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dc.contributor.advisorElmasri, Ramez
dc.creatorSwaminathan, Surya Narayanan
dc.date.accessioned2016-01-27T21:40:09Z
dc.date.available2016-01-27T21:40:09Z
dc.date.created2015-12
dc.date.issued2015-12-15
dc.date.submittedDecember 2015
dc.identifier.urihttp://hdl.handle.net/10106/25497
dc.description.abstractNoSQL databases are rapidly becoming the customary data platform for big data applications. These databases are emerging as a gateway for more alternative approaches outside traditional relational databases and are characterized by efficient horizontal scalability, schema-less approach to data modeling, high performance data access, and limited querying capabilities. The lack of transactional semantics among NoSQL databases has made the application determine the choice of a particular con- sistency model. Therefore, it is essential to examine methodically, and in detail, the performance of different databases under different workload conditions. In this work, three of the most commonly used NoSQL databases: MongoDB, Cassandra and Hbase are evaluated. Yahoo Cloud Service Benchmark, a popular benchmark tool, was used for performance comparison of different NoSQL databases. The databases are deployed on a cluster and experiments are performed with different numbers of nodes to assess the impact of the cluster size. We present a benchmark suite on the performance of the databases on its capacity to scale horizontally and on the performance of each database based on various types of workload operations (create, read, write, scan) on varying dataset sizes.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectNoSQL
dc.subjectBenchmarking
dc.titleQuantitative Analysis of Scalable NoSQL Databases
dc.typeThesis
dc.date.updated2016-01-27T21:40:09Z
thesis.degree.departmentComputer Science and Engineering
thesis.degree.grantorThe University of Texas at Arlington
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Computer Science
dc.type.materialtext
dc.creator.orcid0000-0003-2507-2740


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