计算机与现代化 ›› 2013, Vol. 1 ›› Issue (7): 164-168.doi: 10.3969/j.issn.1006-2475.2013.07.043

• 应用与开发 • 上一篇    下一篇

面向海量数据的电子政务云平台研究

刘军霞1,2,王 磊2,周 喜2   

  1. 1.中国科学院大学,北京100049; 2.中国科学院新疆理化技术研究所,新疆乌鲁木齐830011
  • 收稿日期:2013-03-12 修回日期:1900-01-01 出版日期:2013-07-17 发布日期:2013-07-17

Research on Electronic Government Cloud Platform for Mass Data

LIU Jun-xia1,2, WANG Lei2, ZHOU Xi2   

  1. 1. University of Chinese Academy of Sciences, Beijing 100049, China; 2. The Xinjiang Technical Institute of Physics & Chemistry, CAS, Urumqi 830011, China
  • Received:2013-03-12 Revised:1900-01-01 Online:2013-07-17 Published:2013-07-17

摘要: 针对传统电子政务平台所采用的关系型数据库在处理海量数据时存在性能瓶颈问题,利用Hadoop分布式平台在处理海量数据方面的优势,结合HDFS分布式文件系统、Map/Reduce并行计算模型和Hive仓库技术,设计关系型数据库与Hadoop相结合的电子政务云平台,两者协同提供海量数据查询操作和存储服务,从而降低了关系型数据库服务器的负载压力,增强电子政务平台的扩展性。通过实验证明,Hadoop能大大提高电子政务云平台的查询效率。进一步分析该设计方案中影响查询效率的因素,为深入研究基于Hadoop构建高效的电子政务云提供参考。

关键词: 电子政务, 海量数据, 云计算, Hadoop, 关系型数据库, 查询效率

Abstract: In view of the performance bottleneck in the treatment of massive data of the relational database which is adapted by the traditional electronic government platform, using the advantage of Hadoop distributed platform in mass data processing and combining HDFS distributed file system, Map/Reduce parallel computational mdoel and Hive data warehouse technology, this paper designs an electronic government cloud platform that combines relational database with Hadoop. Both of them collaboratively provide mass data query operation and storage service, so as reduce load pressure of the relational database server and enhance the extensibility of electronic government platform. Through the experiment, it proves that the scheme can greatly enhance the query efficiency of electronic government platform. This paper further analyzes the influence factors of query efficiency of this scheme to provide the reference for further study of building efficient electronic government cloud based on Hadoop.

Key words: electronic government, massive data, cloud computing, Hadoop, relational database, query efficiency