An evaluation of data model for NoSQL document-based databases

Abstract

NoSQL databases offer flexibility in the data model. The document-based databases may have some data models built with embedded documents, and others made with referenced documents. The challenge lies in choosing the structure of the data. This paper proposes a study to analyze if different data models can have an impact on the performance of database queries. To this end, we created three data models: embedded, referenced, and hybrid. We ran experiments on each data model in a MongoDB cluster, comparing the response time of 3 different queries in each model. Results showed a disparity in performance between the data models. We also evaluated the use of indexes in each data model. Results showed that, depending on the type of query and field searched some types of indexes presented higher performance compared to others. Additionally, we carried out an analysis of the space occupied on the storage disk. This analysis shows that the choice of model also affects disk space for storing data and indexes.