This article introduces Apache Kafka as a distributed streaming platform, explaining its three core capabilities, key concepts such as producers, topics, brokers, partitions and consumers, and detailing how messages are delivered, stored in partitions, and consumed by consumer groups.
This article answers common Kafka questions, explaining why Kafka cannot operate without Zookeeper, describing its two retention strategies based on time and size, detailing how simultaneous time‑ and size‑based cleanup works, listing performance bottlenecks, and offering practical guidelines for sizing and configuring Kafka clusters.
This guide walks through setting up a comprehensive security log collection pipeline—covering WAF, firewall, and Nginx logs—using ELK, Logstash, Kafka, and Flume, and then configuring real‑time alerts with Sentinl or ElastAlert integrated with DingTalk and email notifications.
This article explains how disk I/O performance is shaped by page cache behavior, sequential versus random access, elevator scheduling, and storage data structures such as B+Tree and LSM trees, showing why operating systems and databases must design for sequential reads and writes to achieve optimal throughput.
The article details how Didi built and iterated its real‑time data platform, describing the shift from MySQL‑based batch processing to a Kafka‑Samza‑Druid architecture with Spark Streaming and Flink, the challenges addressed, and the current capabilities and operational metrics.
The article describes the architecture, core functions, and workflow of a MySQL slow‑query analysis system built for the CloudDB platform, covering both daily report generation and real‑time slow‑SQL monitoring using tools such as pt‑query‑digest, ELK, and Kafka.
This article examines the design, deployment, and optimization of massive log systems, comparing architectures, discussing real‑time versus near‑real‑time requirements, and presenting practical improvements such as memory, CPU, network tuning, data partitioning, storage reduction, and component upgrades using ELK, Kafka, Fluentd, and HBase.
This article examines the role of message‑queue middleware in high‑concurrency IM systems, compares popular open‑source options such as ActiveMQ, RabbitMQ, Kafka, RocketMQ and ZeroMQ, and provides a detailed multi‑dimensional framework—including functionality, performance, reliability, operational management, and ecosystem factors—to help engineers select the most suitable queue for their specific business needs.
This article provides a comprehensive technical overview of Kafka, covering its core concepts, producer and consumer models, architecture, configuration parameters, replication mechanisms, performance optimizations, operational monitoring, tooling scripts, and related product implementations for real-time data processing.
This article explains Meituan's architecture for accurately and efficiently moving MySQL data into a Hive data warehouse by capturing binlog streams with Canal, transporting them via Kafka, and restoring them offline with Camus and a merge process that handles inserts, updates, and deletes.
The article presents a CDC solution that combines Alibaba’s Canal for real‑time MySQL binlog capture into Kafka with LinkedIn’s Camus for hourly Kafka‑to‑Hive loading, then merges snapshots and incremental binlog data to accurately and efficiently rebuild ODS tables, supporting sharding and delete events.
UCloud’s physical network orchestrator automates large‑scale data‑center switch configuration, reducing IDC network build cycles from 2‑3 days to 2‑3 hours, boosting success rates to 99%, while handling 3000+ switches, 200 Gbps access throughput, and supporting hybrid‑cloud real‑time connectivity through a scenario‑driven, Kafka‑backed architecture.
This article explains why duplicate message consumption occurs when using Spring Cloud Stream with RabbitMQ or Kafka, introduces the concept of consumer groups, and provides a step‑by‑step Java example showing how to configure and use consumer groups to ensure each message is processed by only one instance.
This guide presents New Relic’s 20 practical best‑practice recommendations—covering partitions, consumers, producers, and brokers—to help engineers design, tune, and monitor Apache Kafka deployments for reliable, high‑throughput performance.
Based on interviews with 31 IT leaders from 28 organizations, this article reveals the most popular programming languages, frameworks, and platforms—such as Python, Scala, Spark, Kafka, TensorFlow, and Tableau—currently driving big‑data extraction, analysis, and reporting, and highlights emerging trends and tool preferences.
KafkaBridge is an open‑source, multi‑language SDK built on librdkafka that offers a minimal, easy‑to‑use interface for producing and consuming messages in Apache Kafka, with optimizations for PHP‑FPM, extensive language support, and detailed performance benchmarks.
This article describes how Ctrip's flight ticket team built a massive log‑tracking platform using Elasticsearch, Kafka, and Spark, evaluated storage options such as Cassandra and HBase, introduced secondary indexing and hot‑cold data separation, and continuously evolved the architecture to balance resource usage and query performance.
The article details the evolution from an offline to a real‑time HBase data warehouse, covering business scenarios, the use of Maxwell for MySQL‑to‑Kafka ingestion, Phoenix for SQL access, CDH cluster tuning, monitoring, and several production case studies.
KafkaBridge is a lightweight, multi‑language SDK that wraps Kafka read/write operations, offering a minimal, reliable API for C/C++, PHP, Python and Go, with php‑fpm long‑connection optimizations, and includes compilation, usage, and performance testing details.
KafkaBridge is a lightweight, multi-language SDK that simplifies Kafka read/write operations, offering unified interfaces, long‑connection reuse for PHP‑FPM, and reliable message delivery, with detailed compilation steps, usage examples, and performance benchmarks across C++, Python, PHP, and Go.
This article explains how shifting from synchronous command‑driven interactions to asynchronous event‑driven flows reduces coupling, improves scalability, and enables flexible querying in distributed systems, while also discussing hybrid patterns, the single‑writer principle, and practical advantages illustrated with Kafka‑based examples.
This guide walks through collecting security device and Nginx logs using ELK 5.5.2, Logstash grok patterns, Kafka and Flume pipelines on CentOS 7, and configuring Sentinl or ElastAlert for DingTalk and email alerts, complete with code snippets and deployment commands.
This article examines the high‑real‑time requirements of incremental data ingestion for search engines, compares three update schemes, and details how adopting a Kafka subscription‑based message‑queue approach dramatically improves latency and flexibility for the Nuomi search framework.
This article describes the evolution, architectural design, and key implementation details of an order dispatch system for instant‑delivery services, covering problem analysis, delay‑task mechanisms such as database polling, DelayQueue and TimingWheel, and the final solution that combines Redis with a timing‑wheel scheduler and asynchronous processing.
This article describes the shortcomings of a legacy log‑collection architecture, proposes a streamlined real‑time design that centralises configuration in Etcd, uses a single Go‑based logagent to tail files, applies per‑service rate limiting, and forwards logs to Kafka for downstream processing.
This article explains the architecture of a Real‑Time Data Platform (RTDP), details the technical selection of core components such as DBus, Kafka, Wormhole, Moonbox and Davinci, and discusses data management, security, operations, and four deployment modes—synchronization, flow, rotation and intelligent—illustrating how each fits different business scenarios.
This article provides a comprehensive overview of Apache Kafka, comparing it with ActiveMQ, explaining its distributed architecture, topics, partitions, consumption models, high‑availability mechanisms, exactly‑once semantics, and includes detailed Java producer and consumer code examples for practical implementation.
This article evaluates the server‑side performance of three popular message middleware platforms—Kafka, RabbitMQ, and RocketMQ—by measuring throughput and latency when sending small 124‑byte messages, revealing that Kafka leads, followed by RocketMQ and then RabbitMQ.
Meituan‑Dianping’s User Action System unifies disparate user‑behavior events with a 5W1H format, ingests them via a proprietary MAPI channel into Kafka, processes them in real‑time using Storm and a Lambda batch‑speed architecture, and delivers millisecond‑level responses for billions of daily events while offering flexible, modular query and storage options.
This article reviews the essential concepts of message‑queue middleware, covering why it is used, its disadvantages, how to choose among popular MQs, and practical techniques for ensuring high availability, avoiding duplicate consumption, reliable transmission, and ordered processing.
This article explains Kafka's role as a distributed message queue, covering its architecture, replication mechanisms, producer‑consumer workflow, message integrity guarantees, schema management, and performance tuning for high‑throughput, low‑latency backend systems.
This article examines the challenges of handling massive log data in high‑traffic e‑commerce platforms and presents a comprehensive architecture, optimization strategies, and practical implementations—including Rsyslog, Kafka, Fluentd, and the ELK stack—to improve scalability, performance, and reliability of log management systems.
This article compares the architectures of Kafka and RocketMQ, examines their availability and reliability mechanisms, evaluates their strengths and weaknesses, and proposes hybrid designs and simplified MQ architectures for building highly available and reliable messaging systems.
This article presents a practical, China‑focused microservice reference stack built on Spring Cloud, detailing core support components such as Zuul, Eureka, Apollo, and Spring Boot, as well as monitoring tools like Kafka, ELK, CAT, KairosDB, ZMon, and Hystrix, and explains when and how to apply each in production environments.
After a recent feature release caused a sharp decline in a key data metric, the team followed a systematic, fourteen‑step troubleshooting process—including verification, code review, DBA involvement, local debugging, environment comparison, logging, packet capture, service restarts, request mode changes, load testing, and partition resizing—to identify and resolve a Kafka‑related throughput bottleneck.
This article walks through building a Java‑based flash‑sale (秒杀) service, diagnosing overselling issues, and progressively enhancing it with optimistic locking, distributed rate limiting, Redis caching, and asynchronous Kafka processing to achieve higher throughput and data consistency under heavy concurrency.
This article explains how to design a distributed backend that ingests millions of clicks per second, stores data with Kafka and HDFS, computes top‑N articles using sliding windows and periodic aggregation, handles node failures, and mitigates click fraud, all while balancing accuracy and resource usage.
Tencent Cloud’s senior engineer Yang Yuan explains how their managed Kafka service tackles version diversity, resource allocation, dynamic scaling, broker addition/removal, and partition migration using versioned clusters, bin‑packing algorithms, penalty weighting, and predictive scheduling to sustain trillions of messages and billions of messages per minute.
This article explains the evolution of Kafka message formats from version 0.7.x through 0.8.x (including 0.9.x) to 0.10.x, detailing each field, compression handling, and the design motivations behind the changes.
This article explains why Kafka has become essential for real‑time data streaming in the big‑data era, detailing its performance advantages, core use cases, major adopters, multilingual support, and how its scalable storage and retention mechanisms empower modern data pipelines.
Ctrip’s real‑time user behavior service, a core foundation for recommendations, ads, and user profiling, was redesigned with a Java‑based stack (Kafka, Storm, Redis, MySQL) to achieve millisecond‑level latency, high availability, and ten‑fold scalability across processing and output flows.
This case study details how Beijing 恒远华信息技术有限公司 built a dynamic face‑capture and real‑time recognition solution on Huawei FusionInsight HD, leveraging deep‑learning algorithms, distributed storage, and stream processing to handle hundreds of millions of faces with high speed, efficiency, and security.
The article examines the reliability challenges of microservice architectures that rely heavily on synchronous RPC calls, and proposes a comprehensive solution that converts failing RPCs to asynchronous message‑queue workflows, introduces a write‑ahead‑queue for transactional consistency between databases and queues, and outlines offset management to ensure end‑to‑end fault tolerance.
Milano, a distributed log collection and analysis platform built on the ELK stack, leverages Filebeat, Kafka, Logstash, Elasticsearch, and Kibana to provide high‑throughput, low‑latency, secure, and visual log management for massive clusters, addressing the challenges of traditional manual log inspection.
The article details Snowball's challenges with a saturated CDH Hadoop cluster, outlines the limitations of the original Kafka‑based log pipeline, and explains how a comprehensive redesign using FlumeNG, Spillable Memory Channels, and custom HDFS sinks resolves latency, data loss, and high‑load issues while supporting future growth.
This article introduces message queue middleware, explains its role in distributed systems for decoupling, asynchronous processing, traffic shaping, and log handling, and reviews typical use cases, architectural patterns, JMS models, and popular products such as ActiveMQ, RabbitMQ, ZeroMQ, and Kafka.
This article shares a seasoned developer’s perspective on selecting a message‑queue middleware, outlining typical adoption stages, three key evaluation criteria—coder expertise, current and future requirements, and community/ecosystem health—and offering candid advice on avoiding common pitfalls.
This article details Lianjia's journey of designing and implementing a low‑latency, stable real‑time computing platform using Spark Streaming on YARN, covering technical selection, architecture components, version compatibility challenges, exactly‑once semantics, graceful shutdown, Kafka tuning, and future enhancements.
This article introduces vehicle networking concepts, explains Huawei's OceanConnect solution, and details how data open and data collection are implemented using platforms like DAP, Kafka, and Hadoop to provide reliable, real‑time vehicle information for various applications.
Learn how to construct a scalable data processing platform using the SMACK stack—Spark, Mesos, Akka, Cassandra, and Kafka—covering storage design, processing workflows, resource management, deployment options, and fault‑tolerant task execution for both batch and streaming workloads.
iQIYI’s big‑data collection system has progressed from simple HTTP log uploads to a Flume‑Kafka pipeline and finally to a custom Venus‑Agent architecture with centralized configuration, persistent offsets, dual‑Kafka streams and Flink processing, now handling tens of millions of queries per second and over three hundred billion records daily to power its AI‑driven services.
This article describes the background, design goals, architecture, implementation details, and lessons learned from building a low‑overhead, low‑intrusion distributed tracing system using Kafka, Elasticsearch, and OpenTracing to monitor microservice interactions and support performance analysis and DevOps decision‑making.
Kafka MirrorMaker provides near‑real‑time cross‑data‑center replication by consuming from a source cluster and producing to a target cluster, and this guide explains its core features, new vs. old consumer APIs, partition assignment strategies, performance tuning, network considerations, and practical command‑line examples.
To cope with Meituan Hotel’s exploding micro‑service complexity, the infrastructure team built the Satellite System—combining MTrace and a selective, zero‑intrusion Log4j2‑based logging pipeline that streams enriched logs through Kafka, Storm, Redis and Elasticsearch, delivering second‑level trace‑log queries and six‑month retention, dramatically speeding up debugging.
This article explains Kafka’s core concepts—including producers, consumers, topics, partitions, brokers, and consumer groups—describes its distributed architecture with leader‑follower replication, and provides three hands‑on kafka‑python experiments that demonstrate basic messaging, fault‑tolerant consumer groups, and offset management for reliable consumption.
This article explains Kafka's internal message storage mechanism, detailing how topics are divided into partitions, how partitions are segmented into LogSegments with data and index files, and how sparse indexing enables efficient offset lookups.
This article explains the design and implementation of a unified seller‑operation logging platform that uses Kafka for ingestion, Storm for real‑time processing, Elasticsearch for hot‑data search, and HBase for cold‑data storage, detailing the challenges faced and the optimizations applied.
This article describes the design and implementation of a seller log system, detailing the use of Kafka for high‑throughput messaging, Storm for real‑time stream processing, Elasticsearch for hot‑data search, and HBase for cold‑data storage, along with challenges faced and optimization solutions.
This article delves into Kafka’s network layer implementation, explaining the Selector class’s role in registering socket channels, handling connection events, and orchestrating reads and writes via KafkaChannel and TransportLayer, while illustrating packet structures and providing code snippets for key functions like register, connect, poll, and send.
This article describes the background, requirements, architecture choices, performance testing, and lessons learned from building a large‑scale, distributed log collection and analysis platform at Hujiang using Elasticsearch, Logstash, Kibana, Filebeat, and Kafka to handle billions of log entries daily.
This article details JD's end‑to‑end seller log system architecture, explaining why Kafka, Storm, Elasticsearch and HBase were chosen, the challenges faced during scaling, and the practical solutions implemented to achieve a unified, high‑throughput logging platform for merchants and operations.
This guide demonstrates how to train a KMeans model with Transwarp Sophon and deploy it in Slipstream for real‑time streaming predictions on Kafka data, covering model export, stream creation, SQL‑based inference, and result persistence.
Overwatch is an open‑source distributed monitoring platform built by Dada‑Jingdong Home that collects, aggregates, and visualizes RPC traffic across thousands of micro‑services in real time, enabling engineers to quickly pinpoint the root cause of system failures using directed‑graph visualizations and CQRS‑based data queries.
This article provides an extensive overview of Apache Spark’s ecosystem—including its data‑warehouse capabilities, ML/MLlib libraries, streaming with Spark Streaming, external frameworks, and real‑world enterprise case studies—while also noting a promotional announcement for a React Native conference.
This article details how Vimur, a Kafka‑based real‑time data pipeline, addresses the challenges of service splitting, database sharding, data migration, and synchronization by using CDC, a unified Avro format, and a change distribution platform to support search indexing, cache refresh, and reactive architectures.
PhxQueue is a Tencent‑open‑source, Paxos‑based distributed queue that delivers at‑least‑once delivery, synchronous disk flushing, strict ordering, multi‑subscription, and high throughput, outperforming Kafka in reliability and failover scenarios while supporting massive workloads such as WeChat Pay.
This article explains Kafka’s High‑Level and Low‑Level consumer models, the semantics of Consumer Groups, the rebalance algorithm and its drawbacks, and outlines the planned redesign in Kafka 0.9.x that introduces a central Coordinator to solve herd and split‑brain issues.
KSQL is an open‑source, distributed SQL engine for Apache Kafka that enables continuous, real‑time queries on streaming data, lowering the barrier for analysts to perform stream processing, monitoring, security checks, and analytics without writing code.
PhxQueue, an open‑source, Paxos‑based distributed queue from WeChat, delivers at‑least‑once delivery, synchronous disk flushing, strict ordering, multi‑subscription, and high availability, outperforming Kafka in reliability and latency while maintaining comparable throughput, as demonstrated through detailed design, performance, and failover analyses.
Jkes is a Java‑based search framework built on Kafka and ElasticSearch that provides annotation‑driven JPA‑style mapping, REST APIs for indexing and searching, and detailed integration with Spring Boot, offering developers a complete backend solution for scalable document search.
The article presents the architecture, component design, optimizations, and reliability guarantees of Bilibili's Lancer log collection system, a Flume‑based distributed pipeline that handles both real‑time and offline data streams for billions of events daily.
This article explains why Kafka introduced High Availability in version 0.8, covering the necessity of data replication and leader election, the internal replication and ACK mechanisms, Zookeeper metadata structures, broker failover procedures, and the command‑line tools that help manage and rebalance a Kafka cluster.
This guide walks you through setting up Kafka and Zookeeper, adding Spring Integration dependencies, configuring application.yml, creating producer and consumer configurations with @Configuration and @EnableKafka, implementing a @KafkaListener, and testing the integration via a Spring MVC endpoint, while highlighting common pitfalls.
This guide walks you through installing Zookeeper and Kafka on a CentOS server, configuring essential settings, creating topics, and running producers and consumers, while highlighting common pitfalls and providing the exact commands needed for a successful deployment.
This article provides a comprehensive overview of Apache Kafka, explaining its role in real‑time data pipelines and stream processing, describing key concepts such as topics, partitions, logs, producers, consumers, replication, guarantees, and how Kafka functions as both a messaging and storage system.
This article examines the architectures of Kafka and RocketMQ, analyzes their availability and reliability mechanisms, evaluates their strengths and weaknesses, and proposes a hybrid MQ design that combines the benefits of both systems while simplifying dependencies and improving fault tolerance.
This article details Ctrip's design and implementation of a high‑throughput, low‑latency user data collection platform that leverages Java NIO, Netty, and a custom Kafka‑based messaging layer, covering architecture, encryption, compression, disaster‑recovery, performance testing, and downstream analytics products.
This article examines the architectures of Kafka and RocketMQ, analyzes their availability and reliability mechanisms, compares their strengths and weaknesses, and proposes hybrid designs and simplified MQ solutions for building robust message‑queue systems.
This article describes the design, technology selection, and performance evaluation of Ctrip's real‑time user behavior data collection platform, covering Netty‑based network handling, Kafka/Hermes messaging, encryption, compression, Avro backup, and related analytics products, with detailed feasibility analysis and benchmark results.
This article details Ctrip’s redesign of its real‑time user behavior service, covering the new architecture, data flow, use of Java, Kafka, Storm, Redis, and MySQL, and how it achieves high real‑time performance, availability, scalability, and fault‑tolerance to support massive travel‑industry traffic.
This article explains Kafka's distributed architecture, topic‑partition model, replication and ISR mechanisms, data durability settings, delivery guarantees, deduplication strategies, and presents benchmark results that illustrate how configuration choices affect throughput and latency in real‑world deployments.
This article explains Kafka's distributed architecture, its topic‑partition storage model, replication and synchronization mechanisms, reliability guarantees such as ISR and high‑watermark, and presents benchmark results that illustrate how replication factor, acks settings, and partition count affect throughput and latency.
This guide walks beginners through the entire big‑data ecosystem, covering the 4V characteristics, core open‑source frameworks, Hadoop setup, Hive and SQL on Hadoop, data ingestion and export tools, task scheduling, real‑time processing with Kafka, Storm and Spark Streaming, and an introduction to machine‑learning applications.
This article chronicles the multi‑year architectural evolution of Tongcheng Travel's push notification platform, detailing early batch‑job designs, successive redesigns using Redis, MongoDB, Kafka, Go, and .NET, and the performance, scalability, and operational improvements achieved through each major version.
This article explains Kafka’s core architecture—including brokers, topics, partitions, offsets, producer and consumer mechanics, replication, availability, consistency, persistence, performance optimizations, and Zookeeper integration—providing a comprehensive guide for building reliable distributed messaging systems.
The article describes how Ctrip rebuilt its real‑time user behavior platform using a Java‑based stack (Kafka, Storm, Redis, MySQL) to achieve millisecond‑level latency, high availability, scalable performance, and robust handling of traffic spikes, failures, and data back‑pressure.
This article describes the architecture, deployment, configuration, maintenance, and performance results of a large‑scale near real‑time logging platform built with Kafka, Flume, and Elasticsearch, highlighting practical lessons and future plans for resource‑efficient operation.
This guide walks beginners through the entire big‑data ecosystem—explaining the 4V characteristics, listing essential open‑source components, teaching Hadoop setup, Hive and SparkSQL usage, data ingestion with Sqoop, Flume and Kafka, task scheduling with Oozie, and real‑time processing with Storm and Spark Streaming.
This article explains Kafka's distributed architecture, its topic‑partition storage model, replication and ISR mechanisms, leader election, delivery guarantees, configuration for high reliability, and presents extensive benchmark results showing how replication factor, acks settings, and partition count affect throughput and latency.
This article explains Kafka's core concepts and compares producer‑consumer and publish‑subscribe models, illustrating how to apply each pattern for data ingestion and event distribution in distributed backend systems, and offers practical design alternatives when Kafka’s native capabilities fall short.
This article explains why Spark Streaming combined with Kafka can only guarantee at‑least‑once delivery, outlines the challenges of delayed and out‑of‑order events, and presents practical offline‑repair, deduplication, and output‑format techniques—including code examples—to achieve exact‑once semantics in big‑data pipelines.
The Sentinel system was built to provide real‑time, zero‑modification monitoring of airline ticket business services by consuming Tianwang logs through a Storm cluster, offering flexible rule configuration, addressing performance pitfalls, and planning future enhancements such as custom monitoring scripts and visual dashboards.
This article introduces Inceptor StreamSQL, explains its core concepts of Stream, StreamJob, and Application, and provides a step‑by‑step tutorial—from creating a Kafka source to launching a StreamJob and querying results—highlighting its ease of use and performance benefits.
This article explains various enterprise log types, recommends monitoring tools like Cacti, Zabbix, Splunk, and the ELK stack, and details architectures for handling server, application, and user‑click logs using technologies such as Logstash, Elasticsearch, Kibana, Kafka, Flume, and Spark.
This article details eBay's year‑long development of an enterprise‑grade, Kafka‑driven data transmission platform, covering its architecture, core services, monitoring and automation strategies, as well as performance tuning techniques that enable high throughput, low latency, and reliable cross‑data‑center replication.
The article describes how Qunar designed and evolved its Prism real‑time data platform—leveraging ELK, Kafka, Spark, Docker, and Mesos—to improve data collection, monitoring, and analysis, reduce deployment time, and support scalable DevOps operations across the company.
This article explores the evolution of log processing in the big‑data era, detailing how ELK Stack, Kafka, and Spark Streaming work together to provide scalable, real‑time log collection, analysis, and visualization for modern cloud‑native operations.
This article describes how Qunar’s Prism platform combines ELK, Kafka, Spark, Docker and other open‑source tools to create a real‑time data pipeline that speeds up problem localization, reduces deployment time, and improves resource utilization across development and operations teams.
TDSQL’s cloud‑based audit solution combines a three‑proxy high‑availability layer, Kafka’s O(1) persistent messaging, and a distributed audit‑server that uses consistent hashing and multi‑coroutine processing to consume data within seconds, while fault‑tolerant offsets, majority acknowledgments, and Tencent Cloud MongoDB storage ensure secure, ordered, scalable, and highly reliable audit logging.
Message queues act as a crucial middleware component in distributed systems, addressing coupling, asynchronous processing, traffic shaping, and high availability, with real-world scenarios such as asynchronous handling, decoupling, traffic throttling, logging, and communication, while reviewing popular solutions like ActiveMQ, RabbitMQ, ZeroMQ, Kafka, and JMS.
