This guide explains how to use Alibaba Cloud EventBridge to create and manage RocketMQ 4.0, 5.0, and self‑hosted Apache RocketMQ instances, covering architecture, API parameters, VPC and security settings, and step‑by‑step procedures for event‑driven integration and message routing.
vivo’s Internet Storage team details how it operates RocketMQ for low‑latency online services and Kafka for massive big‑data pipelines, outlines resource isolation, traffic balancing, intelligent throttling, and governance practices, and describes its migration from RabbitMQ and planned shift from Kafka to cloud‑native Pulsar.
This article walks through every stage of a RocketMQ message—how producers create and route messages, the storage mechanisms and zero‑copy techniques used by brokers, high‑availability modes, consumption models, ordering guarantees, and the automatic cleanup policies that finally retire messages.
This article explains the concept of idempotency, illustrates why repeated operations must yield identical results, and details how RocketMQ’s components—NameServer, Broker, Producer, and Consumer—can be designed with unique message IDs and acknowledgment mechanisms to achieve reliable, duplicate‑free messaging in backend systems.
Apache RocketMQ 5.0 introduces a stateless proxy, gRPC‑based multi‑language SDK with immutable APIs, enhanced error handling, SimpleConsumer, and pop consumption, and the article outlines the technical improvements, roadmap, and step‑by‑step guide for developers to set up the environment, understand the new protocols, and contribute language client implementations.
This article introduces the fundamentals of message middleware, outlines its core components, and compares RocketMQ and Kafka architectures to show how they achieve high‑availability through replication, leader election, and coordinated producer‑consumer interactions.
This tutorial explains why message queues are essential, compares popular MQ products, details RocketMQ's core components, and provides step‑by‑step instructions—including environment setup, broker configuration, firewall rules, and monitoring—to deploy a fault‑tolerant RocketMQ cluster on CentOS 7.
This article explains RocketMQ's load balancing process, including when rebalancing occurs, the impact on message consumption, common strategies such as average allocation and consistent hashing, and practical recommendations like minimizing client churn and choosing appropriate strategies for cloud‑native deployments.
This article explains how RocketMQ's message lifecycle can be fully observed using OpenTelemetry‑based metrics, covering producer, broker, and consumer stages, and shows practical monitoring, alerting, and troubleshooting practices for cloud‑native deployments.
This article provides an in-depth comparison of RabbitMQ, RocketMQ, and Kafka, detailing their core components, exchange types, message durability, acknowledgment mechanisms, TTL, dead‑letter queues, load balancing, ordering, transaction handling, high‑availability configurations, and practical solutions for common messaging challenges.
This article provides an in‑depth tutorial on RocketMQ, covering normal, ordered, delayed, transactional, and batch messages, their production and consumption patterns, retry mechanisms, message filtering, and dead‑letter handling, complete with Java code examples for each scenario.
This article explains RocketMQ's broker storage architecture, detailing the roles of CommitLog, ConsumeQueue, and IndexFile, and describes how messages are formatted, indexed, and written to disk using memory‑mapped files to achieve high throughput and reliability.
This article explains the concept of distributed transactions, presents typical microservice scenarios that generate them, and demonstrates a complete implementation using RocketMQ transactional messages and SEATA, including database schema, producer and consumer code, transaction listeners, and idempotent consumption handling.
This article explains RocketMQ's load‑balancing mechanisms, when rebalancing occurs, how different strategies like average allocation and consistent hashing affect consumer behavior, and provides practical recommendations to minimize message backlog and consumption spikes in cloud‑native environments.
This article explains how RocketMQ integrates with Prometheus and Grafana to provide comprehensive metrics, tracing, and logging, detailing the exporter architecture, deployment choices, span topology, dashboard examples, and ARMS‑based alerting for cloud‑native message‑queue observability.
This article explains the background of RocketMQ clusters, demonstrates typical Java consumer and producer usage, and introduces the AOP‑based HunterConsumer and HunterProducer components that simplify lifecycle management, configuration, and code reuse for backend developers working with messaging systems.
This article provides a comprehensive overview of RocketMQ messaging concepts—including synchronous, asynchronous and one‑way sending, ordered and delayed messages, transaction handling, batch processing, tag/SQL filtering, retry mechanisms, and dead‑letter queue management—accompanied by Java code examples for each scenario.
This article explains the concept of distributed transactions, presents typical micro‑service scenarios that generate them, and demonstrates a complete solution using RocketMQ transaction messages together with SQL table definitions and Java code for producers, transaction listeners, and consumers to achieve eventual consistency.
This article explains RocketMQ’s core components, the rocketmq‑remoting module, its Netty‑based protocol design, message structure, communication flow, and the Reactor multi‑thread model that together ensure high‑performance, reliable messaging in distributed backend systems.
RocketMQ 5.0 introduces a brand‑new unified API built on gRPC, refines client types, adds four message categories, pre‑fetches topic routes, embeds a telemetry channel, and provides OpenTelemetry‑based tracing and metrics, all while offering a cloud‑native SDK repository for developers.
This article details the challenges of scaling RabbitMQ, the evaluation of RocketMQ versus Pulsar, the design of a new messaging middleware platform with high availability, performance, and rich features, and the step‑by‑step migration strategy that enabled seamless, low‑cost transition for massive business traffic.
This article explains how RocketMQ implements distributed transaction messages using a two‑phase commit model, detailing the problems with RPC and ordinary MQ, the step‑by‑step workflow, relevant code snippets, and how consistency is ensured across microservice subsystems.
This article explains how RocketMQ implements scheduled and delayed messages using fixed delay levels, the internal storage flow, code examples for setting delay levels, and advanced techniques such as RocksDB integration to achieve arbitrary time precision for delayed delivery.
This article explains the challenges of point‑to‑point data sync, introduces RocketMQ Connect as a cloud‑native solution that decouples upstream and downstream, details its architecture, connectors, REST API, metrics, deployment modes, and provides a step‑by‑step guide to building custom connectors for use cases such as CDC, data lakes, and system migration.
This article provides a detailed tutorial on installing and configuring RocketMQ on a Mac M1 using Docker, covering source‑code and Docker deployments, container setup, broker configuration, dashboard launch, message testing, and common troubleshooting tips.
This article details how to set up Apache RocketMQ on a Mac M1 using Docker, covering source‑code deployment, Docker container configuration, broker and nameserver setup, dashboard launch, message testing, and troubleshooting common connection issues.
This article explains the design and implementation steps for a multi‑active (geo‑distributed) system, covering data‑center segmentation, traffic scheduling, RPC routing, database and Redis sharding, distributed IDs, message queue handling, job coordination, and cut‑over procedures.
This article examines a puzzling 'prevent gc' comment in RocketMQ source, explains how inserting Thread.sleep(0) creates a JVM safepoint to avoid long GC pauses, explores counted vs uncounted loops, and demonstrates practical code modifications to improve performance.
This article explains how RocketMQ creates and manages threads, covering single‑thread techniques, the ServiceThread abstract class, ThreadPoolExecutor fundamentals, custom thread‑pool wrappers, and the critical role of descriptive thread names for debugging and performance monitoring.
This article investigates the mysterious Thread.sleep(0) call found in RocketMQ source, explaining how it interacts with JVM safepoints, why it appears in counted loops, and how changing loop indices from int to long can avoid long stop‑the‑world pauses, backed by code examples and references.
The article walks through an interview scenario on RocketMQ, explaining when increasing consumer instances speeds up message consumption, the conditions that cause consumer pull delays, and the six built‑in queue‑allocation strategies with code examples and practical trade‑offs.
This article examines the puzzling 'prevent gc' comment and Thread.sleep(0) call in RocketMQ’s source, explains how it leverages JVM safepoint mechanics to trigger garbage collection, discusses counted vs uncounted loops, and demonstrates practical code modifications to improve performance.
This comprehensive guide explains the core concepts, deployment modes, evaluation criteria, and detailed pros and cons of the four major message‑queue platforms—Kafka, RabbitMQ, RocketMQ, and ActiveMQ—helping engineers make informed decisions for interview preparation and system design.
This article traces RocketMQ's transformation from a traditional message queue to a cloud‑native, ultra‑fusion platform that integrates messaging, event‑driven architecture, and streaming, highlighting its historical stages, architectural upgrades, multi‑language SDKs, stateless consumption, and future cloud‑native trends.
This article explains the role of RocketMQ's NameServer, detailing its registration process, data structures, periodic heartbeat checks, failure cleanup, and how producers and consumers retrieve routing information to send and receive messages efficiently.
This article explains why delayed queues are needed for scenarios like unpaid orders or auto‑generated comments, compares built‑in and third‑party solutions, and provides a detailed design and implementation guide for a Redis‑based delayed‑queue service, including version‑1.0 features, version‑2.0 optimizations, and multi‑node deployment.
The article explains fundamental concepts, common use cases, and selection criteria for four popular message‑queue platforms—Kafka, RabbitMQ, Pulsar, and RocketMQ—detailing each system’s architecture, strengths, and drawbacks so readers can match workload, scalability, ordering, latency, and operational needs to the most suitable middleware.
To address RabbitMQ’s scalability, reliability, and feature limitations, Vivo’s middleware team evaluated RocketMQ and Pulsar, selected RocketMQ, and built a next‑generation message middleware platform with an AMQP‑proxy gateway, metadata services, and high‑availability mechanisms, enabling seamless, high‑throughput migration and richer messaging capabilities.
This article explains RocketMQ's push and pull consumption modes, shows how its pseudo‑push implementation uses long polling to balance real‑time delivery and consumer pressure, and walks through the core source‑code mechanisms that hold and resume pull requests.
This article explains how RocketMQ reduces message loss and duplicate consumption through synchronous sending, retry mechanisms, multi‑master brokers, synchronous disk flushing, master‑slave replication, at‑least‑once delivery, and idempotent processing techniques.
This article introduces the core concepts, components, exchange types, reliability mechanisms, ordering, delay, transaction, high‑availability, and load‑balancing strategies of three popular message‑queue systems—RabbitMQ, RocketMQ, and Kafka—while also discussing common challenges such as message ordering, delayed consumption, reliability, idempotence, and backlog handling.
This article explains the challenges of maintaining atomicity across multiple downstream services in distributed systems and details how RocketMQ's transactional message feature provides a two‑phase commit mechanism, lifecycle, implementation details, and practical usage to achieve eventual consistency.
This article provides a comprehensive overview of the core components, exchange types, TTL, confirmation mechanisms, dead‑letter queues, and reliability features of RabbitMQ, RocketMQ, and Kafka, along with practical guidance on ordering, delayed consumption, transaction handling, high availability, load balancing, and message deduplication in distributed messaging systems.
This article surveys common delayed‑message implementations—including external storage, RocksDB, Redis, and open‑source MQs like RocketMQ, Pulsar, and QMQ—detailing their architectures, advantages, disadvantages, and practical considerations for distributed asynchronous messaging systems.
This article provides a comprehensive overview of modern message‑queue systems, detailing RabbitMQ components and exchange types, RocketMQ’s core architecture and high‑availability features, and Kafka’s fundamental concepts, controller mechanisms, and consumer rebalance strategies, while also covering reliability, ordering, and dead‑letter handling techniques.
This article explains the fundamental components and mechanisms of RabbitMQ, RocketMQ, and Kafka—including exchanges, TTL, confirm and return listeners, consumer ACK/NACK, dead‑letter queues, delay and ordered messages, transactional flows, high‑availability setups, load balancing, partitioning, leader election, offset handling, and practical solutions to common messaging challenges.
This article surveys common delayed‑message solutions in distributed asynchronous messaging, evaluates implementations based on external storage, databases, RocksDB, Redis, and open‑source MQs like RocketMQ, Pulsar and QMQ, and discusses their advantages, drawbacks, and practical design considerations.
This article provides an in‑depth technical guide to three major message‑queue systems—RabbitMQ, RocketMQ, and Kafka—covering their core components, exchange and routing mechanisms, durability features, consumer acknowledgment, dead‑letter handling, load balancing, and practical strategies for reliability, ordering, and scaling in distributed backend applications.
Facing scaling, high‑availability, and feature limitations of RabbitMQ, the vivo middleware team evaluated RocketMQ and Pulsar, chose RocketMQ, and detailed a seamless migration strategy—including a message gateway, metadata mapping, high‑performance push, consumption controls, and operational benefits that boosted TPS and reduced resource usage.
This article analyzes the challenges of distributed transactions in multi‑node systems and compares various solutions—including 2PC, 3PC, TCC, Saga, local message tables, and MQ‑based approaches—detailing their mechanisms, trade‑offs, and practical implementations with concrete examples from order and cart services.
This article details the challenges of scaling RabbitMQ, the evaluation of RocketMQ versus Pulsar, the architectural design of a new high‑availability message middleware platform, and the step‑by‑step smooth migration strategy that enables higher throughput, richer features, and lower operational costs.
vivo’s Internet Middleware Team replaced its RabbitMQ service with a RocketMQ‑based platform, building an AMQP‑Proxy gateway and metadata layer to enable seamless, zero‑downtime migration while achieving over 100,000 TPS, billion‑message capacity, 50% resource savings, and advanced features such as transactions, ordered and delayed messaging, and tracing.
This article explains the concept, scenarios, advantages, and implementation details of Apache RocketMQ's timed/delay messages, including the TimerWheel algorithm, service workflow, a financial payment‑timeout use case, and practical steps for creating and consuming timed messages.
This article surveys common delayed‑message solutions—including database, RocksDB, Redis, and open‑source MQs like RocketMQ, Pulsar, and QMQ—explaining their architectures, advantages, drawbacks, and practical considerations for building reliable distributed asynchronous messaging.
This article explains the fundamentals of message queues, compares Kafka, RabbitMQ, and RocketMQ in terms of architecture, key concepts, advantages, and drawbacks, and provides practical guidance on selecting the most suitable queue for different application scenarios such as high‑throughput logging, financial transactions, or small‑scale services.
The article examines five backend techniques for automatically closing unpaid e‑commerce orders—scheduled tasks, RocketMQ delayed messages, RabbitMQ dead‑letter queues, a time‑wheel algorithm, and Redis key‑expiration listeners—detailing their principles, advantages, drawbacks, and sample Java code implementations.
This article explains the fundamentals of message middleware, covering its architecture, components, communication models, advantages, common protocols, major open‑source solutions, detailed feature comparisons, and practical guidance for selecting the most suitable message queue based on system scale, business needs, performance, high availability, and operational considerations.
This article explains how RocketMQ’s ordinary messages meet the core demands of business integration, detailing their architecture, lifecycle, key features, typical use cases, and practical code examples for sending and consuming messages.
This article provides a comprehensive introduction to three major message‑queue systems—RabbitMQ, RocketMQ, and Kafka—explaining their core components, exchange types, delivery guarantees, confirmation mechanisms, dead‑letter handling, high‑availability designs, load‑balancing strategies, and common troubleshooting techniques.
RocketMQ consumers initialize by validating configuration, creating subscription data, establishing a client instance, loading offsets, and starting services before registering with the client factory, after which they can receive messages via push (default low‑latency) or pull modes, support ordered processing, and handle acknowledgments with configurable retries and dead‑letter handling.
The article explains how to automatically close unpaid e‑commerce orders by comparing five approaches—simple scheduled tasks, RocketMQ delayed messages, RabbitMQ dead‑letter queues, a time‑wheel algorithm, and Redis key‑expiration listeners—detailing their advantages, drawbacks, and providing Java code examples for each method.
This article introduces Java's CompletableFuture, compares it with Future, explains its core APIs, demonstrates common usage patterns—including creation, result retrieval, chaining, exception handling, and task combination—and shows a practical integration example within RocketMQ's message storage workflow.
The article compares five practical approaches for automatically closing unpaid orders—scheduled tasks, RocketMQ delayed messages, RabbitMQ dead‑letter queues, a time‑wheel algorithm, and Redis key‑expiration listeners—detailing their principles, advantages, limitations, and providing concrete Java code examples for each method.
This article evaluates common delayed‑task solutions such as RocketMQ, Pulsar, Redisson DelayQueue, Redis expiration listeners, RabbitMQ dead‑letter queues, and time wheels, highlighting their drawbacks and recommending reliable approaches for accurate order‑closing tasks in e‑commerce systems.
This article explains RocketMQ’s overall architecture and details how producers and consumers achieve load balancing through routing synchronization, queue selection, and rebalance strategies, illustrated with code examples and a case study of assigning consumption to a specific machine.
The paper presents Pigeon, a lightweight, bus‑style routing service for live‑streaming activities that lets completed modules publish packets to a configurable dispatcher, enabling asynchronous, SDK‑driven composition without code changes, supporting automatic registration, high‑throughput RocketMQ delivery, and future FaaS/BaaS extensions.
The article explains how to redesign a high‑traffic user‑registration flow by introducing an asynchronous queue layer, detailing the broker architecture, commitlog, consumeQueue, page‑cache, mmap, topic/tag routing, high‑availability strategies, and the role of a nameserver in a RocketMQ‑style system.
This guide demonstrates how to integrate RocketMQ with Spring Boot, covering dependency setup, configuration, and two message‑filtering techniques—tag‑based and SQL‑92 expression filtering—complete with consumer and producer code examples, testing endpoints, and visual diagrams illustrating each approach.
This article explains how RocketMQ and RabbitMQ guarantee message reliability by covering production, storage, and consumption stages, introducing confirm mechanisms, persistence settings, manual acknowledgments, and compensation strategies such as message database storage to achieve near‑zero data loss in distributed systems.
This article explores five practical techniques for automatically closing unpaid e‑commerce orders—ranging from simple scheduled tasks to advanced RocketMQ delay queues, RabbitMQ dead‑letter queues, time‑wheel algorithms, and Redis expiration listeners—detailing their mechanisms, trade‑offs, and implementation code.
This article introduces and compares four widely used open-source distributed message-queue systems—Kafka, ActiveMQ, RabbitMQ, and RocketMQ—detailing their architectures, deployment requirements, features, high-availability solutions, advantages, disadvantages, and provides guidance for selecting the appropriate middleware in large-scale applications.
This article explains how RocketMQ tackles registration latency, synchronous coupling, and traffic spikes by introducing an intermediate queue, designing a persistent high‑availability broker, leveraging Linux page cache and memory‑mapped files, and employing topics, tags, and sharding to enable efficient asynchronous processing and scalable consumption.
The article explains how RocketMQ tackles growing business traffic by introducing an asynchronous broker layer, using commitlog and consumeQueue files, page‑cache, mmap, topic/tag routing, and a nameserver to achieve high‑throughput, low‑latency, and highly available message delivery.
This article analyzes why ZuanZuan built its own RocketMQ transaction‑message solution, compares the custom implementation with the community edition’s two‑phase commit approach, explains the underlying principles, sending flow, failure handling, and unknown‑state processing, and summarizes the trade‑offs of each design.
This article reviews common delayed‑message implementation approaches—including external storage, RocksDB, Redis, and built‑in solutions in open‑source MQs like RocketMQ, Pulsar, and QMQ—detailing their architectures, advantages, drawbacks, and practical considerations for distributed systems.
This article explains the three main reasons for using message queues—decoupling, asynchrony, and peak‑shaving—then compares popular MQ products, dives deep into RocketMQ’s components, models, reliability, ordering, filtering, delay, transactions, dead‑letter handling, high‑availability, storage, load balancing, and long‑polling, providing a comprehensive guide for backend engineers.
Message‑queue middleware such as RabbitMQ, RocketMQ, ActiveMQ, and Kafka introduces challenges like ordering, loss, duplication, back‑pressure and delayed delivery, which can be mitigated by using single‑consumer queues or partitioning, enabling acknowledgments and replication, applying idempotent identifiers, scaling consumers, and employing dead‑letter or scheduling mechanisms.
An in‑depth guide compares the four major message‑queue middleware—Kafka, RabbitMQ, RocketMQ and ActiveMQ—detailing their architectures, modes, evaluation criteria, strengths, weaknesses, and selection considerations to help engineers and interviewees answer critical MQ interview questions and make informed technology choices.
This article surveys various delayed‑queue solutions—including Kafka pause/resume, RocketMQ delay levels, Redis/Redisson zset timers, Youzan's custom design, Netty's HashedWheelTimer, and a Kafka‑based time‑wheel—analyzing their architectures, pros and cons, and offering practical implementation guidance.
The article surveys common delayed‑message solutions in distributed MQ systems—including external storage, RocksDB, Redis, thread‑based scanning, and the designs of RocketMQ, Pulsar, and QMQ—detailing their architectures, advantages, and drawbacks for backend developers.
This article explains the eight situations that trigger flow control in RocketMQ—four on the broker side and four on the consumer side—detailing their causes, relevant code paths, and practical measures such as broker scaling or consumer optimization to resolve them.
This article analyzes the problems caused by multiple independent RocketMQ clusters, outlines the current cluster architecture, details pre‑migration preparations, compares two migration schemes (one not recommended and one recommended), and summarizes the benefits of consolidating clusters into a single, well‑managed deployment.
This article explains why RocketMQ guarantees at‑least‑once delivery, describes the three typical duplication scenarios, evaluates simple idempotent approaches, discusses transactional and non‑transactional deduplication solutions—including a Redis‑based method—and outlines their advantages, limitations, and practical recommendations for achieving exactly‑once processing in distributed systems.
The article explains RocketMQ’s load‑balancing for producers and consumers, detailing how producers obtain routing info from the NameServer, select MessageQueues via round‑robin while avoiding failed brokers, and how consumers periodically rebalance queue allocation using strategies such as AllocateMessageQueueAveragely to ensure even distribution and customizable assignment.
This guide walks through downloading the RocketMQ source, importing it into IntelliJ IDEA, configuring ROCKETMQ_HOME, adjusting configuration files, launching the NameServer and Broker, and testing message production and consumption to establish a complete local debugging environment.
This article explains how to guarantee almost 100% reliability of messages in distributed systems by examining RocketMQ’s three‑stage reliability model and RabbitMQ’s confirm, persistence, and manual‑ack mechanisms, plus a database‑backed compensation strategy for extreme failure cases.
The Spring Cloud Alibaba 2021.0.1.0 release, built on Spring Boot 2.6.3 and Spring Cloud 2021.0.1, introduces upgraded Nacos client, RocketMQ 4.9.2 with batch and async support, Sentinel 1.8.3 enhancements for FeignClient, Spring Config import support, and a clarified version‑numbering scheme aligning with Spring Cloud.
The LUBAN MQ platform introduces a gray‑deployment extension for RocketMQ that isolates gray traffic by assigning configurable gray queues, adds a graySubscriptionTable and grayFlag in broker and Namesrv, uses shared consumer groups with a custom rebalance strategy, and ensures loss‑less offset merging and seamless switch‑over.
This article explains the fundamentals of message queues, their typical use cases such as decoupling, asynchronous processing, and traffic shaping, compares popular MQ products, and outlines best practices for high availability, loss prevention, idempotency, ordering, backlog handling, and expiration management.
This article examines various delayed‑message implementations in distributed message‑queue systems, comparing external‑storage approaches using databases, RocksDB and Redis, and reviewing built‑in solutions in open‑source MQs such as RocketMQ, Pulsar and QMQ, while discussing their advantages, drawbacks and design considerations.
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.
This article explains why message queues are used, outlines common scenarios such as decoupling, asynchronous processing and traffic shaping, compares major MQ products, and provides practical guidance on high availability, preventing loss, duplicate consumption, ordering, backlog handling, and expiration.
This article explains the typical problems encountered with message queues, such as message loss, duplicate delivery, and distributed transaction handling, and details various solutions including local message tables, MQ‑based transactions, and the specific mechanisms used by RocketMQ, Kafka, and RabbitMQ to ensure reliability and consistency.
This article surveys various delayed queue implementations—including Kafka, RocketMQ, Redis (Redisson), and Netty’s HashedWheelTimer—examining their design principles, advantages, drawbacks, and integration strategies, and proposes a unified micro‑service architecture leveraging Kafka topics, Redis ZSETs, and thread‑pool optimizations for reliable message scheduling.
This article explains the Lakehouse architecture, its required features, the evolution of big‑data stacks, and provides a detailed, hands‑on guide for constructing a Lakehouse using RocketMQ (Connector & Stream) and Apache Hudi, including configuration, deployment, and sample code.
This article explains the reasons for selecting Apache RocketMQ—its synchronous double‑write safety, strong performance under massive multi‑topic loads, rich features like transactional and delayed messages, active community support, and demonstrates how to leverage Java’s SPI mechanism to build a configurable, dynamically adjustable client architecture.
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 explains the challenges of at‑least‑once delivery in distributed message middleware like RocketMQ, examines simple and concurrent deduplication techniques, and presents both transaction‑based and non‑transactional exactly‑once solutions using database tables or Redis, along with practical Java code examples.
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.
This article details the investigation of frequent RocketMQ timeout errors in a company's procurement service, identifies misconfigured brokerIP2 and unstable master‑slave network as the root causes, and provides step‑by‑step remediation procedures to restore reliable message delivery.
