The Effect of Changing Depth of Embedding in MongoDB

Abstract

A semantic-based study is a very efficient way to represent the information about the object and its relationship to other objects. Keeping this information is more natural for the semantic-based non-relational database. A relational database has to gather the scattered information to obtain the result. The increasing use of NoSQL systems and the need for semantic-based evaluation of the NoSQL MongoDB database motivated to study the effect of data modeling on the performance of the database on query response time. There are mainly two approaches for modeling the data in MongoDB, viz., embedded data model and normalized data model. In this study, an experiment is conducted to observe the behavior of MongoDB database based on the embedded data models with the varying depth of embedding present. The results are compared in the case of two schemas with different depths of embedding required in the MongoDB database regarding the semantics of application in reducing the query execution time and for quickening the response time.

Keywords: Data Modeling, MongoDB Database, Embedded and Normalized Data Models