This article introduces Apache RocketMQ 5.5.0's LiteTopic, a new message model designed for millions of lightweight AI agent sessions with event-driven distribution and session persistence.
This article introduces Apache RocketMQ's strategic evolution into an AI-native message engine for long-running sessions, intelligent compute scheduling, and agent collaboration.
RocketMQ LiteTopic enables fine-grained, per-scenario traffic governance for AI inference workloads via millisecond-level throttling and consumption suspension.
This article examines how Alibaba Cloud RocketMQ functions as the message buffer layer between IoT Platform and downstream consumers, enabling durable...
The article shows how to build scalable Alibaba Cloud architectures using auto-scaling, CDN, SLB, RDS, and caching.
This article explores the essential value of an Event-Driven Architecture (EDA) in the age of AI.
The article introduces Apache RocketMQ’s AI-native upgrade with Lite-Topics and intelligent scheduling to tackle GenAI challenges like long sessions, scarce compute, and Agent collaboration.
This article implements the evolution of the Apache RocketMQ locking mechanism and the design and implementation of the adaptive backoff spin lock mechanism (ABS lock).
This article explains how Alibaba Cloud uses serverless architecture to cut operational costs and improve communication for IoT devices in a cloud-native environment.
This article discusses the cost management practices of ApsaraMQ and introduces architectural optimizations and new capabilities in the serverless version.
The article delves into the evolution of modern messaging systems, focusing on key features such as software-hardware integration, millions of queues, and tiered storage.
This article introduces the technical architecture of a typical IoT scenario, emphasizing the dual role of Message Queues in connecting IoT applications and integrating cloud data processing.
This article introduces the event-driven aspects of RocketMQ 5.0 and EventBridge.
This article introduces the concepts and features of RocketMQ 5.0's stream processing capabilities, including RStreams and RSQLDB, and how they simplify stream processing.
This article introduces the concept of stream storage in RocketMQ 5.0, its usage scenarios, features, and applications through data integration cases.
This article introduces the evolution of message queues, the background and advantages of RocketMQ, and the driving forces behind its development towards the cloud-native era.
This article introduces the overall architecture of RocketMQ and how it supports diverse business scenarios based on its storage-compute separation architecture.
This article introduces the key features and enhancements of RocketMQ, focusing on its role in application decoupling, reliability, performance, and adaptability to various application architectures.
This article explores how RocketMQ solves complex business scenarios from a feature perspective.
This article is part of a series focusing on interview questions for technicians, with a specific emphasis on Kafka.
