This article analyzes Elasticsearch’s distributed architecture, explaining core concepts such as nodes, indices, shards and replicas, comparing mixed and layered deployment models, and discussing data‑layer design choices and their trade‑offs in modern distributed storage and search systems.
This article explains why message middleware guarantees at‑least‑once delivery, describes three common duplication scenarios in RocketMQ, and presents both transactional and non‑transactional deduplication solutions—including SQL examples and a Redis‑based idempotence library—to achieve exactly‑once processing.
MQProxy is a Java‑based distributed message‑queue proxy built on Apache Kafka that abstracts underlying queue selection, protocols, and health monitoring, offering developers a simple SDK with produce/consume/commit APIs, advanced features like delayed and dead‑letter queues, and a scalable architecture for high availability.
The article explains how Ant Group’s Alipay handles massive double‑11 payment traffic by using logical data centers (LDC), unit‑based architecture with RZone, GZone and CZone, traffic routing, disaster‑recovery strategies, and a CAP‑aware design built on the OceanBase distributed database.
This article examines the key problems message middleware must address—such as publish/subscribe, ordering, filtering, persistence, reliability, latency, and transaction support—and explains how Apache RocketMQ’s architecture and design choices provide high‑performance, high‑throughput solutions to each of these challenges.
The article thoroughly examines RocketMQ’s NameServer, detailing its lightweight registration-center architecture, startup sequence, and the three core routing mechanisms—registration via broker heartbeats, timed removal of stale brokers, and client‑pulled discovery—while explaining key metadata tables and design patterns such as JVM shutdown hooks and read‑write locks for high‑concurrency safety.
This article examines why ZooKeeper may not be the optimal solution for service discovery, analyzing CAP trade‑offs, consistency versus availability, scalability limits, health‑check design, and practical lessons from Alibaba’s decade‑long experience to guide the design of robust registration centers.
Using Taobao as a case study, this article traces the architectural evolution from a single-server setup handling hundreds of requests to a multi-layered, distributed system capable of supporting millions of concurrent users, detailing each stage’s challenges, technologies such as caching, load balancing, microservices, and cloud deployment.
The article compares how RocketMQ, Apache Kafka, and Apache Pulsar implement transactional messaging—each using a two‑phase commit with half‑messages or transaction buffers, distinct coordinators, and idempotent producers—to provide atomicity and either strong consistency (RocketMQ) or exactly‑once delivery for high‑throughput stream processing (Kafka and Pulsar).
This article describes how to design, implement, and evaluate a scalable backend that can simulate 10 billion WeChat red‑envelope requests by supporting up to 1 million concurrent users and handling 30 k–60 k QPS per server using Go, Linux tools, and custom monitoring.
The article uses a personal bank‑transfer incident to introduce local transaction concepts, explains the CAP theorem, examines the challenges of distributed transactions, and presents a message‑queue‑based design with transaction logs and compensation mechanisms to ensure consistency and idempotency.
This article explains why rate limiting is essential, outlines common strategies such as circuit breaking, service degradation, delay processing, and privilege handling, compares counter, leaky‑bucket and token‑bucket algorithms, and provides practical Java and Nginx‑Lua implementation examples for backend systems.
This article provides a comprehensive technical overview of large‑scale distributed website architecture, covering its characteristics, design goals, architectural patterns, performance, high availability, scalability, extensibility, security, agility, and a detailed example evolution from a single‑server setup to a multi‑layer, service‑oriented system.
This article presents the architecture, key features, and operational benefits of a cloud‑native ClickHouse OLAP platform, describing how storage‑compute separation, a unified master node, and shared storage reduce cost, improve availability, and simplify management while remaining fully compatible with the open‑source ClickHouse ecosystem.
Shopee Shake’s architecture separates admin and user sides into three layers—access, application, and resource—and uses horizontal scaling, bucketed Redis coin pools, multi‑level caching, asynchronous message queues, precise capacity formulas, and comprehensive monitoring and chaos‑engineered runbooks to reliably handle over 300,000 QPS during major promotional events.
This article explains the design and implementation of MySync, a component that leverages MySQL binlog to synchronously replicate data to heterogeneous storage systems such as Redis, ElasticSearch, and HBase, detailing its architecture, reader and writer modules, Kafka integration, and strategies for handling master‑slave failover.
An in‑depth look at Kafka’s high‑availability mechanisms reveals how multi‑replica design, ISR leader election, and the request.required.acks setting interact, why a single broker failure—especially of the __consumer_offset topic—can halt consumption, and how to configure replication factors to prevent such outages.
The article by the Vivo Internet Server Team details Apache Dubbo’s ten‑layer architecture—service, config, proxy, registry, cluster, monitor, protocol, exchange, transport, and serialization—explaining each layer’s role, the RPC invocation flow, registration/discovery mechanisms, configuration options, and how Dubbo’s extensive SPI‑based modular design enables loose coupling, extensibility, and robust micro‑service development.
This article presents a comprehensive design of a cloud‑native ClickHouse OLAP system, detailing its three‑layer architecture, storage‑compute separation, unified metadata management, high‑availability mechanisms, elastic scaling, cost reductions, and future enhancements for multi‑replica and MPP query support.
The article outlines six fundamental traits of serverless architecture—low barrier-to-entry, hostless, stateless, elasticity, distributed, and event-driven—explains why they matter, discusses the benefits and hidden challenges each introduces, and offers concise recommendations for developers to successfully adopt serverless solutions.
The article details Meituan's five‑year evolution of its instant logistics platform, describing the distributed high‑concurrency architecture, AI‑driven optimization, scalability and fault‑tolerance techniques, and future challenges in micro‑service and unit‑based operations.
This article explains high-fidelity load testing, outlines the technical breakthroughs of JD's ForceBot platform—including massive traffic generation, full‑domain traffic recording and replay for both public and internal networks—and showcases its successful deployment during the 618 and Double‑11 shopping events.
iQIYI TV’s backend implements a distributed real‑time local cache that combines hotspot local storage with a unified messaging bus, management console, and business‑filtering to deliver instant updates, improve hit rates, reduce read‑bottleneck risk, and alleviate load on centralized caches under extreme concurrency.
The article analyzes the evolution of service registration in Alibaba, compares ZooKeeper with other solutions, and argues that for large‑scale service discovery a registration center should prioritize availability over strong consistency, support flexible health checks, handle partitions gracefully, and avoid the pitfalls of using ZooKeeper as a universal registry.
The article outlines the defining traits of large-scale websites—high concurrency, massive data, 24/7 availability, security, and rapid iteration—and discusses major technical challenges such as scaling, caching, clustering, database read/write separation, CDN acceleration, distributed storage, and service decomposition.
This article explains the concepts, strategies, and algorithms of rate limiting—including circuit breaking, service degradation, leaky‑bucket and token‑bucket methods—and provides practical Java, Guava, and Nginx + Lua implementations for controlling concurrency and protecting backend services.
This article explores the importance of resource isolation in distributed systems, detailing thread, process, cluster, data‑center, read/write, static, and crawler isolation techniques, with practical code examples from Netty, Dubbo, and Tomcat, and discusses trade‑offs such as thread‑pool versus semaphore isolation.
This article explains why a message system like Kafka is crucial for decoupling services, handling asynchronous workflows such as e‑commerce flash sales, controlling traffic, and achieving high concurrency, high availability, and high performance through sequential disk writes, zero‑copy reads, replication, and careful resource planning.
Message middleware guarantees at-least-once delivery, but repeated deliveries cause duplicate processing; this article explains RocketMQ's duplicate scenarios, explores simple and advanced deduplication techniques—including database-transactional and Redis-based idempotent tables—and provides practical Java code for implementing exactly-once semantics.
This article uses the classic two‑generals story to illustrate communication failures in distributed systems, then explains local versus distributed transactions, their roles, scenarios, and classifications, linking concepts such as atomicity, retries, idempotence, and the three‑way handshake.
The article explains common microservice transaction patterns—including blocking retry, asynchronous queues, TCC compensation transactions, and local message tables—detailing their implementations, advantages, drawbacks, and practical code examples for ensuring data consistency in distributed systems.
This article explains why resource isolation is essential in distributed architectures and details various isolation techniques—including thread, process, cluster, data‑read/write, static, and crawler isolation—illustrated with Netty, Dubbo, and Tomcat examples, code snippets, and practical recommendations.
This article explains how to implement efficient real-time WebSocket message pushing in a distributed system by using Redis as a coordination layer, defining Go-based Client and ClientManager components, and detailing the end‑to‑end flow from message production to client delivery.
Shopee’s Logistics Channel Service migrated from a shared‑cluster MySQL setup to dedicated clusters, added TiDB for transient tracking data, and ultimately adopted hash‑based sharding with separate order and tracking services plus an asynchronous compensation mechanism, enabling uniform data distribution, cross‑database consistency, six‑month archiving, and scalable growth without rebalancing.
This article explains the fundamentals and advanced aspects of cookies and sessions, covering their definitions, use cases, key differences, handling when cookies are disabled, session management in distributed systems, same‑origin policy, cross‑origin requests, and security considerations for interview preparation.
This article examines the essential concepts, common pitfalls such as cache stampede, penetration, avalanche, hot keys, large keys, consistency, and concurrent pre‑heating, and presents practical design patterns and mitigation techniques for building a robust Redis cache architecture that can handle billion‑scale traffic.
This article provides a comprehensive overview of caching in large distributed systems, covering theory, common components, classification, CDN and reverse‑proxy caches, local application caches, popular implementations like Ehcache, Guava, Memcached and Redis, and a detailed comparison of their features and trade‑offs.
This talk shares practical high‑availability design experiences from Alibaba’s e‑commerce platform to its cloud services, covering traditional IDC stability mechanisms, cache and disaster‑recovery strategies, cloud‑native fault handling, capacity planning, traffic shaping, and lessons learned from real incidents.
The article explains message queues, their primary use cases—async processing, system decoupling, and traffic peak shaving—compares five mainstream products (ActiveMQ, RabbitMQ, Kafka, RocketMQ, Pulsar), and examines core design principles such as architecture, storage choices, push/pull models, and consumer groups, highlighting broader relevance to distributed systems.
Alibaba’s eight‑year Double 11 journey illustrates how the company tackled exponential business growth by inventing high‑availability middleware, precise capacity planning, unit‑based deployment, online stress testing, hybrid‑cloud elasticity, and intelligent runtime control to balance throughput, cost, and user experience during the midnight peak.
The article details Meituan’s five‑year evolution of its instant logistics platform, describing the distributed backend architecture, AI‑driven optimization, scalability and high‑availability practices, as well as future challenges in microservice complexity and operational automation.
This article details Meituan's five‑year journey building a high‑availability, low‑latency instant logistics platform, describing the distributed architecture evolution, AI‑driven optimizations, fault‑tolerance techniques, and future challenges in scaling micro‑services for massive order and rider volumes.
This article explains the fundamentals of local transactions and ACID properties, introduces CAP and BASE theories, discusses compensation jobs and transaction messages, and reviews distributed transaction models such as two‑phase commit, three‑phase commit, XA, TCC, Saga, and the Seata framework.
This article reveals how Taobao Open Platform’s API gateway, message service, and data‑sync system achieve ultra‑high concurrency, low latency, and zero‑loss reliability through multi‑level caching, asynchronous processing, traffic control, and distributed storage techniques.
This article introduces Zookeeper fundamentals and demonstrates four real‑world scenarios—consistent configuration management, distributed locking, distributed queuing, and service load balancing—complete with Java code samples, workflow diagrams, and step‑by‑step explanations for building reliable distributed systems.
This article explains HBase's write path using Memstore and HLog, details the lifecycle of HLog including construction, rolling, expiration, and deletion, and thoroughly analyzes the three fault‑recovery models—Log Splitting, Distributed Log Splitting, and Distributed Log Replay—highlighting their processes, advantages, and configuration nuances.
This article examines financial sector database disaster recovery, defining disaster recovery and backup, analyzing current practices and requirements, reviewing market trends, and detailing various distributed database disaster‑recovery architectures such as single‑center, intra‑city active‑active, and multi‑site three‑center solutions.
This article explains why session sharing is critical in micro‑service architectures, compares common Nginx load‑balancing methods, and provides four practical solutions—ip_hash load balancing, Tomcat session replication, Redis‑based session caching, and cookie‑based sharing—complete with configuration examples and pros/cons.
This article explains why rate limiting is essential for system stability, compares it with caching and degradation, details three core algorithms—counter, leaky bucket, and token bucket—and provides concrete Guava, Java, and Nginx + Lua code examples for implementing both local and distributed throttling.
A real‑world incident with ActiveMQ’s high‑availability setup shows that focusing on component reliability without business‑level capacity planning, monitoring, and graceful degradation can cripple services, highlighting that true high availability must prioritize overall system and user experience.
The article records a video interview where the candidate answers technical questions on MySQL isolation levels, InnoDB indexing, locking, distributed locks, Redis sharding, PHP‑FPM architecture, RabbitMQ, AOP, Hyperf, and other backend topics, reflecting on the challenges faced during the session.
This article explains the fundamental message exchange architectures and routing patterns—including publish‑subscribe, fan‑out, unidirectional and bidirectional streaming, as well as unicast, broadcast, multicast, and anycast—illustrated with diagrams and practical examples for modern backend systems.
By illustrating how to replace simple HashMap caching with Redis‑based distributed caches and locks—using SETNX, Lua scripts, and Redisson—the article shows Spring Boot developers how to prevent cache breakdown, ensure data consistency, and dramatically improve throughput in high‑concurrency web applications.
This article provides a comprehensive introduction to Apache Kafka, covering its core concepts, terminology, data flow, configuration options such as compression and partitioning, producer and consumer behavior, replication, consumer groups, offset management, and log retention strategies.
This article explains how Vipshop built the Hera data service since 2019 to provide a unified API for warehouse access, detailing its background, architecture, core features such as multi‑engine queries, adaptive execution, custom Lisp syntax, task scheduling, metrics collection, and performance gains for both B‑to‑B and B‑to‑C workloads.
This article explains how to transform single‑node Elastic Job Lite deployments into highly available solutions, covering Zookeeper‑based sharding, active‑standby strategies for dual‑data‑center setups, custom sharding implementations, and priority scheduling to ensure tasks run reliably across both primary and backup sites.
This article explains the design and architecture of a cloud-based shopping cart system, covering its functional modules, layered and cluster designs, distributed goals such as stability and elasticity, three-level caching, asynchronous checks, heterogeneous storage, payment solutions, and anti‑scalping measures.
This article explains the split‑brain phenomenon in distributed clusters, uses Zookeeper as a case study to illustrate how network partitions can create multiple leaders, and details Zookeeper's majority‑quorum mechanism, node count considerations, and common strategies for avoiding split‑brain scenarios.
This article explains why session sharing is needed in micro‑service architectures, outlines common Nginx reverse‑proxy strategies, and presents four practical solutions—Nginx ip_hash load balancing, Tomcat session replication, Redis centralized cache, and cookie‑based sharing—detailing their implementations and trade‑offs.
This article explains the circuit breaker pattern, its three-state lifecycle, and provides a detailed Go implementation with code examples, state transition logic, metric tracking, and known limitations for improving service stability.
This article presents a comprehensive visual overview of microservice concepts and architecture evolution, detailing the transition from monolithic to SOA and microservice styles, and introduces Spring Cloud’s core components—including Eureka, Ribbon, Feign, Hystrix, Zuul, Gateway, Config, Bus, OAuth2, and Sleuth—illustrated with 19 mind‑map diagrams.
This article traces the historical development of microservice architecture from monolithic to distributed services, outlines its advantages and drawbacks, and presents a comprehensive technology stack—including service communication, API gateways, authentication, fault tolerance, logging, configuration, containerization, orchestration, and CI/CD—to guide practitioners in building robust, scalable backend systems.
This article provides a comprehensive overview of distributed system fundamentals, covering concepts such as nodes, replicas, consistency models, data distribution strategies, lease and quorum mechanisms, two‑phase commit, MVCC, Paxos, and the CAP theorem, while discussing their practical engineering trade‑offs and failure handling.
The article introduces Youzan's Transparent Multilevel Cache (TMC), detailing its three‑layer architecture, hotspot detection and local caching mechanisms, integration approaches for Java applications, stability and consistency features, and performance results from real‑world e‑commerce campaigns.
Edge computing, the latest computing paradigm beyond distributed, grid, and cloud models, integrates cloud, network, and intelligent edge devices to provide globally coordinated, low‑latency, intelligent services, and its various reference architectures—ETSI MEC, Intel MEC, ECC, and OpenFog—illustrate its resource‑centric, collaborative, and heterogeneous design.
This article explains what RPC is, walks through each step of a remote procedure call—including stubs, routing, load balancing, serialization, encoding, and network transmission—and analyzes where latency originates to guide backend developers in performance optimization.
This article explains how to abstract business functions into a middle platform through 0‑level and 1‑level modeling, defines domain capabilities and centers, outlines eight service design principles, and describes essential distributed mechanisms such as service registration, elastic scaling, rate limiting, gray release, messaging, and distributed transactions.
This article outlines the design principles, layered and cluster architecture, technical requirements, caching strategies, and payment integration of a cloud‑based shopping cart system, highlighting how distributed backend components ensure stability, high performance, elasticity, and anti‑fraud measures.
This article details how a rapidly growing Chinese children's programming platform tackled the complexity of distributed system observability by adopting SkyWalking, Prometheus, and Alibaba Cloud ARMS front‑end monitoring, achieving faster fault detection, reduced operational workload, and improved user experience.
This article explains Kafka's high‑availability mechanisms, covering multi‑replica design, ISR synchronization, leader election, acknowledgment settings, and the hidden risk of the __consumer_offset topic's single‑replica configuration that can cause an entire cluster to become unavailable when one broker fails.
This article traces the evolution of a high‑concurrency backend architecture from a single‑machine setup to cloud‑native microservices, detailing ten‑plus stages such as separating Tomcat and DB, adding caches, load‑balancing with Nginx/LVS, read‑write splitting, sharding, containerization, and finally deploying on public cloud, while also summarizing key design principles.
The Transparent Multilevel Cache (TMC) solution adds application‑level hotspot detection, local caching, and hit‑rate statistics to a standard distributed cache stack, enabling automatic hotspot discovery, reducing load on backend cache clusters, and improving system stability and latency during traffic spikes.
This article explains how microservices evolved from monolithic systems, compares them with SOA, discusses data fragmentation, CAP theory, distributed transactions, service partitioning, circuit breakers, API gateways, configuration management, tracing, and reviews popular frameworks like Spring Cloud and Dubbo.
This article explores the challenges of log aggregation in micro‑service architectures, introduces a globally unique log identifier (logid) with its required properties, compares various logid generation schemes, and presents end‑to‑end solutions for log distribution, aggregation, and analysis using custom tools such as ylog and watcher.
This article details NetEase Cloud's backend engineering solutions for supporting ten‑thousand‑user concurrent mic connections, covering distributed signaling architecture, QUIC‑based weak‑network handling, server‑side audio routing, video QoS strategies, and a global transmission network (WE‑CAN) to achieve high availability and scalability.
The article compares the architectures of NSQ (YouZan branch), Kafka, and RocketMQ—detailing their coordination mechanisms, storage models, consistency guarantees, and operational trade‑offs—while recommending Kafka for log‑big‑data workloads, RocketMQ for massive topic counts, and NSQ for extensibility and lightweight deployment.
The article outlines the challenges of real‑time messaging in live‑streaming education, presents a multi‑stage backend architecture—including AccessServer, MessageServer, and specialized services—along with caching, clustering, and future enhancements such as connection migration and QUIC to achieve high reliability, low latency, and massive concurrency.
Analyzing Hystrix’s source code reveals how its circuit‑breaker, bulkhead isolation (semaphore or thread‑pool), timeout detection, fallback logic, and sliding‑window health metrics work together to prevent cascading failures in distributed systems, as illustrated by an e‑commerce order service calling multiple downstream services.
This article explains the purpose, scenarios, and detailed implementations of common rate‑limiting algorithms—including counter (fixed window), sliding window, token bucket, and leaky bucket—highlighting their advantages, drawbacks, and sample Python code for each.
This article analyzes Bilibili's recent service disruption, 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 active‑active deployments to improve system reliability.
This article explains how to use chaos engineering with Gremlin to design, execute, and analyze four experiments that test Kafka broker load, message loss, split‑brain scenarios, and ZooKeeper outages, helping improve the reliability and resilience of Kafka deployments.
This article explains how adopting ten essential microservice best practices—such as single responsibility, independent data stores, asynchronous communication, circuit breakers, API gateways, backward‑compatible APIs, versioning, dedicated infrastructure, independent release pipelines, and organizational efficiency—helps build a loosely‑coupled, scalable, and maintainable backend system.
This article explains why distributed systems need consistency algorithms, compares weak and strong consistency, outlines the challenges of unreliable networks and clocks, and provides a detailed walkthrough of the Raft consensus protocol, its node states, state variables, RPCs, and a practical lab implementation for leader election.
The article compares four popular service registration centers—Eureka, Zookeeper, Consul, and Nacos—by outlining each system’s architecture, core principles, and operational mechanisms, with an in‑depth source‑code‑level examination of Nacos’s pull/push discovery, Raft‑based leader election, and concurrent instance management, and concludes with a comparative table of consistency, availability, and partition‑tolerance characteristics.
ClickHouse is an MPP column‑oriented DBMS that combines full DBMS functionality, advanced columnar storage with high compression, SIMD‑based vectorized execution, a rich relational SQL interface, diverse table engines, multi‑master clustering, and flexible sharding and distributed query capabilities, making it exceptionally fast for analytical workloads.
This article walks through the motivation, core components, technology choices, architecture, and implementation details—including service registration, provider and consumer modules, custom protocol design, serialization, load balancing, and Netty-based I/O—of a self‑written distributed RPC framework, and presents performance test results.
This comprehensive guide explores microservice architecture—from its historical roots and core characteristics to ten vital design patterns such as database per service, event sourcing, CQRS, Saga, BFF, API gateway, Strangler, circuit breaker, externalized configuration, and consumer‑driven contract testing—detailing their advantages, drawbacks, and practical usage scenarios.
This article walks through implementing a lightweight single‑machine MapReduce framework inspired by the original MapReduce paper, covering the abstract Map/Reduce model, task scheduling between master and workers, core Go code for map, reduce, worker, and coordinator, and a brief reflection on its limitations.
From a fresh Java junior engineer to a seasoned architect, this guide outlines the essential skills, technologies, and responsibilities at each career stage—1‑3 years, 3‑5 years, and 5‑8 years—highlighting data structures, algorithms, frameworks, distributed systems, and database expertise needed for backend development.
The article introduces the DCS_FunTester framework, explains its naming, provides Swagger API access, details local deployment steps, describes a header‑based authentication change, outlines asynchronous test execution, multi‑request handling, progress tracking, and includes relevant Java code snippets for building and running the system.
Zhou Jing shares his journey from backend architecture to leading Alibaba Cloud's edge cloud native container platform, discussing recent projects, technical challenges, design principles, emerging trends like ServiceMesh and WebAssembly, and his role in the GIAC 5G edge computing conference.
The article explains how to abstract remote data location logic using an RPC framework built on Spring and Dubbo, demonstrates proxy injection and service registration, and shows how similar principles are applied to Redis Cluster to reduce request redirection and improve scalability.
This article explains the fundamentals of message queues, their role in asynchronous processing and system decoupling, introduces Java Message Service (JMS), compares point‑to‑point and publish‑subscribe models, and provides an overview of common MQ implementations such as RabbitMQ and Kafka.
This article chronicles my step‑by‑step journey of learning Apache RocketMQ, from downloading the source and building it with Maven, configuring mirrors, launching NameServer, Broker, Producer and Consumer on Windows, to exploring the system’s architecture and design principles.
This article outlines the architecture of a high‑traffic user center system, detailing core requirements, distributed session and transaction handling, caching strategies with Redis and local caches, common challenges such as hot data and high‑frequency queries, and provides practical solutions and a concise summary.
This guide explains how to integrate the Java‑based Snowflake UIDGenerator into a Spring Boot 2.3 application, covering algorithm fundamentals, RingBuffer optimization, database setup, Maven dependencies, configuration beans, and a test case to generate high‑throughput unique IDs.
This comprehensive article explains the microservices architectural style, contrasts it with monolithic (integrated) architecture, outlines the problems solved by microservices, details design principles, splitting strategies, patterns such as Strangler and Rehab, and provides a complete Java SpringBoot example with code snippets and deployment instructions.
This article introduces key microservice architecture design patterns—including Database per Service, Event Sourcing, CQRS, Saga, BFF, API Gateway, Strangler, Circuit Breaker, Externalized Configuration, and Consumer‑Driven Contract Testing—explaining their advantages, drawbacks, appropriate usage scenarios, and example technologies to guide scalable, resilient system design.
This article introduces Apache Kafka’s KRaft (Kafka Raft) mode, explains its architectural differences from ZooKeeper‑based deployments, details essential configuration parameters, and provides a complete step‑by‑step procedure—including commands and utility tools—to set up and operate a KRaft cluster.
This article provides a comprehensive guide to using Apache RocketMQ with Spring Boot, covering core components, basic concepts, key features, step‑by‑step configuration, producer and consumer code examples, advanced message types, transaction handling, and common interview questions for distributed messaging systems.
