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.

Relational Databases, NoSQL Databases, MongoDB, MySQL, Aggregation

[1] Ramez Elmasri, Shamkant B. Navathe, “Fundamentals of Database Systems”, Pearson, India, pp. 621-622, 2007.
[2] Mary Femy P.F, Reshma K.R, Surekha Mariam Varghese, “Outcome Analysis Using Neo4j Graph Database”, International Journal on Cybernetics & Informatics, Vol 5, No.2, pp.229-236, 2016.
[3] Dipina Damodaran B, Shirin Salim, Surekha Marium Varghese, “Performance Evaluation of MySQL and MongoDB databases”, International Journal of Cybernetics and Informatics”, Vol.5, No.2, pp. 387-394, 2016
[4] Guoxi Wang, and Jianfeng Tang, “The NoSQL Principles and Basic Application of Cassandra Model”, In the proceeding of the International Conference on Computer Science and Service Systems, Washington, pp.1332-1335, 2012.
[5] Yue Cui, William Perrizo, “Aggregate Function Computation and Iceberg Querying in Vertical databases”, A Thesis submitted to the Graduate Faculty of the North Dakota State University, North Dakota, pp. 10, 2005.
[6] Sanobar Khan, Vanita Mane, “SQL support over MongoDB using metadata”, International Journal of Scientific and Research publications, Vol.3, Issue.10, pp.1-5, 2013.
[7] Seyyed Hamid Aboutorabi, Mehdi Rezapour, Milad Moradi, Nasser Ghadiri, “Performance evaluation of SQL and MongoDB databases for big e-commerce data”, In the proceeding of the International Symposium on Computer Science and Software Engineering (CSSE), Iran,pp.72-78, 2015.
[8] Zhu Wei-ping, Li Ming-xin, Chen Huan, “Using MongoDB to implement textbook management system instead of MySQL”, In the proceeding of the IEEE 3rd International Conference on Communication Software and Networks, Xi’an, China, pp.828-830, 2011.
[9] Enqing Tang, Yushun Fan, “Performance Comparison between Five NoSQL Databases”, In the proceeding of the 7th International Conference on Cloud Computing and Big Data (CCBD), China, pp.105-109, 2016.
[10] https://docs.mongodb.com/manual/