An Analysis on the Comparison of the Performance and Configuration Features of Big Data Tools Solr and Elasticsearch
Today, every kind of text, audio and visual data, which are thought to be transformed into pieces of information, are stored for long periods of time for processing. The concept of Bid Data is not only associated with the data stored, but also with the system involving hardware and software that collects, processes, stores, and analyzes the data. As the data grows bigger, their physical storage options must be provided in a distributed architecture. Solr and Elasticsearch are among the most preferred tools which makes this storage process easier. As a part of Apache Lucene project, Solr is a software which was started to be developed in 2004 with the searching features of full text, multiple search, dynamic clustering, database integrated, open source and elasticity. Similarly, Elasticsearch is a new open source tool for real time, full text and distributed search, which was launched in 2010 using the Lucene library. Although Solr and Elasticsearch have similar features, there are many parameters that differentiates one from the other such as intended use, type of use, and query and indexing performances. This study researches and analyzes the differences between Solr and Elasticsearch with regards to their query and indexing speeds, ease and difficulties of use, configuration forms, and architectures in light of the literature, and the results are discussed regarding these tools' performances.