Graduation Semester and Year
2015
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Ramez Elmasri
Abstract
NoSQL 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.
Keywords
NoSQL, Benchmarking
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Swaminathan, Surya Narayanan, "Quantitative Analysis of Scalable NoSQL Databases" (2015). Computer Science and Engineering Theses. 476.
https://mavmatrix.uta.edu/cse_theses/476
Comments
Degree granted by The University of Texas at Arlington