Performance Monitoring of MongoDB on Varied Cluster Configuration: An Experimental Approach

Data has become one of the most valuable assets in today's digital world. The nature of the generated data varies from structured to semi-structured and even completely unstructured data items. Like many other verticals, the educational domain is also generating a huge volume of data with high variation. The main objective is to store and retrieve data in optimal time, efficiently utilizing recourses, and maintaining security. The evolution of different “Not only SQL (NoSQL)” data models to manage this huge volume and variety of data has helped to facilitate many powerful applications like Facebook, Instagram, WhatsApp, etc. NoSQL data models are provided by many vendors as installation-based services and cloud-based services as well. MongoDB is one of the popular document-oriented NoSQL data models. In this paper, our objective is to analyze the performance in different aspects of MongoDB on installation-based clustering system and on cloud-based clustering system. We have deployed the methodology for the stated semi-structured big-data handling with a case study. The performance of the execution of Mongo Query Language (MQL) on the two different platforms is analyzed.