Performance Analysis of Sales Big Data Processing using Hadoop and Hive in Cloud Environment

M. H. P. Swari, I. K. S. Satwika, I. Handika
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引用次数: 3

Abstract

Nowadays, big data gains much attention from academics and IT industries. This is due to the extraordinary current growth of data that must be accompanied by a variety of qualified data storage and processing techniques to overcome the5 V’s challenge of big data. This research is aimed to conduct a performance analysis of big data processing. The sales data will be processed in a parallel scheme on the cloud server and then managed using Hadoop and hive. The research shows that the more VMs used, the lower processing time needed, but this is inversely proportional to the CPU time required. Whereas, from the side of block size testing the research result shows that the decrease in the time of query execution is very visible by the change in the use of block size from 2MB to 4MB and 8MB, but the change in the blocksize size from 4MB to 8MB does not significantly affect the speed of query execution.
云环境下使用Hadoop和Hive处理销售大数据的性能分析
如今,大数据受到学术界和IT行业的广泛关注。这是由于当前超乎寻常的增长数据,必须附有各种合格的数据存储和处理技术来克服the5 V大数据的挑战。本研究旨在对大数据处理进行性能分析。销售数据将在云服务器上以并行方案处理,然后使用Hadoop和hive进行管理。研究表明,使用的vm越多,所需的处理时间越短,但这与所需的CPU时间成反比。然而,从块大小测试的角度来看,研究结果表明,从2MB到4MB和8MB的块大小变化对查询执行时间的减少非常明显,但从4MB到8MB的块大小变化对查询执行速度的影响并不显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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