Performance Evaluation of Analytical Queries on a Standalone and Sharded Document Store

Numerous organizations perform data analytics using relational databases by executing data mining queries. These queries include complex joins and aggregate functions. However, due to an explosion of data in terms of volume, variety, veracity, and velocity known as Big Data [1], many organizations such as Foursquare, Adobe, and Bosch have migrated to NoSQL databases [2] such as MongoDB [3] and Cassandra [4]. We investigate the performance impact of analytical queries on a NoSQL document store. We benchmark the performance of MongoDB [3], a cross-platform document-oriented database, in a stand-alone environment and a sharded environment. The TPC-DS benchmark [5] is used to generate data of different scales and selected data mining queries are executed in both the environments. Our experimental results show that along with choosing the environment, data modeling in MongoDB also has a significant impact on query execution times. Analytical query performance is best when data is stored in a denormalized fashion. When the data is sharded, due to multiple query predicates in an analytical query, aggregating data from a few or all nodes proves to be an expensive process and hence performs poorly when compared to the alternative process of executing the same in a stand-alone environment.