An Empirical Comparison of MongoDB and Hive
Anurag Singh Chaudhary, Kanika Singh, Sanchi Kalra, Parmeet Kaur · 2018
Most of the web and mobile applications today involve storage, processing and analysis of large datasets. The existing relational database systems are inadequate in handling the basic challenges introduced by these data-centric applications. This, consequently, has led to a new class of scalable and non-relational data management systems, referred to as NoSQL databases. NoSQL systems are characterized by their ability to scale horizontally and provide high availability. Apart from NoSQL, Hadoop framework and its constituent technologies are also synonymous with solutions for large data sets. This paper investigates the querying performance of a widely used NoSQL document store, MongoDB and compares its performance with respect to the Hadoop analytical language, Hive over a single node. The experimental results show that MongoDB yields a better performance than Hive for the considered dataset over a single node.