This article dissects common MQ mis‑selections, categorises four message types, explains the five architectural roles of a message broker, compares Kafka, RabbitMQ and Pulsar on capabilities and trade‑offs, and provides a step‑by‑step selection guide with real‑world code snippets and best‑practice patterns.
This article dissects why modern systems must enforce three reliability guarantees—no message loss, no duplicate processing, and no out‑of‑order delivery—by examining real‑world order flows, outbox patterns, idempotent keys, partitioning strategies, consumer acknowledgments, and operational safeguards such as replay, dead‑letter handling, and monitoring.
The article explains why cross‑aggregate business logic belongs in domain services, how domain events capture immutable business facts, and demonstrates practical Spring Boot implementations—including in‑process publishing, message‑queue integration, and the Outbox pattern—while highlighting pitfalls and verification techniques.
This article analyses why delayed messaging is a critical system bottleneck, compares RabbitMQ and RocketMQ implementations—including TTL+DLX, delayed‑plugin, fixed‑level and timestamp‑based approaches—provides detailed architectural diagrams, code samples, scaling strategies, operational checklists, and guidance on choosing the right solution for production workloads.
This comprehensive guide compares PostgreSQL and MySQL from an engineering perspective, covering business models, transaction semantics, concurrency, replication, scalability, operational costs, real‑world case studies, and a decision matrix to help teams choose the database that minimizes long‑term complexity and cost.
This comprehensive guide walks through building a production‑grade e‑commerce order service with Spring Boot 3, covering everything from domain modeling and layered architecture to high‑concurrency safeguards, idempotent design, outbox messaging, distributed transactions, caching strategies, observability, security hardening, and cloud‑native deployment on Kubernetes.
This article explains the challenges of maintaining data consistency in distributed, event‑driven microservice architectures and introduces three practical patterns—Outbox, Original Event Handling, and Self‑Read—to guarantee reliable state synchronization across services, even when failures occur.
The article explains why the naïve "save‑then‑publish" pattern in message‑driven microservices can cause data inconsistency, analyzes failure scenarios, and presents the local outbox pattern with transactional guarantees and eventual consistency as a robust solution.
