An Analysis on the Comparison of the Performance and Configuration Features of Big Data Tools Solr and Elasticsearch

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 8 - 12, 26.12.2016

Mustafa Ali Akca Tuncay Aydoğan Muhammer İlkuçar

https://doi.org/10.18201/ijisae.271328

Cited By: 8

Öz

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.

Anahtar Kelimeler

Big data, Elasticsearch, Solr

Kaynakça

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