Semianr: A Comparative Assessment of RDBMS and NoSQL for Integration with Solr System
Tolulope Ibrahim Alonge
Supervisor: Dr. Lourdes Peña-Castillo
A Comparative Assessment of RDBMS and NoSQL for Integration with Solr System
Department of Computer Science
Wednesday, July 26, 2017, 11:30 a.m., Room EN 2022
The increase in big data has highlighted the limitations of the traditional forms of data storage and management and focused attention on new methods. New database technologies like NoSQLare the most suitable solution for handling big data in a distributed system. By design, NoSQL does not provide all of the relational database features such as transactions, data integrity enforcement, and declarative query language. Instead, they primarily focus on providing near real-time reads and writes in the order of billions and millions and also provide what is known as eventual data consistency.
In this project two database systems were benchmarked for their performance and scalability, one of the most popular relational data management system MySQL and the most popular NoSQL database system MongoDB were used for the experiment. The main purpose of these experiment was to examine and compare these two database systems and answer the question of whether one performs and scales better than the other using several metrics. In addition, we evaluate how they perform while migrating data to a NoSQL database Solr which is best known for its search application and faceting capabilities. Our results indicate that no specific system consistently out-perform the others but the best option can vary depending on the features of the data, the type of query and the specific system. Mongo DB performed better when inserting the document before indexing and when indexed. When being queried by measuring the time needed to load and query a specific amount of data “rows per second” both for simple and complex queries MySQL performed slightly better than MongoDB.