing caching, rate‑limiting (leaky bucket, token bucket, sliding window), Nginx and Java Semaphore limits, distributed queue buffering, service degradation, and cache‑consistency techniques to ensure system stability.
This article introduces the concept of rate limiting, explains three core algorithms—fixed window, sliding window, and leaky bucket—along with the token bucket approach, provides Java code examples for each, discusses their principles, advantages, and pitfalls, and outlines practical implementation considerations.
This article outlines essential practices for building secure, reliable API interfaces—including request signing, data encryption, IP whitelisting, rate limiting, parameter validation, unified responses, exception handling, logging, idempotency, payload limits, performance testing, asynchronous processing, data masking, and comprehensive documentation—to help developers meet safety, stability, and maintainability requirements.
This article explains the fundamental concepts of rate limiting, reviews popular algorithms such as token bucket, leaky bucket, and sliding window, and details practical implementation methods across single‑machine and distributed environments using tools like Guava, Nginx, Redis, and Sentinel.
This article explains the fundamentals of rate limiting, describes widely used algorithms such as token bucket, leaky bucket, and sliding window, and details practical implementation methods ranging from single‑machine tools like Guava and Tomcat to distributed solutions using Nginx, Redis, and Sentinel.
The article explains how to protect a suddenly hot product API in an e‑commerce system by applying caching, various rate‑limiting algorithms, service degradation techniques, and distributed caching patterns, providing concrete Java code and architectural recommendations for backend developers.
This guide explains how to design, implement, and configure rate‑limiting solutions for high‑traffic flash‑sale scenarios, covering architecture goals, algorithm choices, single‑node vs distributed approaches, cloud‑native gateway settings, scaling with Kubernetes, and practical Go code examples.
This article explains the fundamental concepts of rate limiting, compares common algorithms such as token bucket, leaky bucket and sliding window, and details practical implementations using Nginx, Tomcat, Redis, Guava, and Sentinel for both single‑node and distributed backend systems.
This comprehensive guide explains what Nginx is, its advantages, deployment scenarios, request processing, high‑concurrency mechanisms, proxy types, directory layout, configuration directives, cookie vs session, threading model, load‑balancing algorithms, rate limiting, health checks, compression, and practical examples for building robust, scalable web services.
This article explores how sudden traffic surges can cause service avalanches and presents cloud‑native scaling, various rate‑limiting algorithms (fixed window, sliding window, token bucket, leaky bucket) and practical fallback techniques to protect backend systems and ensure graceful degradation.
This article explains how to build highly available microservice systems by addressing the risks of distributed architectures, employing graceful degradation, change management, health checks, self‑healing, failover caching, retry and rate‑limiting strategies, bulkhead and circuit‑breaker patterns, and continuous failure testing.
This article explains how to use the iptables hashlimit module to create a stateful rate‑limiting chain, details the required commands, clarifies the meaning of the --hashlimit‑upto and --hashlimit‑burst parameters, and provides an example illustrating credit‑based packet acceptance.
This article walks through practical trade‑offs in designing a Go microservice framework, covering protocol choices, data transport, network handling, serialization methods, service registry options, routing algorithms, and rate‑limiting strategies, while highlighting real‑world implementation details and pitfalls.
This article explains how to configure distributed rate limiting in Spring Cloud Gateway by adding the reactive Redis dependency, setting up Redis connection properties, defining a RequestRateLimiter filter with token‑bucket parameters, implementing a custom KeyResolver, and demonstrating the behavior with HTTP 429 and 403 responses.
This article explains how to build highly available microservice systems by applying fault‑tolerance patterns such as graceful degradation, health checks, self‑healing, failover caches, retries, rate limiting, bulkhead isolation, circuit breakers, and systematic failure testing, while also covering change‑management and deployment strategies.
The article examines B‑Station’s July 2021 outage, explains the concept and quantitative metrics of high availability, and outlines practical techniques such as rate limiting, isolation, failover, timeout control, circuit breaking, degradation, and multi‑region deployment to achieve resilient systems.
This article shares practical insights on API design, covering fundamental concepts, rate‑limiting strategies, versioning practices, security considerations, and tips for cross‑team API integration, all illustrated with real‑world examples and concise code snippets.
This article provides a detailed overview of Nginx, covering its lightweight architecture, high‑concurrency event‑driven model, common use cases such as static file serving, reverse proxy and load balancing, step‑by‑step configuration examples, and advanced topics like rate limiting, health checks, gzip compression, and high‑availability deployment.
This article explores the avalanche effect in distributed systems, outlines common failure scenarios, and presents effective mitigation tactics such as hardware redundancy, dynamic scaling, and isolation, then delves into popular rate‑limiting algorithms—including count, fixed and sliding windows, leaky bucket, and token bucket—followed by an overview of leading circuit‑breaker solutions like Sentinel and Hystrix.
This article outlines a comprehensive, cloud‑native rate‑limiting solution using Kong gateway, covering background challenges, design considerations, multi‑layer architecture, plugin development, CI/CD workflow, deployment strategies, and operational best practices to achieve low cost, high efficiency, and high quality across diverse projects.
This article shares practical insights on API design, covering fundamentals, rate limiting, version management, security considerations, and inter‑team integration, illustrated with code snippets and diagrams to help developers build clear, standardized, and robust backend interfaces.
Rate limiting safeguards distributed systems by controlling request rates through algorithms such as leaky bucket, token bucket, fixed and sliding windows, and back pressure, while client‑side tactics like exponential backoff, jitter, and careful retries, and requires atomic distributed storage solutions (e.g., Redis+Lua) to avoid race conditions.
From kickoff meetings and traffic forecasting to load‑testing strategies, rate‑limiting designs, emergency runbooks, and post‑event retrospectives, this guide walks engineers through the complete technical workflow required to ensure a Double‑11‑scale e‑commerce promotion runs smoothly and safely.
The article explains how to build highly available microservice systems by isolating failures, applying graceful degradation, change‑management, health checks, self‑healing, fallback caches, circuit breakers, retry policies, rate limiting and testing strategies, while acknowledging the cost and operational complexity involved.
This article presents Ctrip's backend engineering practices for the Direct Connection Platform, detailing how a sandbox testing environment, automated acceptance, rate‑limiting, and circuit‑breaking mechanisms were introduced to boost supplier onboarding speed and enhance overall system stability.
This article outlines ten practical methods to secure API data, covering encryption of transmission, digital signing, token authentication, timestamp and nonce replay protection, rate limiting, blacklist/whitelist controls, data masking, and parameter validation, with detailed explanations and implementation steps.
This article explains the sliding window rate‑limiting algorithm, its advantages over fixed windows, and provides a detailed step‑by‑step implementation using Sentinel’s data structures, including window partitioning, bucket management, concurrency handling, and calculation of request counts within a moving time interval.
This tutorial explains what gateway rate limiting is, shows how to integrate Alibaba Sentinel with Spring Cloud Gateway, details the configuration of Sentinel dashboard, gateway flow rules, API group management, implementation principles, and how to customize block responses for robust microservice protection.
Designing a flash‑sale (秒杀) system for massive traffic requires handling instant spikes, page staticization, CDN acceleration, read‑heavy caching, preventing cache‑penetration and cache‑breakdown, managing pre‑deducted inventory, employing atomic database or Redis operations, leveraging Lua scripts, distributed locks, MQ async processing, and robust rate‑limiting strategies.
This tutorial demonstrates how to install Aeraki Mesh, use its MetaRouter CRD to configure local rate limiting for MetaProtocol services such as Dubbo and Thrift, apply per‑service and conditional limits, and view the generated sidecar proxy configuration in a Kubernetes environment.
Monet is a custom API gateway built on Spring Cloud Gateway that consolidates multiple platform endpoints into a single domain, providing unified routing, API management via Zookeeper, authentication and authorization, dynamic rate‑limiting with Sentinel, and extensible monitoring, addressing scalability and operational challenges of the Linglong platform.
This guide explains how to integrate Alibaba Cloud MSE's Sentinel-based rate limiting and degradation features into Go microservices, covering the three-step model, SDK setup, resource definition, and configuration of flow, isolation, circuit‑breaker, and hotspot rules.
This article describes a backend solution that uses Redis for configurable rate‑limiting and interface switches, and Hystrix for circuit‑breaking, detailing the requirements, AOP‑based approach, and full Java code implementation for managing non‑core query overloads.
This article explains the concept of interface idempotency, demonstrates how to achieve idempotent update operations using version control and token mechanisms, and provides a comprehensive guide to distributed rate limiting—including time‑window and resource‑based dimensions, token‑bucket and leaky‑bucket algorithms, and practical implementations with Guava RateLimiter, Nginx, and Redis‑Lua scripts.
This article outlines ten practical methods for protecting API data, covering transmission encryption, digital signatures, token authentication, timestamp and nonce mechanisms, rate limiting, blacklist/whitelist controls, data masking, and parameter validation to ensure comprehensive interface security.
This article provides a step‑by‑step guide to installing Nginx, explains core directives such as listen, server_name, location, and proxy_pass, and covers advanced topics including rate limiting, load balancing methods, keepalive connections, static resource handling, CORS, and anti‑hotlinking configurations.
Spring Cloud Tencent, open‑sourced on June 14, offers a comprehensive, Spring‑compatible microservice suite built on Tencent's Polaris platform, providing service discovery, configuration, routing, rate limiting, and circuit breaking, while explaining its motivations, core capabilities, and future roadmap for Java developers.
This article explores the importance of rate limiting in distributed micro‑service architectures, explains four core algorithms—fixed window, sliding window, leaky bucket, and token bucket—and details a practical, Redis‑backed multi‑layer throttling solution for a voice‑bot platform, including trade‑offs and implementation tips.
This article introduces Spring Cloud Tencent, Tencent’s open‑source microservice solution built on Polaris, and walks through installing Polaris, adding service discovery, configuration management, rate limiting, routing, and circuit‑breaker features with detailed Maven dependencies and YAML configurations, plus code examples for each step.
This article provides a step‑by‑step tutorial on installing Nginx, explains core directives such as listen, server_name, and location, and covers advanced topics like rate limiting, various load‑balancing algorithms, reverse proxy setup, keepalive tuning, gzip compression, CORS, anti‑leech, and integration with LVS and keepalived for high‑availability deployments.
This guide walks through installing Nginx, explains essential directives such as listen, server_name, location and proxy_pass, demonstrates reverse‑proxy setups, details rate‑limiting and connection‑limiting modules, explores various upstream load‑balancing strategies, and covers performance‑tuning options like keepalive, gzip, and CORS handling.
This article explains various rate‑limiting techniques—including counter, sliding window, leaky bucket, token bucket, and Redis‑Lua distributed limiting—detailing their principles, implementation logic, advantages, drawbacks, and practical code examples for high‑concurrency e‑commerce systems in.
This article explains how to integrate Alibaba Sentinel with Spring Cloud Gateway to configure rate‑limiting and circuit‑breaker rules, covering Maven dependencies, Java configuration classes, Sentinel rule definitions, and optional console‑based management for robust backend API protection.
This article explains three Redis‑based rate‑limiting methods—using SETNX for simple counters, leveraging ZSET for a sliding‑window algorithm, and implementing a token‑bucket scheme with LISTs—complete with Java code examples and discussion of their advantages and drawbacks.
This article explains what rate limiting is, why it is needed, various strategies and algorithms such as leaky bucket, token bucket, fixed and sliding windows, and discusses challenges like inconsistency and race conditions in distributed systems, as well as different throttling types.
This article demonstrates how to integrate Alibaba Sentinel into Spring Cloud Gateway for traffic rate limiting and circuit breaking, covering Maven dependencies, configuration of SentinelGatewayFilter and exception handler, rule definitions via YAML or Java code, and custom API grouping, providing a complete backend gateway protection guide.
This article explains the importance of interface idempotency and presents a comprehensive guide to distributed rate limiting, covering key dimensions, token‑bucket and leaky‑bucket algorithms, and concrete implementations using Guava RateLimiter, Nginx, and Redis‑Lua with Java code examples.
This article explains how to build highly available microservice systems by defining clear service boundaries, employing graceful degradation, change‑management strategies, health checks, self‑healing, cache failover, retry logic, rate limiting, bulkheads, circuit breakers, and testing techniques to mitigate failures in distributed environments.
This article explores three core rate‑limiting algorithms—Leaky Bucket, Token Bucket, and Sliding Window—detailing their principles, suitable scenarios, and provides complete Go implementations, enabling developers to choose and integrate the appropriate strategy for controlling request traffic without external dependencies.
This guide walks through creating a SpringBoot service, adding Sentinel dependencies, writing a high‑traffic test endpoint, explaining Sentinel’s entry/exit logic, and configuring the Sentinel dashboard to enforce a single‑node QPS limit of 20, complete with code samples and screenshots.
This article presents a comprehensive design of the Cleaner anti‑crawler system, detailing its background, current challenges, related research, system architecture—including a Flink‑based data processing center, a strategy‑driven ban center, and a lightweight ban store—and evaluates its effectiveness in real‑time bot mitigation.
The article discusses how to achieve interface idempotency using unique business identifiers or token mechanisms, then explores distributed rate limiting by outlining its dimensions, common algorithms such as token bucket and leaky bucket, and provides concrete implementation examples with Guava RateLimiter, Nginx, and Redis‑Lua scripts.
This comprehensive guide explains what Nginx is, its advantages, typical use cases, request handling, high‑concurrency architecture, proxy types, directory layout, key configuration directives, virtual host setup, load‑balancing algorithms, rate‑limiting, static‑dynamic separation, gzip compression, health checks, and practical code examples for production deployments.
This guide explains how to use Alibaba Cloud's Application High Availability Service (AHAS) to monitor QPS, define granular rate‑limiting rules, prevent abuse, isolate upstream failures, and protect both HTTP and non‑HTTP workloads in microservice architectures.
The article explains why distributed systems need fault‑tolerance mechanisms such as rate limiting, circuit breaking, and service degradation, describes common metrics (TPS, HPS, QPS), outlines several limiting algorithms (counter, sliding window, leaky bucket, token bucket, distributed and Hystrix‑based), and discusses circuit‑breaker states, considerations, and practical Hystrix usage.
To deliver a flawless live broadcast of the 2021 League of Legends S11 World Championship to over 100 million viewers, Bilibili mobilized hundreds of engineers for four months, establishing strict standards, modeling dozens of user scenarios, estimating traffic, conducting layered stress and chaos tests, implementing automated and manual degradation, detailed SOPs, rate‑limiting safeguards, and on‑site monitoring, which together ensured system stability throughout the event.
This article explains how to achieve interface idempotency using unique business IDs or token mechanisms and presents comprehensive distributed rate‑limiting techniques—including token‑bucket and leaky‑bucket algorithms, Nginx directives, Guava RateLimiter, and Redis‑Lua scripts—along with practical Spring Boot code examples.
This article details the architecture, S3‑compatible functionality, rate‑ and bandwidth‑limiting mechanisms, dependency degradation strategies, multi‑cloud switching, monitoring, and future roadmap of the Beike storage gateway, illustrating how it achieves high availability and scalability for billions of objects.
This article explains three Redis‑based rate‑limiting techniques—setnx with expiration, a sliding‑window implementation using sorted sets, and a token‑bucket algorithm with lists—complete with Java code examples and discussion of their advantages and limitations for high‑concurrency backend services.
This article explains why client‑side circuit breaking is essential, describes common state‑machine and Google SRE breaker strategies, provides practical pseudocode, and discusses mitigation methods such as Gutter mode, jittered exponential backoff, and graceful degradation to protect system stability.
This article provides a comprehensive overview of Nginx, covering its definition, advantages, typical use cases, request processing flow, high‑concurrency design, proxy types, directory layout, key configuration directives, load‑balancing algorithms, rate‑limiting mechanisms, security features, and practical deployment tips for modern web services.
The article explains why service rate limiting is essential for high‑concurrency systems, compares it with service degradation, describes common algorithms such as counter, leaky bucket and token bucket, and shows how to implement distributed rate limiting using Redis.
This article explains why service rate limiting is essential for high‑concurrency systems, compares it with service degradation, introduces common limiting algorithms such as counter, leaky‑bucket and token‑bucket, and shows how to implement distributed rate limiting using Redis for real‑world backend services.
The article details Vivo’s push notification platform, describing its evolution from cloud‑based beginnings to a self‑built, three‑region architecture that supports over 1 million concurrent connections, billions of daily messages, and incorporates optimizations such as adaptive heartbeats, advanced load‑balancing, distributed caching, multi‑layer rate limiting, circuit‑breaker mechanisms, and comprehensive content security.
This article demonstrates how to build a token‑bucket based rate‑limiting solution in a Spring Boot application using a custom annotation, an interceptor, a bucket utility class, scheduled token generation, and global AOP exception handling to control access to APIs.
The article explains the concepts, problem scenarios, resource granularity, rule definition, sliding‑window calculation, and implementation flow of rate limiting and circuit breaking in microservice architectures and API gateways, providing practical guidance for building robust backend services.
This article presents Bilibili’s high‑availability design, covering load‑balancing decisions, subset selection, multi‑cluster deployment, adaptive rate limiting, retry policies, timeout propagation, and chain‑failure mitigation, all illustrated with diagrams and practical SRE insights.
This article explains how to protect a Spring Boot API from traffic spikes by applying common rate‑limiting algorithms, configuring Redis for distributed throttling, writing Lua scripts for atomic counters, and integrating a custom @Limit annotation with AOP to enforce limits across a cluster.
