This article explains the fundamentals of message queues, compares point-to-point and publish‑subscribe models, introduces Java Message Service (JMS) and its programming model, and provides an overview of popular MQ implementations such as RabbitMQ, ActiveMQ, RocketMQ, and Kafka, highlighting their architectures and use cases.
This article presents a comprehensive guide to using Redis for various backend scenarios, including caching hot data, sharing sessions, implementing distributed locks, generating global IDs, counting, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, tags, product filtering, follow relationships, and ranking, all illustrated with concrete code examples.
This article explains the fundamentals of message queues, compares JMS with popular implementations such as RabbitMQ and Kafka, describes their architectures, usage patterns, and how they improve system performance, scalability, and decoupling in backend applications.
This article introduces Tencent Cloud Message Queue (CMQ), explains its underlying architecture and reliability mechanisms, compares it with Kafka and RabbitMQ, and demonstrates several practical scenarios such as broadcast‑pull model, long‑polling, delayed messages, message rewind, topic routing, large‑message handling, and encryption, accompanied by Python SDK code examples.
This article provides a comprehensive overview of message middleware concepts, the role and benefits of message queues, and an in‑depth look at RabbitMQ architecture, components, reliability mechanisms, advanced features, and internal storage and flow‑control mechanisms.
This article provides a comprehensive overview of Apache Kafka, covering its definition, architecture, command‑line API usage, Java producer and consumer examples, core principles such as high availability and message ordering, configuration tuning, and a summary of its advantages as a high‑throughput, fault‑tolerant streaming platform.
This article explains the fundamental differences between RabbitMQ and Apache Kafka, covering their architectures, message models, ordering guarantees, routing capabilities, timing controls, retention policies, fault‑tolerance mechanisms, scalability, and consumer complexity, and then provides practical criteria to help architects decide which solution best fits a given use case.
The article explains why large‑scale e‑commerce systems need sharding and data heterogeneity, compares dual‑write, asynchronous MQ, and Canal‑based binlog solutions, and discusses how to achieve real‑time, ordered data synchronization using MySQL replication, Canal, and message queues.
This article explains the core concepts of message queues—including producers, consumers, messages, brokers, and push/pull delivery—and analyzes three consumption scenarios: at‑most‑once, at‑least‑once, and exactly‑once, detailing the required producer, broker, and consumer behaviors for each.
This article provides a comprehensive overview of message queues, explaining their purpose, usage scenarios, two communication models, and detailing Java Message Service (JMS) as well as popular MQ implementations such as RabbitMQ and Kafka, complete with diagrams and code examples.
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 talk by Zha Jia showcases StreamNative’s open‑source Function Mesh, which builds on Apache Pulsar’s unified messaging and streaming platform and its lightweight Pulsar Functions, using Kubernetes CRDs to simplify deployment, management, and scaling of serverless data processing while highlighting the project’s rapid community growth.
The article examines how to prevent data inconsistency during cross‑service money transfers by using local transactions, two‑phase commit, and message‑queue based eventual consistency, providing detailed code examples, performance considerations, and practical solutions for large‑scale backend systems.
This article explains the drawbacks of traditional synchronous architectures, outlines why adopting message queues improves latency, coupling, and peak‑handling, and then details common MQ problems such as duplicate messages, data inconsistency, loss, ordering, backlog, and increased complexity along with concrete mitigation strategies.
This article explains how to guarantee data consistency when updating multiple tables or services by using local transactions for single‑node systems, two‑phase commit for distributed databases, and reliable message‑queue patterns—including RocketMQ transactional messages—to achieve eventual consistency and handle duplicate delivery.
This article explains why Kafka is used, its buffering, decoupling, redundancy and robustness benefits, details the ack reliability levels, storage design, replica synchronization, ISR handling, consumer partition assignment strategies, transaction support, exactly‑once semantics, and why read‑write separation is not provided.
This article examines common techniques for handling service call failures in micro‑service architectures—blocking retries, asynchronous queues, TCC compensation transactions, local message tables, and MQ transactions—detailing their implementations, pitfalls, and trade‑offs to achieve reliable data consistency.
This article explains why message queues are used in system architecture, outlines their advantages such as decoupling, asynchronous processing, and traffic shaping, discusses their drawbacks, and compares four popular MQ solutions—Kafka, ActiveMQ, RabbitMQ, and RocketMQ—to help candidates ace interview questions.
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 examines four common message middleware delivery models—Point-to-Point, Publish/Subscribe, Partition, and Transfer—explaining their core characteristics, differences, usage scenarios, and performance considerations, with practical examples from ActiveMQ, Kafka, RocketMQ, and NSQ in modern systems.
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 explains why RocketMQ was chosen, describes its large‑scale usage, details the design and implementation of a same‑city dual‑center architecture with near‑by production and consumption, outlines failover mechanisms, governance practices, lessons learned, and future plans for the messaging platform.
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.
DCheck is a real‑time asset‑loss detection platform that monitors MySQL binlog and MQ streams, matches upstream and downstream states, and triggers alerts via configurable topics, events, sub‑events and Groovy scripts implementing filter and check logic, while offering rule‑based controls, debugging tips, and proposed enhancements for reliability.
This article explains why message queues are used in modern systems, outlines common interview questions about MQ, discusses the advantages of decoupling, asynchronous processing, and traffic shaping, examines the pros and cons of MQ, and compares the four major MQ products—Kafka, ActiveMQ, RabbitMQ, and RocketMQ—to help candidates prepare for technical interviews.
This tutorial explains the Redis publish/subscribe pattern, contrasts it with the observer pattern, and provides a complete Spring Boot example—including configuration, publisher and subscriber code, and verification steps—to help developers quickly implement real‑time messaging with Redis.
This article explains practical techniques for detecting lost messages, ensuring reliable delivery across production, storage, and consumption stages, handling duplicate deliveries with idempotent designs, managing message backlogs through performance tuning, and using transactional messages to achieve distributed transaction consistency.
The article explains how to detect and prevent message loss, guarantee reliable delivery, handle duplicate consumption, manage message backlog, and implement distributed transaction messages in systems like Kafka, RocketMQ, and RabbitMQ, providing practical code examples and best‑practice guidelines.
This article introduces Apache Pulsar, outlines its key features and advantages over other messaging systems, and provides step‑by‑step Spring Boot integration code examples including dependencies, configuration, producer and consumer implementations for both normal and delayed messages.
This article provides a comprehensive overview of RabbitMQ, covering its AMQP fundamentals, exchange types, reliable delivery mechanisms, consumer flow control, idempotency, dead‑letter queues, TTL, clustering modes, and related high‑availability tools such as HAProxy and Keepalived.
This article evaluates open‑source message‑queue options by examining company, middleware‑team, and end‑user criteria, then explains why RocketMQ’s low technical and labor costs, stability, rich features, high performance, built‑in management tools, monitoring support, and active community make it a cost‑effective choice over alternatives like Kafka and Pulsar.
This article explains the evolution of message‑queue middleware by comparing the basic double‑ended queue implementation, Redis list usage, Kafka’s partitioned log architecture with segment files and sparse indexes, and Pulsar’s compute‑storage separation using BookKeeper, highlighting their designs, strengths, and trade‑offs.
This article explains Apache RocketMQ's high‑performance distributed messaging architecture, detailing its core components—Producer, Broker, Consumer, and Namesrv—along with routing metadata, message ordering, priority handling, parallelism, and essential concepts such as topics, groups, tags, and reliability mechanisms.
This article explains Apache Pulsar’s cloud‑native, storage‑compute separated architecture, its data model and scalability features, and how it integrates with Flink to provide a unified platform for both real‑time streaming and batch processing in big‑data applications.
This article analyzes why an ActiveMQ notification queue became overloaded during transaction spikes, identifies synchronization and configuration bottlenecks, and presents three practical optimization steps—including removing a synchronized lock, tuning queuePrefetch, and redesigning with dual queues—to dramatically improve throughput.
This article compares Apache Pulsar and Apache Kafka across performance, architecture, features, and real‑world use cases, highlighting Pulsar’s multi‑layer design, scalability, client language support, ecosystem integrations, and operational advantages while providing detailed analysis of storage, messaging models, and community resources.
This article explains the design of a low‑latency timeout center, compares it with traditional high‑latency approaches, and details a Redis‑based solution that uses sorted sets, topic partitioning, and multi‑stage queues to achieve fast, reliable, and at‑least‑once message processing.
This guide walks you through installing Kafka on CentOS, configuring Zookeeper, using command‑line tools, setting up the Kafka‑Eagle visual interface, and integrating Kafka with a Spring Boot application, providing code snippets and step‑by‑step instructions for a quick start.
This article provides a comprehensive introduction to Kafka, covering its role as a message system, core concepts such as topics, partitions, producers, consumers, messages, the cluster architecture with replicas and controllers, performance optimizations, log segmentation, and network design, all illustrated with diagrams and code examples.
This article explores the role of message queues in microservice architectures, discussing their advantages such as decoupling, asynchronous processing, and load shedding, while also addressing design challenges like concurrency, ordering, duplicate handling, and transactional messaging with solutions including Kafka partitions, outbox patterns, CDC, and RocketMQ.
This article analyzes the causes of data backlog in an ActiveMQ message queue, demonstrates how synchronized locks and default prefetch settings limit throughput, and presents three optimization phases—including removing the lock, adjusting queuePrefetch, and redesigning queue handling—to achieve over 30‑fold performance improvement.
This article provides a clear, visual walkthrough of Kafka’s fundamental architecture, explaining how producers and consumers interact, the role of topics and partitions, consumer groups, and ZooKeeper’s coordination, helping readers grasp message flow, storage, ordering, and fault‑tolerance in a distributed streaming system.
The article examines five different retry mechanisms for ensuring reliable message delivery between payment and billing microservices, evaluates their pros and cons, and ultimately recommends the third solution as a cost‑effective, highly reliable approach achieving 99.99% consistency.
This article breaks down the design and source‑code implementation of RocketMQ's NameServer, detailing its core responsibilities, component interactions, and the seven Java classes that together provide network handling, connection management, routing, configuration, and startup logic.
The guide details how to build a distributed message‑governance platform for the Hello mobility service, covering unified SDK design, RocketMQ pitfalls, client and cluster health monitoring, risk mitigation, and a tiered microservice high‑availability architecture that uses circuit‑breaking, rate‑limiting, and pre‑heating to ensure resilient traffic handling.
Kafka 2.8 replaces the external ZooKeeper dependency with an internal Quorum controller (KRaft), enabling ZooKeeper‑free deployments that reduce resource usage, improve performance, support larger clusters, but currently lack some features such as ACLs, transactions, and partition reassignment, making it unsuitable for production yet.
This article compares the fundamentals and limitations of Redis list‑based queues with Kafka and Pulsar, explaining how partitions, cursors, replication, segment storage, and consumption models overcome Redis’s drawbacks and enable high‑performance, scalable, and highly available messaging systems.
This article examines Ctrip’s Hermes messaging system, tracing its evolution from a simple Mongo‑based queue to a broker‑centric, MySQL/Kafka hybrid architecture, and explains the design choices, performance optimizations, cluster management via lease‑based meta‑server, and lessons learned for building high‑throughput, low‑latency MQ solutions.
This guide walks through the complete process of installing RocketMQ 4.8.0, configuring a two‑master‑two‑slave asynchronous high‑availability cluster across two Linux servers, setting up the web console, and troubleshooting common pitfalls such as missing JDK, memory limits, firewall rules, and network address issues.
This article explains the role of message queues in handling traffic spikes, decoupling services, and ensuring reliability, compares popular MQ solutions such as RabbitMQ, Kafka, RocketMQ and ActiveMQ, and discusses their architectures, advantages, drawbacks, idempotency, ordering, high‑availability and scaling strategies.
This comprehensive guide covers the core concepts of distributed systems—including CAP theory, consistency models, transaction protocols like 2PC/3PC and TCC, Redis features, persistence options, expiration strategies, memory eviction policies, distributed locking with Redis and Zookeeper, as well as an overview of Kafka's architecture and reliability mechanisms.
The article compares basic in‑memory queues, Redis lists and streams, Kafka’s partitioned log architecture, and Pulsar’s compute‑storage separation, explaining each system’s core mechanisms, strengths, and limitations so readers can choose the most suitable message‑queue solution for their workloads.
This article explains the architecture, reliability mechanisms, dynamic configuration, monitoring, and alerting strategies of JD Daojia Open Platform's Business Message Queue (BMQ), illustrating how the system handles bidirectional communication, fault isolation, and scalable message processing for merchants.
This article introduces NSQ, an open‑source Go‑based distributed message queue, detailing its core components, scalable high‑availability deployment topology, backup and replay mechanisms, and monitoring practices to ensure reliable, fault‑tolerant message processing.
This article provides a detailed side‑by‑side analysis of four major distributed message‑queue systems—Kafka, RabbitMQ, RocketMQ, and ActiveMQ—examining them across seventeen criteria such as documentation, language support, protocols, storage, transactions, load balancing, clustering, management UI, availability, duplication, throughput, subscription models, ordering, acknowledgments, replay, retry, and concurrency.
This article examines cache consistency challenges in Redis-backed systems and compares several update strategies—including TTL, delayed double‑delete, cache‑aside, and message‑queue approaches—detailing their workflows, code examples, advantages, and drawbacks to guide backend developers toward reliable cache invalidation.
This guide walks through integrating RocketMQ 4.8.0 with Spring Boot 2.3.9, covering dependencies, configuration, code examples for normal, ordered, cluster/broadcast, and transactional messages, and demonstrates testing results with detailed explanations of each messaging mode.
This article details RocketMQ's 2020 Double‑11 transformation, covering its migration to a Kubernetes‑based operator, a MessageType‑indexed filter optimization that cut CPU usage by up to 32%, and the introduction of a POP consumption model that eliminates rebalance‑induced latency and improves reliability.
This guide walks through installing RocketMQ 4.5.1, launching NameServer and broker, testing with sample producer/consumer scripts, explains core components and console setup, shows Spring Boot starter integration with example code, and demonstrates sending normal, ordered, and transactional messages.
This article introduces NSQ, an open‑source Go‑based distributed message queue, detailing its high‑performance architecture, core components (nsqd, nsqlookupd, nsqadmin), recommended high‑availability deployment topology, backup and replay mechanisms, and essential monitoring practices for reliable backend operations.
The article investigates a performance bottleneck in Tesla's interface testing platform where large test tasks experience up to nine‑minute report delays, identifies the hidden latency in RocketMQ message dispatch to handlers, and proposes a redesign of channel scheduling that reduces execution time to under four minutes.
This article explores why reliable inter‑service communication is essential in microservice architectures, illustrates common pitfalls with real‑world examples, and presents five practical retry and persistence solutions—including fast retry, in‑memory queues, persistent queues, retry services, and pre‑notification—to improve message delivery reliability.
This tutorial explains RocketMQ's architecture—including producers, brokers, consumers, topics, tags, and consumption models—then walks through installing the service, configuring the environment, and integrating it with Spring Boot using Maven dependencies, YAML settings, and sample producer and consumer code.
This article introduces RabbitMQ, explains its core concepts and typical scenarios such as decoupling, asynchronous processing, and traffic shaping, and discusses the benefits and challenges of integrating a message queue into backend architectures.
This article explains how Apache Pulsar handles message retention, backlog quotas, and TTL policies, detailing the default behavior, configuration commands at the namespace level, practical examples, and troubleshooting tips for common production scenarios.
This article explains how to implement delayed message delivery in RabbitMQ using the delayed‑message‑exchange plugin and Spring Boot, covering the shortcomings of traditional approaches, configuration of exchanges and queues, sending messages with delay headers, and a test demonstrating a 6‑second delayed receipt.
This article explains why traditional approaches like Redis expiration, database polling, or JVM DelayQueue are inefficient for delayed messaging, and demonstrates how to use RabbitMQ's delayed‑message‑exchange plugin with Spring Boot to create, configure, send, and consume delayed messages, including full code examples and a test showing a 6‑second delay.
This article explores common pitfalls in RabbitMQ message delivery, explains persistence settings and the confirm mechanism, and why they alone can't ensure 100% reliability, then proposes a robust solution combining pre‑persisting messages to Redis, confirm callbacks, scheduled retries, and idempotent consumer design to achieve near‑zero loss.
This article explains how to integrate RabbitMQ into a Spring Boot application, covering dependency setup, connection configuration, message production and consumption, handling complex JSON messages, and ensuring both sending and receiving reliability through publisher confirms, return callbacks, and consumer acknowledgements.
This article provides a comprehensive guide to installing and configuring Apache RocketMQ, covering its history, core features, high‑availability architectures, detailed port planning, downloading, editing broker configuration files, creating data directories, starting NameServer and brokers, troubleshooting common issues, and deploying the web console for monitoring.
This article compares ActiveMQ, RocketMQ, and Kafka across multiple dimensions such as messaging models, API completeness, language support, throughput, latency, availability, message loss risk, documentation, community activity, and commercial backing, helping readers choose the most suitable queue for their backend needs.
This comprehensive guide explains Kafka's role as a message system, detailing topics, partitions, producers, consumers, replication, controller, ZooKeeper coordination, performance optimizations like sequential writes and zero‑copy, and practical recommendations for hardware, configuration, and cluster deployment.
This article explains RocketMQ consumer fundamentals, covering concurrent and ordered consumption modes, how to achieve idempotent processing with Redis locks, the load‑balancing strategy implemented by RebalanceImpl, and the complete pull‑process‑consume workflow including code examples.
This article examines RocketMQ's end‑to‑end reliability mechanisms, covering producer‑side sending strategies, broker storage guarantees, and consumer‑side consumption semantics to show how the system minimizes message loss in distributed environments.
This article provides a comprehensive introduction to Apache Kafka, covering its distributed publish‑subscribe architecture, its key components such as brokers, topics, partitions, producers, consumers, and ZooKeeper, as well as its advantages, drawbacks, storage mechanisms, partition assignment strategies, and reliability guarantees for high‑throughput big‑data streaming.
This guide walks through downloading, extracting, configuring, and starting RocketMQ 4.8 on a Kubernetes master, covering directory setup, configuration edits, and verification of the NameServer and Broker processes to complete a production‑ready message‑queue deployment.
Version 1.0.3 of the pure PHP coroutine Wind Framework introduces a new Chan class for intra-process coroutine messaging, enhances the message queue with Redis and Beanstalk drivers supporting massive consumer counts and timeout settings, and fixes several Beanstalk client command errors.
This article explains what RabbitMQ is, describes common scenarios such as decoupling services, improving asynchronous processing efficiency, and handling traffic spikes, and outlines the benefits and drawbacks of integrating a message queue into system architecture.
This article explains the role of message queues in asynchronous processing, application decoupling, traffic shaping and log handling, compares popular MQ products, details RabbitMQ concepts and common patterns, and discusses reliability, ordering, dead‑letter, TTL and clustering strategies.
The article compares three approaches—synchronous dual write, asynchronous dual write with a message queue, and CDC via Canal—to keep Elasticsearch and a relational database consistent for cross‑database aggregated full‑text search, outlining their steps, advantages, and drawbacks.
This article examines how to guarantee reliable message delivery in RabbitMQ by using durable queues, the confirm callback mechanism, pre‑persisting messages with Redis or a database, scheduled compensation tasks, and idempotent processing techniques such as optimistic locking and unique‑ID fingerprinting.
This article explains why message queues are essential for decoupling systems, enabling asynchronous processing and peak‑shaving, outlines their advantages and disadvantages, compares popular MQ products, and provides practical guidance on high availability, reliability, ordering, and architectural design.
This article explains the purpose of message systems, compares various solutions such as RabbitMQ, Redis, ZeroMQ, ActiveMQ, RocketMQ and Kafka, then details Kafka's design goals, core concepts, architecture, replication, retention policies, zero‑copy transfer, batching, and performance optimizations for high‑throughput distributed messaging.
RabbitMQ 3.8.10 has been released, introducing core server bug fixes such as preventing unlimited log segment growth when repeatedly polling a quorum queue with basic.get, and adding a new reject-publish max-length overflow policy for quorum queues, alongside other enhancements.
This article compiles essential interview‑style questions and detailed answers on distributed system fundamentals—including CAP and BASE theories, consistency models, distributed transactions, Redis features and persistence, Zookeeper coordination, Kafka architecture, and common design patterns for high‑concurrency scenarios.
This article explains RabbitMQ’s AMQP fundamentals—including servers, connections, channels, messages, exchanges, queues, routing keys, and virtual hosts—while covering reliable delivery, message idempotency, consumer flow control, TTL, dead‑letter handling, clustering modes, and high‑availability solutions such as HAProxy and Keepalived.
This article introduces Kafka’s fundamental role as a messaging system, explains topics, partitions, producers, consumers, replicas, consumer groups, and the controller, and explores its cluster architecture, performance optimizations like sequential writes and zero-copy, providing a comprehensive overview for building scalable data pipelines.
This article explains RabbitMQ's AMQP fundamentals, exchange types, reliable delivery mechanisms, idempotency strategies, consumer flow control, TTL and dead‑letter handling, as well as clustering, federation, HAProxy and Keepalived solutions for building a resilient messaging infrastructure.
Apache Kafka is a distributed, fault‑tolerant streaming platform that uses broker clusters, topic partitions, and replicated logs to provide publish/subscribe and queue messaging, configurable retention, strong ordering within partitions, producer acknowledgments, consumer groups, and performance optimizations such as batching and zero‑copy for real‑time data pipelines.
This article explains how Redis can be used as a lightweight message‑queue solution by leveraging its List, Pub/Sub, and Stream data structures, covering core commands, blocking consumption, acknowledgment mechanisms, consumer groups, and practical usage patterns for distributed systems.
This article provides an in‑depth overview of RabbitMQ, covering AMQP core concepts, exchange types, reliable delivery mechanisms, message idempotency, consumer flow control, dead‑letter handling, clustering modes, and high‑availability deployment strategies for robust backend systems.
This article provides a comprehensive guide to RabbitMQ, covering core AMQP concepts, exchange types, message reliability mechanisms, dead‑letter handling, QoS settings, and various high‑availability cluster modes such as mirrored, federated, and multi‑active deployments.
Apache RocketMQ 4.8.0 introduces extensive DLedger enhancements, including asynchronous pipeline processing, batch log replication, extensive chaos‑testing validation, preferred‑leader selection, and batch‑message support, delivering several‑fold throughput improvements, faster recovery from failures, and new functional capabilities for production‑grade messaging.
This article explains common message‑middleware use cases such as asynchronous processing, system decoupling, and traffic‑shaping, compares the performance of popular brokers, and provides a step‑by‑step guide with code examples for integrating RabbitMQ into a Spring Boot application.
This article explains how Redis can be used to build lightweight, high‑performance message queues by leveraging lists for simple queues, sorted sets for delayed delivery, Pub/Sub for multicast, and the Stream data structure for durable, scalable messaging, while also covering practical PHP implementations, acknowledgment, retry, graceful restart, and rate‑limiting techniques.
This article compares RabbitMQ and Apache Kafka by examining their internal designs, messaging models, ordering guarantees, routing, timing, retention, fault‑tolerance, scalability, and consumer complexity, then provides concrete guidance on when to choose each technology for real‑world systems.
JCQ, JD Cloud’s self‑developed cloud‑native message queue, evolved from a monolithic 1.0 version to a 3.0 architecture that separates compute and storage, introduces multi‑Raft scaling, dynamic resource allocation, and high‑availability features, offering flexible, low‑maintenance messaging for high‑concurrency workloads.
This article explains Kafka's multi-replica architecture, the role of leader and follower replicas, ISR mechanism, ack settings, and how misconfiguration of the __consumer_offsets topic can cause a full cluster outage, offering practical steps to restore high availability.
This article explains the concept and common use cases of delayed messages and compares five practical implementation methods—using Redis sorted sets, RabbitMQ delayed‑message exchange plugin, ActiveMQ scheduled messages, RocketMQ fixed delay levels, and a custom RocketMQ solution—detailing their workflows, advantages, and drawbacks.
This article explains how to build a highly available, high‑throughput, and horizontally scalable RocketMQ cluster by deploying multiple NameServers and using the Dledger mode to achieve fault‑tolerance, load distribution, and massive message handling for demanding business scenarios.
