Analysis of Aggregate Functions in Relational Databases and NoSQL Databases
DOI:
https://doi.org/10.26438/ijcse/v6si6.7479Keywords:
Relational Databases, NoSQL Databases, MongoDB, MySQL, AggregationAbstract
The attractions in Big Data Analytics made a progress from relational databases to NoSQL databases. A NoSQL structure can be utilized to enhance the distribution of storage and analysis work of data in the world of big data. MongoDB is a type of NoSQL database which represents data as a collection of documents. Ordinary database systems like MySQL can store only organized data in tabular form as rows and columns. As the majority of the data created now is in unstructured or semi structured format, it is difficult for conventional database systems to store or process this data. NoSQL data stores like MongoDB can store this huge data which additionally have very powerful query engines and indexing features. These features made it simple and fast to execute extensive variety of queries including aggregate ones. The aggregation pipeline and map reduce concepts in MongoDB provides support for aggregate operations. This paper primarily makes a comparison of performance of aggregate queries in MySQL and MongoDB. A set of experiments were performed with two datasets of different size in the two databases. The results show that MongoDB performs better in all the cases. The results can be a boost for companies to change the structure of their databases from conventional form to NoSQL.
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