A study of normalization and embedding in MongoDB

With the advancement in the database technology NoSQL databases are becoming more and more popular now a days. The cloud-based NoSQL database MongoDB is one of them. As one can know the data modeling strategies for relational and non-relational databases differ drastically. Modeling of data in MongoDB database depends on the data and characteristics of MongoDB. Due to variation in data models MongoDB based application performance gets affected. In the present paper we have applied two different modeling styles as embedding of documents and normalization on collections. With the embedding feature we may face situation where documents grow in size after creation which may degrade the performance of database. The maximum document size allowed in MongoDB is limited. With references we get maximum flexibility than embedding but client-side applications must issue follow-up queries to resolve the references. The joins in this case cannot be effectively used. Hence there is need for defining the strategy of extent of normalization and embedding to get better performance in the mixed situation. The paper discussed here shows the variation in the performance along with the change in the modeling style with reference to normalization and embedding and it gives the base to find the extent of normalization and embedding for reducing query execution time.