This article explains the JMS standard, its two messaging models (Point‑to‑Point and Publish/Subscribe), how messages are consumed synchronously or asynchronously, the core JMS programming objects, and provides an overview of common middleware such as ActiveMQ, RabbitMQ, ZeroMQ, and Kafka.
This article compares Kafka and RabbitMQ, examining their design philosophies, throughput capabilities, consumer diversity, message ordering, and handling of individual messages, to help engineers decide which system suits high-volume or flexible-consumer scenarios and understand the trade-offs of each technology.
This article introduces the fundamentals of message queues, explains common use cases such as asynchronous processing, system decoupling, traffic shaping, and log handling, and reviews popular middleware implementations including JMS, ActiveMQ, RabbitMQ, ZeroMQ, and Kafka.
This article explains the AMQP protocol and RabbitMQ implementation, detailing components such as brokers, virtual hosts, exchanges, queues, messages, bindings, connections, channels, and commands, then describes the three‑layer protocol stack and multiple real‑world messaging scenarios with Java code examples.
This article provides a comprehensive overview of message queues, covering their core concepts, key application scenarios such as asynchronous processing, system decoupling, traffic shaping, log handling, and communication, and examines popular middleware like ActiveMQ, RabbitMQ, ZeroMQ, and Kafka, along with JMS models and programming details.
The article examines the fundamentals of message‑based distributed architecture, explains core messaging patterns such as Message Channel, Publisher‑Subscriber and Message Router, and provides practical application scenarios, technology‑selection criteria, and challenges for building robust enterprise integration solutions.
This article introduces Celery, a Python‑based distributed task queue, explains its three‑part architecture, shows how to install RabbitMQ and Celery on an AWS EC2 Linux instance, and provides step‑by‑step code examples for defining tasks, running workers, and retrieving asynchronous results.
After two months of deep involvement in a distributed hybrid‑cloud project, I share practical insights on using Restful APIs, RabbitMQ, Redis, logging, component‑based development, testing strategies, coding standards, and cross‑team communication, highlighting challenges and solutions encountered while integrating C#, Java, and Python services.
Over the past two months I helped develop a distributed hybrid‑cloud platform, tackling challenges from OS differences and network ports to message queues, logging, multi‑language integration, and testing, and I share the architectural decisions, tooling choices, and practical lessons learned for building robust backend systems.
This article explains why RabbitMQ clusters struggle with network partitions, how to detect partition events via logs and rabbitmqctl, the impact on queues and bindings, and step‑by‑step methods—including manual recovery commands and automatic handling modes—to restore a healthy cluster.
