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The IUP Journal of Computer Sciences :
The Effect of Changing Depth of Embedding in MongoDB
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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.

 
 
 

The major decision in designing the data models is to be taken depending upon the structure of the document and the relationships between the data. The relationships between the objects can be represented using one of the two ways: embedded documents and reference keys (normalization). While designing the data model, these decisions reflect the degree to which the data model should store related pieces of data in a single document in the form of embedded data model. These data models may store redundant information across related models. Although some data models may be functionally equivalent to a given application, different data models may have significant impacts on database and applications performance (Anuradha and Arpita, 2013, 2014a, 2014b and 2015). While building a new application, often one of the first things is to design its data model. The considerations and requirements force developers to make some multi-factored decisions when modeling data. The data models chosen must consider the following things:
• Growth of data;
• Change in the data over time; and
• Kinds of queries specific to the application (Roberto, 2010; Sasirekha, 2011; and MongoDB, 2014).
For one-to-many relationships, indexes are the nested collections resulting from the precomputed joins between tables that quickly find all the rows whose foreign key points to row with the particular primary key. As the relational model is not closed under the composition, the notion of the index has to be defined outside the model (Anuradha and Arpita, 2014a and 2014b). In SQL category, child nodes point to parent nodes when foreign key of a child node matches the primary key of the parent node. In NoSQL category, the direction is just opposite. The parent nodes point to child nodes when the child pointer in the parent matches the address of the child node in the database (MongoDB, 2014). Thus, the NoSQL is dual of SQL category. It is, in fact, CoSQL. They are not in conflict, but they are two opposites that coexist in harmony and can transmute into each other. So this opportunity can be taken to fine-tune the data model to reflect the duality between the values and computations. Moreover, also, the benefits of synchronous ACID and asynchronous BASE properties can be obtained (Roberto, 2010; Jun and Osamu, 2011; and Alexandru et al., 2012).
In this paper, a study on NoSQL, open source, schema-free, document store—MongoDB is presented.

 
 
 

Computer Sciences IUP Journal ,Data modeling, MongoDB database, Embedded and normalized data models