This article examines four core microservice design approaches—data sharing, aggregation, proxy, and asynchronous messaging—detailing their advantages, drawbacks, and ideal scenarios to help architects choose the right pattern for large‑scale systems.
This article explains the communication challenges of modern multi‑agent AI applications and demonstrates how RocketMQ for AI’s event‑driven, asynchronous messaging architecture can improve scalability, reliability, and cost efficiency through a step‑by‑step weather‑and‑travel planning example.
This article explains how Apache RocketMQ’s new semantic Topic and Lite‑Topic features enable dynamic ability discovery, asynchronous communication, and closed‑loop task coordination for multi‑agent AI systems, outlining communication patterns, subscription management, and an event‑driven model that bridges AI autonomy with reliable backend messaging.
This article outlines four key microservice architecture patterns—aggregation, shared database, proxy (API gateway), and asynchronous messaging—detailing their advantages, drawbacks, and typical application scenarios to help architects choose the right approach for scalable, resilient systems.
This article explains core microservice design patterns—including data‑sharing anti‑patterns, aggregation layers, proxy/sidecar architectures, and asynchronous messaging—offering practical guidance for building scalable, decoupled backend systems.
This article introduces and compares four common microservice architecture patterns—aggregation, shared database, proxy (including sidecar), and asynchronous messaging—detailing their designs, advantages, disadvantages, and typical use cases for large‑scale systems.
This article explains asynchronous messaging patterns, compares RabbitMQ and Apache Kafka in terms of architecture, queues, topics, and use cases, and provides guidance on selecting the appropriate platform for different scenarios in modern systems.
This article explains the fundamental asynchronous messaging patterns, compares RabbitMQ and Apache Kafka in terms of architecture, queue and pub/sub models, and provides guidance on selecting the appropriate technology for different backend scenarios.
This article walks you through the complete journey from a simple monolithic e‑commerce application to a highly scalable, low‑latency, event‑driven microservices system, covering design patterns, principles, communication strategies, data management techniques, and technology choices.
The article examines why growing business complexity and throughput demands necessitate system decomposition and architectural evolution, detailing horizontal scaling, vertical and business splitting, database partitioning strategies, cache and index integration, remote service data sourcing, and asynchronous messaging to achieve scalable, robust micro‑service‑based solutions.
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.
To solve the scalability, visibility, and debugging challenges of heterogeneous asynchronous calls after Alibaba Momma’s advertising services shifted to micro‑services, the team created the AMB (Alibaba Momma Application Message Bus), a console‑driven, bus‑style platform with layered connectors, reusable components, isolated JStorm topologies, and centralized monitoring that turns hours‑long coding into rapid visual workflow configuration.
This article explains the importance of asynchronous messaging and event‑driven communication for coordinating changes across multiple microservices and bounded contexts, covering single‑receiver and multi‑receiver patterns, message brokers, eventual consistency, publishing/subscribing mechanisms, and practical implementation considerations.
The article uses Starbucks' coffee‑ordering process to illustrate asynchronous order handling, correlation identifiers, exception‑handling strategies, two‑phase commit, and conversation patterns, showing how real‑world practices inform the design of reliable distributed messaging systems.
The article examines how to reduce tight coupling between microservices by applying module‑division principles, converting synchronous calls to asynchronous messaging, leveraging event‑driven architectures and CQRS, employing local caching and data landing, and refactoring overly coupled services for more resilient backend systems.
The article uses Starbucks' coffee‑ordering process as a concrete analogy to explain asynchronous messaging, correlation, error‑handling strategies such as write‑off, retry and compensation, and why two‑phase commit is unsuitable for high‑throughput services.
The article uses Starbucks' coffee‑ordering workflow as a real‑world analogy to explain asynchronous messaging patterns, correlation challenges, exception handling strategies, and why two‑phase commit can hinder scalability, illustrating how everyday processes inspire robust distributed system designs.
This article explores how applying finite state machines to a ticket order transaction system improves stability, scalability, and maintainability, detailing the system’s architecture, database design, framework selection, custom MSM implementation, and practical challenges such as transaction handling, action matching, asynchronous messaging, and concurrency control.
This article outlines six fundamental microservice design patterns—Aggregator, Proxy, Chain, Branch, Data‑Sharing, and Asynchronous Messaging—explaining their structures, use‑cases, scalability characteristics, and potential pitfalls to help architects choose the right approach for their systems.
The article explains how microservice architectures increase testing complexity and presents practical strategies—including orchestration testing, service virtualization, handling API changes, asynchronous command patterns, event firehose, and CI/CD environment configuration—to reliably test individual services and their interactions in cloud‑native environments.
The article summarizes the design principles of reactive microservices, covering isolation, single responsibility, autonomy, private state, asynchronous messaging, service discovery, API management, and security, and explains how these concepts enable scalable, resilient cloud‑native systems.
This article details Xiaomi's evolution from a simple two‑server e‑commerce site to a sophisticated, decoupled architecture with the Notify asynchronous messaging system, explaining design decisions, message splitting, agent proxying, performance upgrades, and lessons for building resilient backend services.
This article explains service branch mode, its impact on availability, asynchronous messaging, shared‑data anti‑patterns, and essential microservice fault‑tolerance techniques such as compartment isolation, circuit breaking, rate limiting, and failover strategies.
This article details the three-stage evolution of Xiaomi's e‑commerce architecture, introduces the design of the Notify asynchronous message system built on Redis and MySQL, and explains the subsequent upgrades—including agent proxy, Go‑based modules, and MyCAT integration—to improve scalability, reliability, and performance.
