中国民航大学学报

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HDFS 可视化及其在QAR 数据中的应用研究

冯兴杰,吴稀钰   

  1. (中国民航大学计算机科学与技术学院,天津300300)
  • 收稿日期:2016-05-09 修回日期:2016-06-12 出版日期:2017-02-25 发布日期:2017-04-10
  • 作者简介:冯兴杰(1969—),男,河北邢台人,教授,博士,研究方向为云计算,数据仓库和智能信息处理.
  • 基金资助:

    国家自然科学青年基金(61301245,61201414)

Research on HDFS visualization and its application in QAR data

FENG Xingjie, WU Xiyu   

  1. (College of Computer Science & Technology, CAUC, Tianjin 300300, China)
  • Received:2016-05-09 Revised:2016-06-12 Online:2017-02-25 Published:2017-04-10

摘要:

Hadoop 是一个由Apache 基金会开发的开源的云计算基础框架,主要由Hadoop 分布式文件系统(HDFS,Hadoop distributed file system)和Map/Reduce 分布式计算模型组成。其中,HDFS 为海量数据提供分布式存储,如何方便快捷地使用并管理HDFS 成为目前亟待解决的问题。Hadoop 提供了命令行接口与浏览器接口以访问HDFS,但是其浏览器接口的交互界面并不友好,给用户的使用带来了不便,因此目前用户访问HDFS 主要是通过命令行的方式。为了方便用户使用并管理HDFS,在深入研究HDFS 工作原理的基础上,采用struts2 开源架构及JFreeChart 图表绘制类库设计并实现了HDFS 可视化系统。利用该系统实现了对海量QAR 数据文件的分布式存储,提高了对QAR 数据进行并行化数据挖掘的效率。

关键词: HDFS, 可视化, Hadoop, QAR 数据文件, 集群管理

Abstract:

As an open software framework developed by Apache, Hadoop provides the distributed processing of large data
sets that across clusters of computer by simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. And now a wide variety of companies and organizations use Hadoop for both research and production. The core modules of Hadoop are HDFS (Hadoop distributed file system)and MapReduce distributed programming model. An HDFS cluster primarily consists of a NameNode that manages the file system metadata and DataNodes that store the actual data. However, the management issues of HDFS cluster resources has became urgent. There is a web page to browse HDFS provided by Hadoop, but this interface of HDFS is not friendly for users because command line is the main method to achieve operation of the document now. In order to use HDFS conveniently, using the struts2 develop framework and JFreeChart, the architecture of HDFS is researched and then an HDFS visualization system is designed based on web interface. And the development efficiency based on Hadoop for QAR data is improved.

Key words: HDFS, visualization, Hadoop, QAR data file, cluster management

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