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This article revisits RocketMQ's Name Server, detailing its core components, how it registers brokers, maintains routing information, handles client queries, and uses heartbeat mechanisms to ensure high availability and dynamic scaling in distributed messaging systems.
This article explains the motivations behind creating a new distributed scheduling framework, compares existing solutions, and details the design choices—including gRPC communication, protobuf serialization, a custom NameServer for load balancing, and a built‑in message queue with persistence and retry mechanisms—to handle frequent task creation and dynamic parameter changes in a high‑concurrency environment.
This article introduces FolkMQ, a pure Chinese message middleware, explains its lightweight 7 MB embedded version with a web console, and provides step‑by‑step integration instructions, configuration files, code samples, testing procedures, and a link to a complete SpringBoot example.
This article explains the three message delivery semantics, the common causes of duplicate messages in queue systems, and presents concrete producer‑side, broker‑side, and consumer‑side deduplication techniques for Kafka and Pulsar, including code samples and best‑practice recommendations.
RocketMQ, an Apache‑licensed distributed message queue from Alibaba, offers low latency, high reliability, and high throughput through features like distributed architecture, synchronous disk flushing, master‑slave replication, and flexible messaging models, and it compares favorably against Kafka, RabbitMQ, and ActiveMQ in various scenarios.
This tutorial explains RocketMQ's delayed message feature, covering its core concepts, implementation using a time‑wheel algorithm, step‑by‑step usage instructions, practical code examples, and important considerations for reliable scheduling in distributed backend systems.
This tutorial walks through downloading, extracting, configuring, starting, creating topics, producing and consuming messages, and finally stopping Apache Kafka 3.8.1 on a Linux system, including all necessary command‑line instructions.
This article examines four techniques for automatically closing overdue orders—database polling, Redis key‑expiration listeners, Redis sorted‑set delay queues, and message‑queue delayed messages—detailing their implementations, advantages, drawbacks, and practical recommendations for reliable backend systems.
This guide walks through creating a FastAPI application that both publishes and consumes messages via RabbitMQ, detailing the setup of a Pika client, asynchronous consumer, message handling, API routes, and deployment with uvicorn, complete with code snippets and testing instructions.
This article explains why orders that remain unpaid for 30 minutes should be automatically cancelled and compares five technical approaches—database polling, JDK Timer, message‑queue delayed queues, distributed schedulers like Quartz, and Redis expiration listeners—detailing their implementation steps, code samples, pros, cons, and suitable scenarios.
The article examines how e‑commerce platforms implement order auto‑closure using delayed tasks, compares methods such as message‑queue delayed delivery, Redisson delay queue, Redis expiration listening, RabbitMQ dead‑letter queues and time wheels, and recommends reliable approaches while warning against unsafe practices.
The article examines various approaches for implementing accurate order‑cancellation timers in e‑commerce systems, compares message‑queue delayed delivery, Redisson delay queues, Redis expiration listeners, RabbitMQ dead‑letter queues, and time wheels, and recommends reliable solutions while warning against common pitfalls.
This article explains how RocketMQ implements long‑polling for message consumption, detailing the pull‑based model, the broker and consumer timeout settings, the internal suspension of pull requests, and the processing loop that resumes suspended requests to improve efficiency.
This article introduces the high‑performance LMAX Disruptor library, explains its core concepts such as Ring Buffer, Sequence, Sequencer, and Wait Strategy, and provides a step‑by‑step Java demo with complete code to build a producer‑consumer message queue.
This article examines the three major reliability challenges of message queues—loss, duplicate consumption, and backlog—and provides detailed RocketMQ‑specific strategies, including producer acknowledgment, broker replication, idempotent consumer design, monitoring, scaling, and parameter tuning to ensure high‑availability distributed systems.
The article explains how to achieve transaction consistency in distributed internet systems by balancing CAP trade‑offs and presents common design approaches such as 2PC, 3PC, TCC, reliable message delivery, best‑effort notification, and database‑transaction plus compensation mechanisms.
This article explains RocketMQ's transactional message mechanism, covering half‑message storage, three transaction states, status‑check procedures, key APIs, storage reliability, and the two‑phase commit process that guarantees eventual consistency in distributed systems.
To prevent overselling during flash sales, the article explains how non‑atomic database updates cause negative stock and presents solutions such as optimistic DB locking, Redis Lua atomic deductions, Redisson distributed locks, transactional message queues, and pre‑deduction with rate limiting, recommending a combined approach that achieved 120 000 QPS with zero oversell.
Using a local message table, developers can break down distributed transactions into local database operations and asynchronous MQ messages, ensuring eventual consistency, simplifying implementation, and handling retries, while balancing advantages like simplicity and compatibility against drawbacks such as added maintenance and potential queue dependencies.
The article explains how RocketMQ ensures strict message ordering through global FIFO queues and partitioned ordering, covering use cases, key implementation techniques on the producer, broker, and consumer sides, as well as lock mechanisms, retry strategies, and fault‑tolerance design.