RDBMS vs NoSQL: Performance and Scaling Comparison

The massive amounts of data collected today by software in fields varying from academia to business and many other fields, is increasingly becoming a huge problem due to storage technologies not advancing fast enough to provide the performance scalability needed. This is even more true for data which are highly organised and require analysis while being stored in databases and being accessed by various applications simultaneously. Databases also have the added advantage of providing failover mechanism in case of disruptions or one node failing. As database vendors struggle to gain more market share new technologies emerge attempting to overcome the disadvantages of previous designs while providing more features. Two popular database types, the Relational Database Management Systems and NoSQL databases are examined. The aim of this project was to examine and compare two databases from these two database models and answer the question of whether one performs and scales better than the other. From the comparison of the results it was found that MongoDB can perform much better for complicated queries at the cost of data duplication which in turn results to a larger database. Also the database size did not appear to be a deciding factor as performance was not crippled significantly for larger database sizes. Writing benchmarks were also run which showed that MySQL performs best at deletion whereas MongoDB excels at inserting documents. The last comparison showed that using sharding to split up the database in MongoDB did not provide a performance advantage which may be related to the routing done by the MongoDB system.