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Cluster Computing

, Volume 20, Issue 3, pp 2095–2106 | Cite as

Making a case for the on-demand multiple distributed message queue system in a Hadoop cluster

  • Cao Ngoc Nguyen
  • Soonwook Hwang
  • Jik-Soo KimEmail author
Article

Abstract

In this paper, we present a framework that can provide users with a simple, convenient and powerful way to deploy multiple message queue system on demand in a Hadoop cluster. Specifically, we are leveraging the Apache Kafka which is one of the state of art distributed message queue systems that can achieve high throughput, low latency, and good load balancing. Our framework provides automation of setting up and starting Kafka brokers on the fly and users can leverage the framework to quickly adopt Kafka without spending much efforts on installation and configuration challenges. In addition, the framework supports users to run their Kafka-based applications without detailed knowledge about the Hadoop YARN APIs and underlying mechanisms. We present a use case of the framework to evaluate Kafka’s performance with various test cases and working scenarios. The experimental results allow Kafka’s potential users to perceive the influences of different settings on the queuing performance.

Keywords

Distributed message queue Kafka Hadoop YARN Many-task computing MOHA 

Notes

Acknowledgements

This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0190-16-2012, High Performance Big Data Analytics Platform Performance Acceleration Technologies Development).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Korea Institute of Science and Technology InformationUniversity of Science & TechnologyDaejeonRepublic of Korea
  2. 2.Department of Computer EngineeringMyongji UniversityYonginRepublic of Korea

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