This article explains how to use RabbitMQ's delayed message plugin, covering its features, installation on multiple platforms, configuration steps, and complete Java Spring Boot code examples for producing and consuming delayed messages in a backend system.
This article provides a detailed tutorial on using Spring Cloud Sleuth with Zipkin to monitor and troubleshoot distributed micro‑service calls, covering concepts, configuration, Maven dependencies, YAML settings, controller examples, RabbitMQ integration, logback adjustments, and alternative tracing solutions.
This comprehensive guide walks you through RabbitMQ fundamentals, deployment on macOS, core concepts such as exchanges and routing, detailed code examples for producers and consumers in Java, and advanced features like TTL, dead‑letter queues, and manual acknowledgments, helping you bridge theory and practice in message‑queue systems.
This article describes how combining Redis and RabbitMQ to implement an asynchronous cache refresh mechanism significantly improves interface performance and stability for a high‑traffic used‑car platform, meeting strict tp99 latency requirements while maintaining data freshness.
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 chronicles the author's four‑stage evolution with message queues—from early experiments with ActiveMQ, through Redis and RabbitMQ, to MetaQ and finally RocketMQ—highlighting practical challenges, architectural decisions, performance tuning, and insights for building robust, high‑throughput backend systems.
This guide explains RabbitMQ clustering concepts, compares classic and mirrored modes, provides step‑by‑step commands to set up a multi‑node RabbitMQ cluster, and shows how HAProxy and Keepalived can be combined to achieve true high‑availability and load balancing.
This article describes how to design and implement a gray (canary) messaging capability for RabbitMQ and Kafka, covering background, gray scenarios, two consumption strategies, and detailed production and consumption flows with code snippets for header tagging, requeue handling, and consumer group management.
The article evaluates common approaches for implementing delayed tasks such as order‑closing, critiques unreliable methods like Redis expiration listeners and RabbitMQ dead‑letter queues, and recommends robust solutions like RocketMQ, Pulsar, or Redisson delay queues with proper compensation mechanisms.
This article introduces four essential open‑source technologies—Elastic, MariaDB, RabbitMQ, and Redis—explaining their core features, typical use cases such as search, relational data, messaging, and caching, and why they are popular choices for building scalable backend systems.
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 demonstrates how to use RabbitMQ's delayed‑queue plugin together with Spring Boot to create a lazy exchange, configure a durable queue, send messages with a specified delay, and consume them, providing complete Java code examples and explaining why this approach outperforms traditional Redis, database polling, or JVM DelayQueue solutions.
This article explains why RabbitMQ is used for decoupling, asynchronous processing, and traffic shaping, describes its architecture and core components such as broker, exchange, queue, binding, routing key, vhost, producer, consumer, and channel, and outlines its six operating modes with illustrative diagrams.
This article compares Kafka and RabbitMQ across six practical scenarios—message ordering, routing, delayed processing, persistence, error handling, and throughput—explaining each system's strengths and weaknesses to help engineers make an informed queue‑selection decision.
This article explains how to answer interview questions about guaranteeing 100% message delivery in MQ middleware such as Kafka, RabbitMQ, or RocketMQ, covering system decoupling, message lifecycle stages, reliability mechanisms, idempotent consumption, unique ID generation, and handling message back‑pressure.
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 explains what message queues are, outlines key application scenarios such as asynchronous processing, message distribution, and traffic shaping, compares popular solutions like ActiveMQ, RabbitMQ, RocketMQ, and Kafka across performance and reliability dimensions, and provides practical guidance for selecting the most suitable queue for different business needs.
This tutorial explains how to build a lightweight log management module by defining a custom @SystemLog annotation, using Spring AOP to capture method execution details, and sending the log data through RabbitMQ via Spring Cloud Stream for asynchronous persistence in a database.
This article explains how to configure RabbitMQ queue priority, defines reusable PHP base, service, and client classes, and demonstrates running the code to send and consume prioritized messages using the PhpAmqpLib library.
This tutorial explains the producer‑consumer principle using RabbitMQ, provides a reusable PHP base class for queue connections, and demonstrates how to create service and client classes to send and receive messages, followed by step‑by‑step instructions to run the code and view the queue.
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.
This tutorial walks through downloading compatible Erlang versions, installing RabbitMQ on Windows, starting the service, verifying its status, and accessing the management UI with default credentials, providing a complete setup for backend development with RabbitMQ.
This article introduces RabbitMQ as an open‑source AMQP‑based message broker written in Erlang, outlines its major advantages and drawbacks, and explains core concepts such as producers, exchanges, queues, and consumers for backend developers.
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 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 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 guide walks through installing the Dapr JavaScript SDK, configuring a RabbitMQ pub/sub component, injecting DaprClient and DaprServer into a NestJS project, creating a custom @DaprPubSubSubscribe decorator, and running a complete end‑to‑end demo that publishes and consumes page‑view events.
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 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.
This article compares delayed tasks with scheduled tasks, explains their differences, and presents five practical Java solutions—including database polling, JDK DelayQueue, Netty time wheel, Redis implementations, and RabbitMQ—detailing each approach's design, code examples, advantages, and drawbacks.
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.
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 comprehensive guide explores RabbitMQ fundamentals, messaging patterns, performance metrics, AMQP concepts, exchange types, advanced features, environment setup, and hands‑on Java code examples, helping developers understand and apply RabbitMQ in production systems.
The article examines various ways to implement precise delayed tasks such as order‑closure timers in e‑commerce, comparing message‑queue delayed delivery, Redisson DelayQueue, Redis expiration listening, RabbitMQ dead‑letter queues, and time‑wheel structures, and recommends the most reliable approaches.
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.
The article explains why common shortcuts such as Redis key‑space expiration notifications, RabbitMQ dead‑letter queues, and non‑persistent time wheels are unreliable for implementing delayed tasks, and recommends using proper message‑queue solutions like RocketMQ, Pulsar, or Redisson DelayQueue with compensation mechanisms.
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.
The article explains why using Redis key‑expiration notifications or RabbitMQ dead‑letter queues for delayed task execution is risky, compares several common approaches, and recommends reliable solutions such as dedicated message‑queue schedulers, Redisson delay queues, or time‑wheel implementations with proper compensation mechanisms.
This article explains what dead letters are in RabbitMQ, outlines their common causes, and provides a complete Spring Boot configuration and code examples for setting up dead‑letter exchanges, queues, TTL handling, message rejection, and consumer processing to reliably manage undeliverable messages.
The article evaluates common delayed‑task implementations such as message‑queue scheduling, Redis expiration listening, RabbitMQ dead‑letter queues, time wheels, and Redisson delay queues, highlighting their drawbacks and recommending robust backend approaches for reliable order‑closure processing.
This article explains how to design and operate a RabbitMQ cluster capable of handling millions of messages per second by describing RabbitMQ fundamentals, Google‑scale deployment, sharding and consistent‑hash plugins, high‑availability mirroring, federation, and integration with Spring AMQP, while also covering practical deployment scenarios and performance trade‑offs.
This article examines why using Redis key‑space expiration listeners, RabbitMQ dead‑letter queues, and in‑memory time wheels are unreliable for precise delayed‑task execution, and recommends more robust solutions such as dedicated message‑queue delay features and Redisson DelayQueue with fallback mechanisms.
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 what a Message Queue (MQ) is, why it is used for traffic shaping, system decoupling, and asynchronous processing, reviews popular MQ products such as Kafka, ActiveMQ, RocketMQ and RabbitMQ, and details RabbitMQ's core concepts and advanced features like dead‑letter, delayed, idempotent, priority, and lazy queues.
The article compares five delayed‑task implementations—database polling, JDK DelayQueue, Netty time wheel, Redis sorted sets, and RabbitMQ dead‑letter queues—detailing their concepts, Java code examples, advantages, and drawbacks to help developers choose the most suitable approach for order‑timeout scenarios.
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 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.
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 demonstrates how to build a real‑time messaging system for a space‑station scenario using RabbitMQ and Spring Boot, covering configuration of direct and fanout exchanges, scheduling updates, one‑to‑one chat, and broadcast messaging with complete Java code examples.
This article explains how to prevent message loss in RabbitMQ by using producer transaction mechanisms, confirm mode, message persistence, and database‑backed compensation, and also details consumer‑side manual acknowledgments to achieve reliable end‑to‑end delivery.
This article explains the three stages where RabbitMQ messages can be lost and presents a comprehensive reliability strategy—including producer confirm mode, durable exchanges/queues, message persistence, database‑backed compensation, and manual consumer acknowledgments—to achieve virtually loss‑free message delivery.
The article explains how to achieve near‑zero message loss in RabbitMQ by using producer confirm mode, persisting exchanges, queues and messages, storing outbound messages in a database, and switching consumers to manual acknowledgment with proper retry and idempotency handling.
This article compares three popular open‑source message brokers—RabbitMQ, Kafka, and Redis—examining their scalability, persistence, consumer models, and ideal use cases to help developers select the most suitable asynchronous communication solution for their microservice architecture.
This article compares several approaches to implementing delayed tasks in Java, including Quartz polling, JDK DelayQueue, Netty's HashedWheelTimer, Redis sorted sets and keyspace notifications, and RabbitMQ delayed queues, analyzing their principles, code examples, advantages, and drawbacks for order timeout handling.
This article compares Kafka and RabbitMQ across six real‑world scenarios—message ordering, routing, timeout handling, persistence, error handling, and throughput—to help engineers decide which message queue best fits their system requirements.
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.
This article explains how to replace inefficient traditional delay mechanisms with RabbitMQ's delayed‑queue plugin, showing step‑by‑step configuration, producer and consumer code in Spring Boot, and demonstrates successful delayed message delivery using real‑world examples such as auto‑confirm orders and ticket reservations.
This guide walks through setting up Spring Cloud Stream with RabbitMQ, covering supported binders, core components, configuration, code examples for producers and consumers, consumer groups, and message partitioning to build scalable, event‑driven microservices.
This article explains why traditional delayed‑message solutions like Redis expiration, database polling, or JVM DelayQueue are inefficient, and demonstrates how to use RabbitMQ's delayed‑queue plugin with Spring Boot to configure exchanges, send delayed messages, and consume them reliably.
This article explains why traditional delay solutions like Redis expiration, database polling, or JVM DelayQueue are inefficient, then demonstrates step‑by‑step how to configure RabbitMQ’s delayed‑queue plugin, create exchanges, queues, and bindings in Spring Boot, and send and consume delayed messages with code examples.
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 explains why using scheduled tasks for closing timed‑out orders is problematic, demonstrates how to use RabbitMQ dead‑letter queues and the x‑delay‑message plugin to achieve accurate order timeout handling, and provides complete Spring Boot configuration and code examples with testing and limitations.
The article explains how to achieve 100% message delivery and consumption in MQ systems by covering producer acknowledgments, broker persistence mechanisms, and consumer idempotency, with detailed comparisons of RabbitMQ and Kafka implementations and configuration tips.
This tutorial explains how to use Alibaba Cloud EventBridge together with MNS and RabbitMQ to implement message routing, multi‑active messaging, and protocol adaptation, providing detailed architecture, resource setup, step‑by‑step lab instructions, verification methods, and a summary of advantages.
This article explains step‑by‑step how Spring Boot’s RabbitAutoConfiguration sets up connection factories, RabbitTemplate, listener containers, and lifecycle management to enable @RabbitListener‑driven message processing, complete with code examples and the underlying bean registration flow.
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 how to guarantee that messages are never lost when using MQ middleware such as Kafka, RabbitMQ, or RocketMQ, outlines the key interview points, and provides practical design patterns, detection mechanisms, idempotency, and scaling strategies for reliable message delivery.
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 explains the fundamentals of RabbitMQ, compares normal and mirrored cluster modes, details Google’s large‑scale test setup, and walks through advanced plugins such as sharding, consistent‑hash exchange, federation, and high‑availability strategies for achieving million‑level message throughput.
This article explains how to horizontally scale RabbitMQ clusters, use sharding and federation plugins, configure mirror queues and other high‑availability features, and apply practical patterns such as confirms, retries, and delayed delivery to achieve million‑level message throughput in production environments.
This article explains how to horizontally scale RabbitMQ clusters to handle millions of messages per second by leveraging cluster modes, mirror queues, sharding plugins, consistent‑hash exchanges, federation, and high‑availability configurations, while also covering practical scenarios such as retries, delayed tasks, and Spring AMQP integration.
This article explains how to horizontally scale RabbitMQ clusters, use sharding and federation plugins, and configure high‑availability and reliability features to achieve million‑level message throughput in demanding production environments.
The article explains a typical payment‑timeout scenario and compares four backend solutions—database polling, Java DelayQueue, Redis sorted set, and RabbitMQ TTL with dead‑letter exchange—detailing their mechanisms, advantages, and drawbacks for implementing delayed task cancellation.
This article explains how to achieve near‑zero message loss in RabbitMQ by using producer confirm mode, persisting exchanges, queues and messages, storing messages in a database for compensation, and switching consumers to manual acknowledgment, complete with Java code examples.
This article explains how to use Alibaba's open‑source Canal middleware to capture MySQL binlog changes and forward them via RabbitMQ for real‑time data synchronization, covering server installation, configuration, testing, and a Java client implementation.
This article explains the purpose of message queues, outlines RabbitMQ's architecture, describes common MQ patterns such as simple, work, fan‑out, direct, and topic, and discusses reliability features like transactions, confirms, dead‑letter queues, TTL, clustering, and ordering guarantees.
This article explains why message queues are used, outlines core interview questions, analyzes interviewers' expectations, compares major MQ products, and provides practical guidance on selecting the right queue for different scenarios.
This article explains RabbitMQ's advanced reliability features, including how to prevent message loss with confirmCallback and returnCallback, configure TTL and dead‑letter queues, and provides practical code samples and interview‑style Q&A for backend developers.
This article explains the difference between synchronous and asynchronous microservice communication, outlines the benefits of async messaging, and compares three popular message brokers—RabbitMQ, Kafka, and Redis—based on scale, persistence, and consumer patterns, offering guidance on selecting the right broker for various use cases.
This article explains how to implement scheduled tasks in a Spring Boot application using RabbitMQ delayed queues, detailing the use of dead‑letter exchanges, message TTL settings, queue configuration, and Java code examples for publishing and consuming delayed messages.
This article explains how to replace database‑polling scheduled jobs in e‑commerce systems with RabbitMQ delayed queues by leveraging dead‑letter exchanges and message TTL, providing step‑by‑step configuration, code examples, and deployment details for Spring Boot applications.
This article explains how to achieve high‑reliability message delivery in RabbitMQ by using the confirm publish mechanism, persisting exchanges, queues and messages, storing outbound messages in a database for compensation, and switching consumers to manual acknowledgment to prevent loss in various failure scenarios.
This guide explains how to achieve 99.999999% reliability in RabbitMQ message delivery by using publisher confirms, persisting exchanges/queues/messages, storing outbound messages in a database, and switching consumers to manual acknowledgments to prevent loss in extreme failure scenarios.
This article introduces the fundamentals of message queues and compares five popular products—ActiveMQ, RabbitMQ, ZeroMQ, Kafka, and RocketMQ—detailing their features, typical use cases, and providing curated learning resources for each.
This article demystifies message queues by comparing them to parcel lockers, explains their role in asynchronous processing and system decoupling, details point‑to‑point and publish‑subscribe models, introduces JMS, AMQP, RabbitMQ, ActiveMQ, RocketMQ, and Kafka, and outlines their architectures and high‑availability mechanisms.
This article explains the three stages of RabbitMQ message flow, identifies potential loss points, and provides practical solutions—including confirm mode, message persistence, database-backed compensation, and manual acknowledgments—to achieve near‑perfect reliability for both producers and consumers.
The article explains how to achieve high‑reliability message delivery in RabbitMQ by using producer confirm mode, persisting exchanges, queues and messages, storing outbound messages in a database, and switching consumers to manual acknowledgments to prevent loss in network or broker failures.
This article explains how to achieve near‑zero message loss in RabbitMQ by using producer confirm mode, durable exchanges/queues/messages, database‑backed message storage, and manual consumer acknowledgments, while providing practical code examples for each technique.
This article explains how to implement delayed message delivery in RabbitMQ using the official delayed‑queue plugin and Spring Boot, covering configuration of exchanges, queues, bindings, message publishing with delay headers, and consumer handling, along with code examples and test results.
This article provides a comprehensive overview of RabbitMQ, covering the AMQP protocol fundamentals, key components such as connections, channels, exchanges, queues, and bindings, as well as message structures, various messaging patterns, consumption models, financial‑grade features, and a detailed feature comparison.
This article explains how to horizontally scale RabbitMQ clusters to achieve million‑message per second throughput, covering Google’s large‑scale test setup, sharding and consistent‑hash plugins, federation, high‑availability mirroring, reliability mechanisms, and practical deployment tips for production environments.
This article explains why distributed tracing is essential for microservice architectures, compares common tracing tools, and provides a step‑by‑step guide to integrate Spring Cloud Sleuth with Zipkin, configure logging, switch transport to RabbitMQ, and persist trace data using MySQL.
This article compares RabbitMQ and Kafka by explaining their architectures, queue consumption and production mechanisms, highlighting the master‑mirror design of RabbitMQ versus Kafka's partitioned approach, and summarizing their throughput characteristics to help engineers choose the appropriate messaging middleware for their needs.
