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 the benefits, costs, and various update, penetration, bottom‑hole, avalanche, and hot‑key rebuilding strategies for caches in high‑concurrency distributed systems, offering practical guidance on choosing eviction algorithms, using Bloom filters, and improving overall system performance and reliability.
This article reviews the historical evolution of system architecture—from single‑application monoliths through vertical splitting, distributed services, SOA, and finally microservices—explaining their advantages, drawbacks, design principles, remote‑call mechanisms, automation, monitoring, and the relationship between SOA and microservices.
This article explains the design goals, exactly‑once semantics, Java transaction API, internal components such as the coordinator and transaction log, data‑flow interactions, performance considerations, and best‑practice tips for using Apache Kafka transactions in stream‑processing applications.
This article traces the evolution of system architecture—from simple monolithic applications through vertical splitting, distributed services, SOA, and finally microservices—explaining their advantages, drawbacks, design principles, remote‑call mechanisms, and the operational practices needed for modern backend development.
This article explains the principles of high availability in internet systems, covering redundancy, automatic failover, availability metrics, and detailed HA designs for each architectural layer such as load balancers, microservices, middleware, and databases.
This article explains the concept and classifications of edge computing, compares it with central cloud, outlines deployment models, discusses the challenges of running Kubernetes at the edge, and presents OpenYurt as an open‑source platform that extends native Kubernetes capabilities to distributed edge environments.
This article explains the concept of API idempotency, why duplicate requests occur in distributed systems, and presents a comprehensive set of server‑side and client‑side techniques—including token mechanisms, PRG pattern, unique indexes, optimistic locking, distributed locks, and idempotent state machines—to guarantee that repeated calls produce only a single effect.
This guide compares Kafka, Pulsar, RocketMQ, RabbitMQ, and NSQ across architecture, consumption models, delay and dead‑letter support, persistence, ordering, multi‑tenant isolation, and performance, recommending Kafka for high‑throughput log processing, Pulsar for transactional delayed messaging and disaster recovery, and NSQ for Go‑centric custom solutions.
The article discusses the importance of caching in high‑concurrency distributed systems, outlining its benefits and costs, various update policies such as LRU/LFU/FIFO, expiration, active refresh, and advanced optimizations like penetration protection, hole mitigation, avalanche prevention, and hot‑key rebuild techniques.
The article explains why caching is essential in high‑concurrency distributed systems, analyzes its benefits and costs, and then details various cache update, penetration, no‑hole, avalanche, and hot‑key rebuilding techniques, offering practical guidance for backend engineers.
Cloud‑native databases have shifted from simple containerized services to comprehensive, elastic solutions that integrate multiple engines and distributed technologies, offering horizontal scaling, fine‑grained billing, and high‑performance architectures, while enterprises must evaluate vendor perspectives and choose between public‑cloud services or private‑cloud implementations.
This article explains the key concepts, simple design, practical steps, and multiple solution patterns—including front‑end scaling, cache‑based concurrency control, and database handling—used to build a reliable high‑traffic flash‑sale system.
This article walks through the design and practical implementation of a high‑throughput red‑envelope service capable of handling 10 billion requests, covering target load calculations, hardware and software choices, client‑server coordination, performance testing, monitoring, and key takeaways for building scalable backend systems.
This article presents thirty concise software architecture principles—ranging from KISS and YAGNI to distributed system consistency and user‑experience guidelines—offered by architect Srinath to help teams adopt a gardener‑like, iterative, and pragmatic approach to building scalable, maintainable, and user‑centric systems.
This article compiles sixteen categories of learning materials, detailing the number of books for each topic—including Java, databases, caching, messaging, MongoDB, search, Zookeeper, networking, design patterns, data structures and algorithms, distributed systems, cloud‑native microservices, containers, service mesh, DevOps, and operating systems—along with a Baidu link and extraction code for access.
This article explains how to guarantee that messages are never lost when using MQ middleware such as Kafka, RabbitMQ, or RocketMQ, outlines the key interview points, and provides practical design patterns, detection mechanisms, idempotency, and scaling strategies for reliable message delivery.
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 examines the role of caching in high‑concurrency distributed systems, outlining its performance benefits and associated costs, and detailing various cache‑update strategies—including LRU/LFU/FIFO, expiration, active refresh, Bloom‑filter protection, and solutions for penetration, avalanche, hot‑key rebuild, and the “no‑bottom‑hole” problem.
This article explains how Kafka 2.8 removes its dependency on Zookeeper, describes the roles of brokers, topics, partitions, and the controller in the Zookeeper‑based architecture, and outlines the KIP‑500 upgrade that replaces Zookeeper with a quorum‑based KRaft controller to improve scalability and operational simplicity.
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.
This article explains how to design microservices using domain‑driven design principles, covering bounded contexts, aggregates, context mapping, event storming, service communication patterns, and the BFF approach to achieve loosely‑coupled, highly‑cohesive backend systems.
This article explains the core concepts of SchedulerX, a cloud‑native distributed task scheduling platform, covering its resource model, visual control features, distributed batch processing capabilities, practical use‑case scenarios, and step‑by‑step integration with a SpringBoot application.
This article details the design, goals, hardware and software choices, implementation techniques, performance testing phases, and analysis of a prototype backend capable of handling one million concurrent connections and up to 60 k QPS to simulate the massive load of ten billion WeChat red‑envelope requests.
This article presents a comprehensive guide to building high‑concurrency systems for e‑commerce, covering server architecture, load balancing, database clustering, caching strategies, concurrency testing tools, message‑queue based solutions, first‑level caches, static data handling, layered and distributed designs, as well as redundancy and automation practices.
This article surveys common delayed‑message solutions in distributed messaging, compares implementations based on external storage (MySQL, RocksDB, Redis) and built‑in MQ features (RocketMQ, Pulsar, QMQ), and analyzes their advantages, disadvantages, and design considerations.
This article details a practical backend design and stress‑test that simulates 1 million concurrent users handling 100 billion WeChat red‑packet requests, covering load calculations, Go‑based implementation, QPS targets, monitoring, and performance analysis.
2021 marked a pivotal year for databases, with rapid cloud adoption, the rise of HTAP architectures, the decline of Hadoop, and open‑source becoming mainstream, while PingCAP’s TiDB exemplifies DBaaS implementation through cloud‑native design, cost optimization, security, automation, and multi‑region compliance.
Covering essential high‑availability techniques, the article examines disaster‑recovery architectures from same‑city dual‑center to cross‑country active‑passive deployments, compares five patterns, details three multi‑active models, outlines required traffic‑scheduling, replication, and database layers, and provides design methodology, practical safeguards, and key HA metrics.
This article examines common delayed (scheduled) message implementations in distributed systems, comparing external storage approaches using databases, RocksDB, and Redis, as well as built-in solutions in open-source MQs like RocketMQ, Pulsar, and QMQ, and discusses their advantages, drawbacks, and design considerations.
The article explains the CAP theorem’s origin, formal proof, detailed definitions of consistency, availability and partition tolerance, its impact on relational and NoSQL databases, various consistency models, and practical trade‑offs for designing distributed data systems.
This article explains the business scenarios that require delayed processing, compares several existing delayed‑queue solutions, and details a Redis‑backed delayed‑queue design—including its data structures, message format, multi‑node deployment, and runtime workflow—providing practical guidance for building scalable backend systems.
This article traces the history of data management from early manual and file‑system storage through relational models to modern distributed databases, covering key concepts like CAP theorem, distributed transactions, HTAP, and cloud‑native deployment trends.
This article explains the origin of the CAP theorem, its formal definitions of consistency, availability, and partition tolerance, examines how it applies to relational and NoSQL databases, discusses trade‑offs and practical strategies for balancing these properties in distributed system design.
The article reviews major cache‑consistency strategies—Cache‑Aside, Read‑Through, Write‑Through, and Write‑Behind—detailing their performance and consistency trade‑offs, discusses compensation techniques such as delayed double‑delete and MySQL binlog‑based incremental parsing (DTS) for reliable deletion and HA, and advises selecting the appropriate approach based on specific business requirements.
This article explains service governance concepts, the challenges of managing thousands of micro‑services, introduces etcd and its Raft‑based consistency model, details BoltDB storage internals, and describes Baidu's large‑scale Tianlu platform with its high‑availability, performance, scalability, and operational metrics.
This article presents JD's comprehensive big‑data security framework and distributed permission system, detailing the overall planning of the security center, data lifecycle protection strategies, core modules such as subjects, resources, policy language, and high‑performance access control, and how they address national compliance, business scalability, and technical challenges.
This article surveys delayed‑queue implementations, comparing Kafka, RocketMQ, and Redis (Redisson) designs, outlining their architectures, pros and cons, integration details, timing‑wheel mechanisms, and practical considerations for building a reliable distributed delayed‑queue service.
This article explains the Bully election algorithm used by Elasticsearch versions prior to 7.x, describes phenomena such as false death and split‑brain, and provides a detailed walkthrough of the master‑election source code including key Java methods and configuration parameters.
This article explains the fundamentals of Remote Procedure Call (RPC), detailing the end‑to‑end workflow—from interface definition and dynamic proxy generation on the client, through service registration in ZooKeeper, to server‑side handling with Spring and Netty—accompanied by complete Java code snippets and configuration examples.
The article explains how Kafka implements delayed operations using a timing wheel with O(1) insertion and deletion, describes its parameters, multi‑level wheel design for large time spans, and the mechanism of advancing the wheel via DelayQueue and the ExpiredOperationReaper, contrasting it with Netty's approach.
This article details a step‑by‑step engineering effort to model and benchmark a high‑throughput red‑packet service capable of handling 10 billion requests, covering target metrics, hardware setup, Go implementation, load generation, monitoring, and performance analysis.
This article walks through the step‑by‑step evolution of a basic single‑server MVP architecture into a large‑scale distributed system, using a simulated Taobao example to illustrate ten‑plus architectural stages, key technologies, design principles, and the eventual shift to cloud‑native microservices.
The article details Tubi's Multimedia Processing Platform (TMPP), describing its architecture, processing stages, resource management, and distributed task scheduling for large‑scale video transcoding and delivery across multiple devices.
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.
This article explains the concept of consensus in blockchain, compares private, consortium and public environments, and introduces key algorithms such as Paxos, Raft, PBFT, Proof‑of‑Work and Proof‑of‑Stake, highlighting their challenges and solutions.
This article explains how Spring Cloud Alibaba provides a one‑stop microservices solution, details its components and migration costs, offers practical learning steps, and recommends resources for mastering the framework, making it ideal for businesses seeking rapid digital transformation.
This comprehensive guide outlines a step‑by‑step learning roadmap for aspiring big data professionals, covering fundamentals, programming languages, Linux, databases, distributed theory, networking, offline and real‑time computing, data governance, warehouses, toolchains, video/book recommendations, future industry trends, interview tips, and community resources.
The article introduces “Suffering‑Oriented Programming,” a risk‑averse development approach that prioritizes only truly painful problems, follows the three maxims “first make it possible, then make it beautiful, finally make it fast,” and illustrates its application in building Storm’s distributed stream‑processing system.
This article provides a comprehensive, step‑by‑step guide on why and how to split monolithic applications, prepare for migration, execute database vertical and horizontal sharding, handle schema changes, ensure consistency, and maintain stability during and after the split, enriched with real‑world pitfalls and solutions.
This article explains the principles, evolution, and implementation details of high‑availability architectures—from basic single‑node setups to multi‑active, cross‑region deployments—covering redundancy, disaster recovery, data synchronization, routing strategies, and the challenges of achieving true geo‑distributed active‑active systems.
The 2021 year‑end summary details FunTester’s rapid growth, the expansion of its public account, major performance upgrades reaching over 100 k QPS, the design of a distributed testing framework, dynamic load models, log‑replay innovations, and the author’s deep dive into Go, Java and Groovy.
The article presents a detailed 19‑section knowledge map for software architects, covering everything from core responsibilities and fundamentals to distributed caching, messaging, load balancing, performance testing, OS, algorithms, networking, databases, JVM, micro‑services, DDD, security, high availability, big data, and blockchain, with visual mind‑maps for each topic.
This article outlines the functional and non‑functional requirements, traffic calculations, service decomposition, and detailed microservice designs needed to build a highly available, scalable Twitter‑style system using distributed caches, databases, and asynchronous processing.
This article explains the fundamentals of load balancing, covering its purpose, vertical and horizontal scaling, various classifications such as DNS, IP, link‑layer and hybrid methods, common algorithms like round‑robin and weighted, as well as hardware solutions, providing a comprehensive guide for building scalable, high‑availability systems.
This article explains the evolution from centralized to distributed architectures, the role of Zookeeper in solving consistency problems, the mechanics and drawbacks of 2‑phase and 3‑phase commits, and key consensus algorithms such as Paxos, Raft, ZAB, as well as CAP and BASE theories that guide practical system design.
This article explores how caching accelerates read/write performance and reduces backend load, analyzes its benefits and costs, and presents practical strategies for cache updates, granularity control, penetration, avalanche, and hot‑key issues with concrete examples and diagrams.
This article explains how data consistency is achieved through local ACID transactions, global XA/2PC mechanisms, CAP and BASE theories, and practical patterns such as reliable events, TCC, and SAGA, providing a comprehensive overview for architects designing distributed systems.
The iQIYI Basic Data Platform unifies internal data exchange standards, integrates massive multi‑business data, and implements high‑availability solutions for ID services, messaging, HBase storage, and read‑write scaling, showcasing practical engineering approaches to big‑data reliability and performance.
This article explains how caching improves application performance and reduces backend load, and it details practical optimization methods such as benefit‑cost analysis, update policies, granularity control, penetration protection, bottomless‑hole mitigation, avalanche prevention, and hot‑key rebuild strategies, primarily using Redis.
This article explains the architecture and key features of NetEase Cloud's local server‑side recording cluster, detailing how dynamic scaling, multi‑backup high availability, load‑balancing strategies, monitoring, and an embedded registration center enable secure, reliable, and scalable recording for data‑sensitive applications.
This article explains the concept of anti‑entropy as a key mechanism for achieving eventual consistency in distributed database systems, illustrates how XDB Enterprise handles node failures and data drift, and shows practical examples of repairing inconsistencies using the AE service.
This article explains the concept of eventual consistency in distributed databases, introduces the Hint Switch (HH) queue used by XDB Enterprise to handle failed writes, and discusses how replication factor and node failures affect data consistency, illustrated with practical examples and diagrams.
This article explains the fundamentals of load balancing, covering why it is needed for high‑traffic services, the difference between vertical and horizontal scaling, various load‑balancing techniques (DNS, HTTP, IP, link‑layer, hybrid), common algorithms, and the trade‑offs of software versus hardware solutions.
Designing a distributed system involves overcoming major challenges such as heterogeneity, transparency, openness, concurrency, security, scalability, and fault tolerance, each of which must be addressed to build a reliable, extensible, and performant system.
This article provides a comprehensive overview of Kafka, covering its layered architecture with Zookeeper, core concepts such as topics, partitions and consumer groups, communication workflow, partition selection strategies, rebalancing mechanisms, reliability configurations, replica synchronization, and reasons for moving away from Zookeeper, all explained in clear English.
This article explains how to design a high‑concurrency system by selecting appropriate server architecture, load balancing, database clustering, caching layers, message‑queue handling, static‑content delivery, service‑oriented decomposition, redundancy, automation, and monitoring to ensure smooth operation under heavy user traffic.
This article provides a comprehensive guide to RocketMQ, covering Windows installation, environment configuration, startup commands, visual monitoring integration, SpringBoot dependency setup, core components, message models, reliability strategies, idempotent processing, transactional messaging, and ordering guarantees for distributed systems.
This article explains how to design and evolve server architectures, load‑balancing, database clustering, caching, message queues, and other techniques to handle high‑concurrency scenarios such as flash sales and timed red‑packet distribution in large‑scale e‑commerce systems.
This article explains the concepts of sequential, causal, and eventual consistency models, illustrating their definitions, guarantees, and practical examples—including process interactions and real‑world scenarios—while comparing their strengths, trade‑offs, and related consistency guarantees such as monotonic reads, writes, and read‑your‑writes.
This article explains how caching can accelerate read/write performance and reduce backend load, analyzes its benefits and costs, discusses update strategies, granularity control, and solutions for common problems such as cache penetration, empty‑hole, avalanche, and hot‑key reconstruction, providing practical guidelines for robust backend design.
This article explains how to design and optimize backend server architecture for high‑concurrency scenarios, covering load balancing, database master‑slave clusters, NoSQL caching, concurrency testing tools, caching strategies, message‑queue based async processing, layered and distributed designs, redundancy, automation, and service‑oriented approaches.
This article explains why rate limiting is essential for system stability, outlines common throttling patterns such as circuit breaking, degradation, delayed processing, and privilege handling, and dives into popular algorithms like counter, leaky bucket, and token bucket with concrete Java and Nginx examples.
The article explains consistency concepts in distributed systems—including strong and weak (eventual) consistency, CAP and FLP theorems, and key protocols such as 2PC, 3PC, and Paxos—detailing their mechanisms, advantages, and challenges.
The article explains how to design a high‑concurrency system by planning server architecture, load balancing, master‑slave databases, NoSQL clusters, caching layers, message‑queue based asynchronous processing, static‑content CDN, and automated redundancy to ensure scalability and reliability.
This article reviews the 13‑year evolution of Ant Group's registration center, analyzes the scaling and reliability challenges of multi‑cluster service discovery, and explains how the SOFARegistry 6.0 redesign—featuring meta‑driven consistency, slot‑based sharding, application‑level discovery, chaos testing, and automated operations—addresses those challenges while preparing the project for open‑source community growth.
This article introduces the fundamentals of message queues and compares five popular products—ActiveMQ, RabbitMQ, ZeroMQ, Kafka, and RocketMQ—detailing their features, typical use cases, and providing curated learning resources for each.
The article explains the characteristics of flash‑sale scenarios, presents core design principles such as rate limiting, peak shaving, asynchronous processing and scalability, and details a complete backend and frontend architecture—including Redis‑based queueing and caching—to build a robust, high‑throughput seckill system.
This article provides a comprehensive technical summary of large‑scale distributed website architecture, covering characteristics, goals, architectural patterns, performance, high‑availability, scalability, extensibility, security, agility, and a detailed evolution roadmap with practical examples and recommendations.
This article provides a comprehensive analysis of ZooKeeper 3.5.5 source code, covering the distributed‑mode server startup sequence, leader election algorithm, cluster data synchronization, server role responsibilities, and the request‑processor pipeline, offering practical insights for developers and operators.
This article explains how to evolve a large‑scale website architecture through stages such as initial single‑server setups, application‑data separation, caching, server clustering, read‑write separation, CDN/reverse proxy, distributed storage, micro‑services, and automation to achieve high availability, scalability, performance and security.
This article compares Apache Doris and ClickHouse across architecture, deployment, multi‑tenant management, data import, storage, query capabilities, performance testing, and cost, providing practical guidance for selecting the most suitable analytical database in large‑scale OLAP scenarios.
The article explains the functional purpose, early evolution, layered and clustered design, distributed technical architecture, caching, asynchronous checks, heterogeneous storage, payment solutions, and anti‑bot measures of a cloud‑based shopping cart, highlighting stability, performance, elasticity, and fault‑tolerance.
This article analyzes the evolution of large‑scale website architecture, covering stages from single‑server setups to layered, distributed, and clustered designs, and explains how caching, read‑write separation, CDN, asynchronous messaging, redundancy, automation, and security collectively achieve high performance, availability, scalability, and extensibility.
This article presents a comprehensive guide to designing high‑concurrency backend systems, covering server architecture, load balancing, database and NoSQL clustering, caching strategies, concurrency testing tools, message‑queue solutions, first‑level cache, static data handling, layering, distribution, asynchronous processing, redundancy and automation.
This article analyzes the extreme‑traffic problem of Chinese train ticket sales, presents a multi‑layer load‑balancing architecture with Nginx weighted round‑robin, demonstrates Go and Redis code for local and global stock deduction, and shows benchmark results proving that a single machine can handle over 4 000 requests per second while preventing oversell and few‑sell.
This comprehensive guide explains the essence of software architecture, its layers and classifications, architectural levels, strategic versus tactical design, evolution from monoliths to micro‑services, common pitfalls, and key metrics for evaluating a sound architecture.
The article explains edge computing as a new, global computing model that extends cloud computing by bringing compute, storage, and services closer to users, detailing its differences from centralized computing, its resource‑edge and global‑resource characteristics, and several reference architectures such as ETSI, Intel MEC, ECC, and OpenFog.
This article examines Martin Kleppmann’s view of Kafka as a database, breaking down how Kafka handles atomicity, consistency, isolation, and durability through event streams and partitioning, and argues why MySQL still remains essential for many workloads.
This article provides a thorough overview of distributed systems, explaining their definition, core concepts such as partition and replication, key challenges, essential characteristics, typical components and protocols, a practical request flow example, and a curated list of real‑world implementations to help readers build a solid learning roadmap.
This article provides a comprehensive overview of the design principles, architectural patterns, performance, availability, scalability, security, and operational considerations for building large distributed web sites, illustrated with a step‑by‑step evolution from a single‑server setup to a multi‑layer, cloud‑native architecture.
This article explains Kafka’s core architecture—including topics, partitions, replication, consumer groups, controller coordination with Zookeeper, and performance tricks like sequential writes and zero‑copy—to show how it achieves high‑throughput, fault‑tolerant messaging for large‑scale systems.
This article presents a code-less business process orchestration framework that transforms static Go code into configurable drag‑and‑drop workflows, detailing the underlying concepts, architecture, core Go data structures, dynamic configuration, and practical deployment examples for distributed task scheduling and execution.
This article explains the core concepts of Elasticsearch’s distributed architecture—including nodes, indices, shards, replicas—and compares mixed and tiered deployment models, while also discussing data storage strategies, replica benefits, and the trade‑offs of local‑file versus shared‑storage distributed systems.
This article explains the concepts of high concurrency, high performance, and high availability, and demonstrates how to design cloud‑native IaaS, PaaS, and SaaS layers to keep internet services resilient during massive traffic spikes such as Double‑11.
This article explores service governance fundamentals, examines why etcd’s Raft‑based consensus and BoltDB storage make it ideal for large‑scale systems, compares it with ZooKeeper and Consul, and shares Baidu’s practical architecture, performance tricks, and operational metrics for high‑availability, high‑performance service management.
The article explains the reasons for scaling, outlines hardware and component expansion strategies, introduces the AKF splitting principle for distributed systems, discusses database clustering and distributed ID generation methods such as UUID and Snowflake, and describes elastic scaling challenges and solutions.
This article explains why scaling is necessary, outlines hardware and component expansion strategies, introduces the AKF partitioning principle for horizontal and vertical scaling, discusses challenges after splitting, and reviews database clustering and distributed ID generation techniques such as UUID and Snowflake, highlighting their advantages and drawbacks.
This article explains how JD.com designs and evolves its OLAP platform, covering data sources, ingestion, storage, real‑time and offline processing, key challenges such as timeliness, high throughput, consistency, and the solutions implemented to support massive e‑commerce analytics.
The article introduces Alibaba's Elastic Federated Learning Solution (EFLS), describing its business motivations, core functionalities, system architecture, sample‑set intersection, federated training pipeline, novel algorithms, product console, and future roadmap for privacy‑preserving advertising in large‑scale sparse scenarios.
