Data Transformation and Query Analysis of Elasticsearch and CouchDB Document Oriented Databases

Abstract

With the advent of large complex datasets, NoSQL databases have gained immense popularity for their efficiency to handle such datasets in comparison to Relational databases. The use of web and mobile applications has abruptly increased leading to the generation of mixed data types which are handled by NoSQL databases. These databases has been classified into 4 categories namely Key-Value Databases, Document-oriented Databases, Columnar NoSQL databases and Graph Databases. A number of NoSQL data stores belonging to these classes have been designed for e.g. MongoDB, Apache CouchDB, HBase, Elasticsearch etc. Operations in these data stores are executed quickly. In this thesis, we focus on two most popular NoSQL databases: Elasticsearch and Apache CouchDB. This thesis aims to transfer the heavy dataset from Relational database to NoSQL databases namely CouchDB and Elasticsearch and analyze the performance of these two NoSQL databases on the image data sets as well as text datasets. This analysis is based on the results carried out by transferring bulk data, instantiate, read, update and delete operations on both document-oriented data stores and thus showing how CouchDB is more efficient than Elasticsearch during insertion, updation and deletion operations but during selection operation Elasticsearch performs much better than CouchDB.