Energy efficiency analysis of query optimizations on MongoDB and Cassandra

2017 Eighth International Green and Sustainable Computing Conference (IGSC) Pub Date : 2017-10-01 DOI:10.1109/IGCC.2017.8323581

Divya Mahajan, Ziliang Zong

{"title":"Energy efficiency analysis of query optimizations on MongoDB and Cassandra","authors":"Divya Mahajan, Ziliang Zong","doi":"10.1109/IGCC.2017.8323581","DOIUrl":null,"url":null,"abstract":"As big data emerges, the complexity of database workloads and database systems has increased significantly. It is no longer possible for one type of database to efficiently handle all big data applications. NoSQL databases are widely used to complement conventional SQL databases. In addition to traditional metrics such as response time and throughput, large scale NoSQL database systems pose higher requirements on energy efficiency due to the incredible volume of data (and the associated cost) that need to be stored and processed. Unfortunately, research on optimizations for energy efficiency in database systems has been historically overlooked. In this paper, we investigate numerous optimizations for two NoSQL databases (MongoDB and Cassandra) and conduct a comprehensive study on the impact of these optimizations on performance and energy efficiency. Our experimental results derived from 100GB of Twitter data reveal that 1) energy efficiency can be improved significantly for both MongoDB and Cassandra via query optimizations without degrading performance; and 2) energy efficiency does not always scale linearly with performance improvement.","PeriodicalId":133239,"journal":{"name":"2017 Eighth International Green and Sustainable Computing Conference (IGSC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Eighth International Green and Sustainable Computing Conference (IGSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGCC.2017.8323581","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 8

Abstract

As big data emerges, the complexity of database workloads and database systems has increased significantly. It is no longer possible for one type of database to efficiently handle all big data applications. NoSQL databases are widely used to complement conventional SQL databases. In addition to traditional metrics such as response time and throughput, large scale NoSQL database systems pose higher requirements on energy efficiency due to the incredible volume of data (and the associated cost) that need to be stored and processed. Unfortunately, research on optimizations for energy efficiency in database systems has been historically overlooked. In this paper, we investigate numerous optimizations for two NoSQL databases (MongoDB and Cassandra) and conduct a comprehensive study on the impact of these optimizations on performance and energy efficiency. Our experimental results derived from 100GB of Twitter data reveal that 1) energy efficiency can be improved significantly for both MongoDB and Cassandra via query optimizations without degrading performance; and 2) energy efficiency does not always scale linearly with performance improvement.

查看原文本刊更多论文

MongoDB和Cassandra查询优化的能效分析

随着大数据的出现，数据库工作负载和数据库系统的复杂性显著增加。一种类型的数据库不再可能有效地处理所有大数据应用程序。NoSQL数据库被广泛用于补充传统的SQL数据库。除了响应时间和吞吐量等传统指标外，由于需要存储和处理的数据量(以及相关成本)惊人，大型NoSQL数据库系统对能源效率提出了更高的要求。不幸的是，对数据库系统能源效率优化的研究一直被忽视。在本文中，我们研究了两个NoSQL数据库(MongoDB和Cassandra)的许多优化，并对这些优化对性能和能源效率的影响进行了全面研究。我们从100GB Twitter数据中得出的实验结果表明:1)通过查询优化可以显著提高MongoDB和Cassandra的能效，而不会降低性能;2)能源效率并不总是与性能改进成线性关系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2017 Eighth International Green and Sustainable Computing Conference (IGSC)

自引率

0.00%

发文量