The article explains why AI tasks suffer from long‑lasting, blocking calls, and shows how Apache RocketMQ 5.5.0’s LiteTopic transforms synchronous multi‑agent workflows into asynchronous, non‑blocking pipelines, boosting throughput, preserving session state, and providing smart GPU scheduling.
The article explains two ways to implement a 30‑minute order auto‑cancellation in RabbitMQ—using the classic TTL + dead‑letter exchange pattern (with its head‑blocking pitfall) and the newer delayed‑message‑exchange plugin—provides Spring Boot configuration examples, compares their trade‑offs, and offers interview tips on when to choose each solution.
The article explains how message queues can dramatically improve response time, decouple services, and smooth traffic spikes, outlines seven advantages and eight drawbacks, and provides concrete guidelines on when to adopt them, how to prevent loss, duplication, and ordering issues, and how to ensure end‑to‑end reliability.
The article explains why ACID guarantees break down in distributed systems, introduces the BASE and CAP trade‑offs, then details six concrete transaction solutions—2PC, 3PC, TCC, Saga, local message tables, and reliable messages—highlighting their processes, drawbacks, and a decision framework for selecting the right approach.
This article dissects the evolution of a flash‑sale system from a simple monolithic controller to a cloud‑native, micro‑service architecture that can handle over one million requests per second, detailing traffic‑shaping, multi‑level caching, async processing, and inventory‑consistency techniques.
This article explains the fundamentals of distributed transactions, outlines the CAP theorem trade‑offs, and compares three concrete solutions—two‑phase commit, TCC, and a message‑queue based eventual‑consistency approach—detailing their workflows, advantages, and drawbacks.
The article compares common delayed‑task solutions for order cancellation, explains why Redis expiration listeners, RabbitMQ dead‑letter queues, and in‑memory time wheels are unreliable, and recommends using proper message‑queue delayed delivery or Redisson delay queues with compensation mechanisms.
In distributed systems, message‑queue backlogs can cripple core services; this article breaks down a six‑step emergency workflow—from alert detection and throttling to temporary scaling, root‑cause analysis, targeted fixes, and final validation—plus long‑term architectural and monitoring strategies, illustrated with real‑world cases and Java code samples.
This article walks through the interview question of designing a message queue that handles billions of messages daily and peaks at millions of QPS, covering traffic calculations, core roles, storage and throughput techniques, scalability, high availability, observability, framework comparisons, a real‑world case study, and key follow‑up interview topics.
The article explains why simple key‑based idempotency fails when a second request carries different parameters, and demonstrates how to use database row locks, request fingerprinting, state machines, and explicit error handling to guarantee safe, non‑duplicate execution in payment‑critical APIs.
The article dissects a common interview question about automatically canceling unpaid orders after 30 minutes, explains why naïve cron‑based scans are unsuitable for tens of millions of rows, and presents three progressively robust solutions using Redis expiration, Redis ZSet polling, and message‑queue or time‑wheel architectures.
This article compares RabbitMQ, RocketMQ, and Kafka on throughput, latency, scalability, and reliability, outlining each system's core features and recommending suitable scenarios such as reliable messaging, high‑performance streaming, and large‑scale real‑time data processing.
RocketMQ 5.0 replaces the monolithic Broker with a stateless Proxy layer that decouples compute from storage, solves scalability, multi‑protocol and cloud‑native adaptation challenges, and is demonstrated through detailed architecture comparisons, Java code samples, and two real‑world IoT and finance case studies showing significant performance and cost benefits.
The article explains why message backlog occurs in SpringBoot applications, outlines systematic troubleshooting steps, proposes comprehensive monitoring across producer, broker, and consumer layers, and presents scaling tactics such as instance expansion, concurrency tuning, batch consumption, and long‑term capacity planning.
This article walks through sixteen common Redis scenarios—including caching hot data, sharing state across services, implementing distributed locks, generating global IDs, counting events, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, tagging, product filtering, and leaderboards—each illustrated with concrete commands and code snippets.
This article walks through more than ten common Redis scenarios—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow/fan relationships, and ranking—showing concrete command examples and code snippets for each.
This article explains why Disruptor—a lock‑free, high‑throughput Java queue from LMAX—was chosen over traditional brokers, details its core concepts such as RingBuffer, Sequence, and WaitStrategy, and provides a step‑by‑step SpringBoot demo that can handle up to six million orders per second without pressure.
This article walks through Kafka’s core architecture, explains why it achieves massive throughput, and provides a step‑by‑step SpringBoot integration—including environment setup, Maven dependencies, configuration, producer and consumer code, advanced features like transactions and dead‑letter queues, plus performance monitoring and tuning tips.
This tutorial explains why RocketMQ is chosen for high‑throughput messaging, outlines its core components and typical scenarios, guides environment setup via Docker or local installation, shows Maven integration, provides Spring Boot configuration, and presents complete producer and consumer code with testing steps, extensions for sync/async messages, and common troubleshooting tips.
The article analyzes how replacing Kafka with a simple in‑memory Go queue reduced architectural complexity, boosted throughput from 240‑330 K to 1.8‑2.0 M messages per second, and clarified debugging, while still acknowledging scenarios where Kafka remains the better choice.
This article explains why RabbitMQ is essential for distributed systems, outlines its core functions and typical scenarios, and provides step‑by‑step instructions for setting up Docker or local installations, adding Maven dependencies, configuring SpringBoot, and implementing Direct, Fanout, and Topic exchange patterns with code examples and troubleshooting tips.
During a Meituan second-round interview, the candidate explains how to diagnose and resolve high RocketMQ consumption latency in production, outlining a three‑step approach—stop the bleeding, recover service, and cure the root cause—through consumer scaling, message forwarding, logic optimization, concurrency tuning, queue expansion, and monitoring.
The article analyzes why large‑scale delayed messaging is needed, identifies the bottlenecks of the Apache Pulsar community solution, and explains TDMQ Pulsar's three‑step redesign—hierarchical time wheels, expiration re‑push, and immutable message IDs—that together enable stable million‑message delayed queues with controlled memory and minute‑level hole impact.
This guide walks Java backend developers through Redis’s eight core scenarios—caching, distributed locks, rate limiting, session sharing, leaderboards, counters, message and delay queues, bitmap statistics, and geolocation—providing complete code, diagrams, and production‑grade best practices.
This article breaks down the interview focus points for messaging middleware, explains RocketMQ's "Three High and One Rich" advantages—high throughput, reliability, consistency, and rich features—compares it with Kafka and RabbitMQ, presents six detailed reasons with code samples, a selection decision tree, common interview variants, memory mnemonics, and a concise conclusion for Java developers.
This article presents sixteen practical Redis use cases—from simple caching and distributed sessions to global IDs, rate limiting, bitmaps, shopping carts, timelines, message queues, likes, tags, filtering, follow relationships, and ranking—each illustrated with commands and code snippets for real‑world backend development.
This article walks through the core contradictions of flash‑sale systems, presents a layered architecture using CDN, Nginx, Redis, MQ and a database, explains key implementation details and common pitfalls, and offers incremental optimization steps for building a robust, high‑concurrency service.
This article walks through the challenges of a fast‑growing e‑commerce platform during peak sales, explains why Apache Kafka is the ideal asynchronous messaging backbone, and provides a complete Go implementation—including producers, consumers, best‑practice patterns, and real‑world use cases—to achieve high throughput, fault tolerance, and seamless scalability.
This article explores sixteen practical Redis applications—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, product tags, filtering, follow relationships, and ranking—demonstrating how Redis can serve as a versatile data store beyond simple caching.
This guide walks through setting up a two‑node RocketMQ cluster with dual master‑slave asynchronous replication, covering host role assignment, configuration file edits, binary deployment, memory tuning, and the exact commands to start NameServers, master brokers, and slave brokers.
This article presents a comprehensive guide to 16 practical Redis use cases—including caching, distributed locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow‑recommendation models, and ranking—complete with code snippets and data‑structure examples.
This article examines the limitations of traditional Kafka in large‑scale deployments and presents AutoMQ’s cloud‑native redesign—detailing its stateless architecture, storage separation, automatic scaling, read/write isolation, performance benchmarks, and real‑world migration case studies that demonstrate reduced latency, higher throughput, and lower resource costs.
This article analyzes the challenges of high‑concurrency voice AI agents—such as massive session management, tiny packet transmission, strict latency, and asynchronous result handling—and presents a detailed Cloud‑Native architecture using Alibaba Cloud RocketMQ LiteTopic to achieve stable, low‑latency, and automatically managed real‑time voice message pipelines.
This article presents a comprehensive technical design template for a CRM user‑reach module, covering project overview, design goals, functional and performance requirements, architecture choices, detailed workflow, data model, API specs, risk assessment, testing advice, and deployment steps to help engineers build a reliable, high‑throughput messaging system.
This guide walks through ten classic Redis scenarios—caching, session storage, rate limiting, leaderboards, message queues, Pub/Sub, real‑time analytics, distributed locks, geospatial queries, and shopping carts—providing Go code examples, best‑practice tips, and performance considerations for each pattern.
This article explains the unique challenges of using message queues for AI inference workloads, why traditional throttling methods fall short, and how Apache RocketMQ 5.x's LiteTopic introduces lightweight topics, fine‑grained flow control, physical isolation, and consumption suspension to achieve millisecond‑level real‑time throttling and minute‑level busy‑idle scheduling.
This article compiles essential Kafka fundamentals, architectural details, and a thorough set of interview questions ranging from basic to advanced topics, providing a concise yet complete resource for developers and engineers looking to master this distributed messaging platform.
The article explains how OpenClaw tackles common multi‑agent messaging problems by treating sessionKey as a partition key, redefining DM scope for multi‑source inputs, employing a dual‑layer queue with per‑session serialization and global lane throttling, and exposing configurable knobs for micro‑batching, backpressure, and observability.
This article outlines Kafka's role as a leading MQ middleware, highlighting its key traits of decoupling, asynchronous processing, and throttling, compares it with traditional messaging systems, and provides a structured list of basic, intermediate, and advanced topics plus interview questions for in‑depth study.
This guide walks through why a high‑traffic flash‑sale system needs a message queue, explains the three core benefits of async processing, decoupling and traffic‑shaping, and then details RabbitMQ installation, common work patterns, durability, idempotency, ordering, dead‑letter handling, high‑availability clustering and advanced features such as delayed and priority queues.
This article breaks down the interview focus points, core solution framework, underlying RocketMQ mechanisms, step‑by‑step remediation actions, common pitfalls, and a concluding strategy for handling message backlog through emergency scaling, consumer optimization, degradation, dead‑letter handling, and proactive capacity planning.
This article explains how RocketMQ’s consumer model is fundamentally pull‑based with a long‑polling optimization that provides a push‑like experience, covering interview focus points, detailed mechanism, code examples, best practices, and common misconceptions.
RabbitMQ supports transactional messaging to guarantee atomic delivery, using txSelect to start, txCommit to finalize, and txRollback to abort; this article explains the mechanism, outlines its advantages and performance drawbacks, and provides a complete Java example with step‑by‑step code walkthrough.
An interviewee’s instinct to simply add more RocketMQ consumers for a backlog of 100 million messages sounds plausible but fails to address root causes; the article breaks down why this quick fix is insufficient and outlines a multi‑stage, systematic approach—from emergency mitigation to root‑cause analysis and long‑term prevention—to handle massive message queues effectively.
This article presents a step‑by‑step guide to designing and implementing a Kafka‑class distributed log‑based message queue kernel, covering architecture, sequential writes, sparse indexing, zero‑copy I/O, partitioning, replication, consumer‑group metadata, batch pipelines, crash recovery, and performance benchmarks.
This comprehensive guide explores Kafka’s core value as a message queue, explains producers, consumers, topics, partitions, and replication, dives into cluster architecture, zero‑copy I/O, resource planning for disks, memory, CPU and network, and provides practical configuration, consumer‑group management, and operational tooling tips for building high‑throughput, highly available Kafka deployments.
This article explains how to connect Spring Boot to external services by choosing the appropriate HTTP client (RestTemplate, Feign, WebClient), configuring beans, implementing service methods, and applying various data‑synchronization techniques such as full sync, UPSERT, incremental sync, webhook callbacks, and message‑queue based replication.
Faced with limitations of existing tools like Quartz, XXL-Job, and PowerJob, the author explains the motivation for creating a custom scheduling framework, describes its architecture—including gRPC communication, protobuf serialization, a self-implemented name server for load balancing, a simple message queue, and time-wheel scheduling—provides code examples, and shares diagrams of discovery and dispatch processes.
This article compares Kafka, RabbitMQ, and NATS for Go microservices, explains why Kafka is often over‑engineered for internal communication, and shows how NATS provides a lightweight, event‑driven alternative with concrete code examples and a clear selection matrix.
This article compares the storage models, data organization, indexing, read/write processes, and performance trade‑offs of three major message queues—Kafka, RocketMQ, and JMQ—providing detailed technical insights for architects and engineers making storage‑related design decisions.
The article argues that a single PostgreSQL instance can handle caching, queuing, full‑text search, and real‑time notifications, eliminating the need for separate services like Redis, RabbitMQ, and Elasticsearch, while reducing cost and complexity.
This article dissects a common interview question about Kafka's higher throughput versus RocketMQ's richer features, explains the underlying design philosophies, storage models, I/O paths, scaling limits, real‑world use cases such as transaction, delayed and ordered messages, and provides concrete optimization steps and code samples to help engineers make an informed messaging platform choice.
This guide explains how to avoid duplicate message consumption in Kafka by designing unique identifiers, implementing consumer-side idempotence with deduplication tables, leveraging Kafka’s transactional features, and establishing system-level safeguards and monitoring to ensure reliable, exactly‑once processing.
During peak traffic like Double‑11, a message queue can accumulate millions of messages, and simply adding consumer instances only offers temporary relief; this article explains the partition model limits, how to calculate proper partition numbers, fast remediation tactics, and deep consumer‑side optimizations for robust, scalable processing.
This article examines whether Redis can serve as a reliable message queue by exploring its List, Pub/Sub, and Stream data structures, comparing their features, limitations, and durability against professional brokers like Kafka and RabbitMQ, and offering practical usage guidelines.
This comprehensive guide explores RocketMQ’s four‑component architecture, storage formats, routing mechanisms, write‑and‑read workflows, high‑availability designs, performance optimizations, and a side‑by‑side comparison with Kafka, providing practical insights for building robust distributed messaging systems.
This guide explains why delayed messages are essential for distributed system stability, compares RabbitMQ's TTL+DLX and delayed‑message plugin, details RocketMQ's precise timing and delay‑level features, and offers custom Redis and time‑wheel solutions with practical Java code examples and deployment tips.
This article examines whether Redis can serve as a reliable message queue by comparing its List, Pub/Sub, and Stream features against professional solutions like Kafka and RabbitMQ, covering usage patterns, code examples, performance trade‑offs, persistence, and handling of message loss and backlog.
This article explains how the River library leverages PostgreSQL to provide a Go‑native job queue with true transactional guarantees, high concurrency via goroutines, and efficient scheduling using SKIP LOCKED, while offering step‑by‑step setup and code examples for rapid adoption.
An in‑depth guide to Apache RocketMQ 4.x producer SDK covers essential and optional configurations, internal startup and sending workflows, transaction and ordered messaging, failure handling, performance tuning, monitoring, and practical code examples to help you build a reliable, high‑throughput messaging system.
This guide walks you through three practical Spring Boot solutions—database scheduled scans, message‑queue delayed queues, and Redis key‑expiration notifications—to automatically cancel unpaid orders after 30 minutes, complete with code samples, architecture diagrams, pros and cons, and best‑practice recommendations.
This guide walks a new e‑commerce developer through the evolution of order data synchronization—from naïve full‑table loads, through incremental and batch strategies, cursor‑based pagination, performance tuning, and finally to real‑time CDC with message queues—highlighting pitfalls and practical solutions.
This article provides a comprehensive overview of RocketMQ, covering its core components (Producer, Broker, Consumer, NameServer), detailed architecture diagrams, step‑by‑step message production and consumption processes, and typical scenarios such as asynchronous decoupling, traffic shaping, transactional messaging, and event‑driven logging.
This comprehensive guide explains the core components, exchange types, TTL, confirm mechanisms, consumer ACK/NACK, dead‑letter queues, and high‑availability features of RabbitMQ, RocketMQ, and Kafka, while also covering load balancing, ordering, transaction handling, and best practices for reliable message delivery.
This article presents a detailed design for a high‑concurrency (10‑100k QPS) distributed transaction solution that integrates RocketMQ transactional messages, an eBay‑style local message table, and XXL‑Job reconciliation to achieve eventual consistency while handling failures, retries, and data‑explosion challenges.
This guide outlines core principles and practical solutions for building resilient backend systems, covering fast‑failure handling, automatic retries with exponential back‑off, circuit‑breaker usage, idempotency, batch job strategies, online transaction patterns, and robust message‑queue processing.
Discover the three stages where MQ messages can be lost and explore five practical solutions—including producer confirmations, message persistence, consumer acknowledgments, transactional messaging, and retry with dead‑letter queues—complete with code examples and guidance on selecting the right approach for different scenarios.
Kafka achieves its remarkable speed through a combination of sequential disk I/O, zero‑copy networking, OS page‑cache usage, efficient batching, compression, partitioned parallelism, and a minimalist log format, each design choice synergistically boosting throughput while keeping latency low.
The article reveals Xiaohongshu’s unusually high 2023 campus recruitment packages—over 51 w annual total—while also providing an extensive backend interview preparation guide covering TCP vs UDP differences, design patterns, workflow versus rule engines, message‑queue selection, and Redis data structures and eviction policies.
This article explains how to build a high‑throughput, low‑latency, and scalable queue system in Go by leveraging Kafka, RabbitMQ, and Redis, covering core concepts, practical code examples, performance optimizations, and guidance on choosing the right solution for different workloads.
Learn how to prevent message loss in RabbitMQ by addressing three critical failure points—producer‑to‑broker, broker storage, and broker‑to‑consumer—using publisher confirms, durable queues with persistent messages, cluster mirroring, and manual consumer acknowledgments, complete with Java code examples.
The article explains why interviewers often reject flashy project tech stacks, shows how to evaluate the practicality of your own solutions, and lists common over‑engineered patterns such as unnecessary micro‑service suites, misuse of Redis, and needless sharding.
This article explains how modern ticketing systems achieve millisecond‑level response times under massive concurrent demand by combining client‑side defenses, API‑gateway signature verification, Redis atomic stock decrement, and message‑queue buffering, ensuring fairness, preventing bots, and protecting backend services from overload.
This article presents a comprehensive roadmap for backend architects, covering microservice fundamentals, design principles, gateway patterns, communication protocols, service registration, configuration management, observability pillars, service mesh options, and a detailed comparison of modern message‑queue technologies.
This article explains why consumer instances cannot scale indefinitely, how to plan partition numbers, fast‑track solutions for message backlog, consumer‑side performance tweaks, and an advanced asynchronous consumption model, providing a complete, interview‑friendly framework for handling MQ congestion.
This article explains how to handle massive concurrent requests in a group‑buy scenario by using Redis distributed locks to avoid database overselling, MQ delayed messages for timeout processing, and a local outbox table to guarantee reliable cross‑service notifications.
This article examines common pitfalls and compares five backend solutions—database scans, JDK DelayQueue, Redis keyspace notifications, Redis sorted sets, and delayed MQ messages—for reliably handling order timeout in high‑traffic e‑commerce systems, highlighting their advantages, drawbacks, and best‑fit scenarios.
This guide introduces a lightweight, high‑performance PHP queue library built on Redis 5.0+ Streams, detailing its features such as high concurrency, delayed tasks, multi‑producer/consumer support, ACK handling, message replay, audit mode, and provides step‑by‑step installation, configuration, and usage examples with code snippets.
Designing a real‑time step leaderboard for billions of users requires tackling massive write spikes, fast friend‑based queries, scalable storage, and high availability; this guide outlines a three‑step architecture using asynchronous message queues, Redis ZSETs, and MySQL sharding to meet those challenges.
This article examines common high‑availability challenges across applications, databases, caches, message queues, containers, and GC, presenting real JD engineering cases, root‑cause analyses, and practical mitigation strategies to help engineers design more resilient systems.
This guide walks you through installing RabbitMQ, configuring Spring Boot, building basic producers and consumers, exploring work, fanout, direct and topic patterns, and applying advanced features like confirmations, manual ACKs, TTL, dead‑letter, delayed and priority queues, plus clustering, monitoring and troubleshooting tips.
This article breaks down the design of a high‑traffic like service, detailing four core requirements—speed, accuracy, stability, and cost control—while recommending Redis, a message queue, and MySQL, and illustrating the full architecture from frontend to persistence with practical code examples and optimization tips.
This article explains Kafka's storage architecture, identifies three major message‑loss scenarios across production, storage, and consumption, and provides practical end‑to‑end configurations, detection methods, and business‑level patterns to achieve near‑zero message loss in high‑concurrency distributed systems.
This guide walks you through designing, preparing the environment, installing, configuring, and validating a production‑grade RocketMQ cluster with multiple masters and slaves, including optional web console setup, one‑click deployment scripts, performance tuning, security hardening, and monitoring recommendations.
This article explores the challenges of duplicate message consumption in distributed systems, explains why naive de‑duplication fails under high concurrency, and presents four progressively robust idempotency strategies—from database pessimistic locks and local message tables to a state‑machine approach with Redis or MySQL, highlighting their trade‑offs.
This article explains how event‑driven programming and state‑machine concepts can be applied to microcontroller firmware, compares a simple flag‑based approach with a message‑queue solution, and presents a reusable GF1.0 framework that combines ISR, message buffering, and a main state machine for robust embedded systems.
Message queues are foundational to large‑scale architectures, and this article explains four key scenarios—asynchronous communication, application decoupling, flash‑sale traffic buffering, and log processing—illustrated with diagrams to help backend engineers design resilient, high‑throughput systems.
Explore eight practical techniques for achieving asynchronous execution in Java—from low‑level threads and Futures to Spring’s @Async, ApplicationEvent, message queues, and Guava ListenableFuture—complete with code samples, performance insights, and best‑practice recommendations for building responsive backend services.
This article introduces the high‑performance Disruptor library, explains its core concepts such as Ring Buffer, Sequence, and Wait Strategy, and provides a step‑by‑step Java demo—including Maven setup, event model, producer, consumer, and test—showcasing how to build a lock‑free in‑memory message queue.
This guide walks through the full process of analyzing requirements, selecting self‑hosted servers, preparing CentOS nodes, installing Docker and Docker‑Compose, configuring RabbitMQ, and deploying a three‑node high‑availability RabbitMQ cluster with detailed commands and configuration files.
This article explains what message queue backlog is, why it harms system latency, and provides practical, step‑by‑step strategies—including temporary consumer scaling, prioritizing core messages, queue splitting, root‑cause analysis, performance tuning, message design, dead‑letter handling, traffic control, capacity planning, and monitoring—to eliminate backlog and ensure reliable asynchronous processing.
Learn how to design and implement automatic order expiration in e‑commerce systems using three approaches—simple scheduled table scans, delayed message queues, and a Redis time‑wheel—detailing their trade‑offs, code examples, indexing tips, distributed locking, and failure‑handling strategies.
This article walks through seven essential RocketMQ concepts—including message ordering, delayed delivery, accumulation handling, transactional guarantees, retry mechanisms, storage strategies, and filtering—providing code examples, configuration tips, and visual diagrams to help developers build robust distributed messaging systems.
This article guides job seekers through the autumn recruitment season, highlights bank hiring timelines and English requirements, and then provides a comprehensive Java interview Q&A covering JVM memory structures, object allocation failures, the parent‑delegation model, message‑queue usage, Spring AOP principles, and database string type differences.
This article explores ten practical use‑cases for message queues—covering system decoupling, asynchronous processing, traffic shaping, data synchronization, log collection, broadcast updates, ordered and delayed messages, retry mechanisms, and transactional messaging—illustrated with Java code examples and architectural diagrams.
This article explores ten practical use cases for message queues, from system decoupling and asynchronous processing to traffic shaping, data synchronization, log collection, broadcasting, ordered and delayed messages, retry mechanisms, and transactional messaging, providing code examples and best‑practice recommendations for robust backend design.
This article examines how traditional high‑performance message queues and event‑driven architectures can be revitalized for AI agents, tracing the evolution of messaging middleware, highlighting key integration points, and showcasing Apache Pulsar's cloud‑native features that enable reliable, scalable, and intelligent multi‑agent systems.
This article explains why message queues can accumulate millions of messages, analyzes root causes such as over‑fast producers and slow consumers, and presents five practical solutions—including consumer code optimization, queue‑strategy tweaks, producer rate limiting, dead‑letter handling, and automated monitoring—to dramatically reduce backlog without costly hardware scaling.
This article explains Apache Pulsar's architecture, client‑to‑broker and broker‑to‑bookie latency components, data storage model, write path, journal flush strategies, and presents detailed benchmark results showing sub‑millisecond write latency and up to 1.5 million TPS on NVMe storage.
RocketMQ tackles the high‑performance, high‑reliability challenges of distributed messaging by combining sequential disk writes, memory caching, and multi‑level indexing, detailing its storage logic, core structures, zero‑copy techniques, replication modes, static topic scaling, and practical tuning guidelines for optimal throughput.
