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2018 | OriginalPaper | Chapter

Review of Elasticsearch Performance Variating the Indexing Methods

Authors : Urvi Thacker, Manjusha Pandey, Siddharth S Rautaray

Published in: Progress in Intelligent Computing Techniques: Theory, Practice, and Applications

Publisher: Springer Singapore

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Abstract

In today’s world, data is increasing rapidly. Users mostly refer to internet for any information. Also a recent study shows that most of the users go to a search engine to refer to any other site also. So search has become an inseparable activity in internet. Elasticsearch is a java-based search engine that works efficiently in cloud environment. It mainly serves purpose of scalability, real-time search and efficiency that relational databases were not able to address. In this paper, we represent our involvement with Elasticsearch, an open source, Apache Lucene-based, full-text search engine that gives near real-time search ability, as well as a RESTful API for the simplicity of access to users in the various fields like education and research.

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Literature
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go back to reference Bai, J. (2013). Feasibility analysis of big log data real time search based on hbase and elasticsearch. Ninth International Conference on Natural Computation (ICNC) (pp. 1166–1170). IEEE, Natural Computation (ICNC). Bai, J. (2013). Feasibility analysis of big log data real time search based on hbase and elasticsearch. Ninth International Conference on Natural Computation (ICNC) (pp. 1166–1170). IEEE, Natural Computation (ICNC).
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go back to reference Oleksii Kononenko, O. B. (2014). Mining modern repositories with elasticsearch. Proceedings of the 11th Working Conference on Mining Software Repositories (pp. 328–331). ACM. Oleksii Kononenko, O. B. (2014). Mining modern repositories with elasticsearch. Proceedings of the 11th Working Conference on Mining Software Repositories (pp. 328–331). ACM.
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go back to reference Pingkan P. I. Langi, W. W. (2015). An evaluation of Twitter river and Logstash performances as elasticsearch inputs for social media analysis of Twitter. International Conference on Information & Communication Technology and Systems (ICTS) (pp. 181–186). Surabaya: IEEE. Pingkan P. I. Langi, W. W. (2015). An evaluation of Twitter river and Logstash performances as elasticsearch inputs for social media analysis of Twitter. International Conference on Information & Communication Technology and Systems (ICTS) (pp. 181–186). Surabaya: IEEE.
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go back to reference Tong, C. G. (2015). Elasticsearch: The Definitive Guide. O’Reilly Media, Inc. Tong, C. G. (2015). Elasticsearch: The Definitive Guide. O’Reilly Media, Inc.
Metadata
Title
Review of Elasticsearch Performance Variating the Indexing Methods
Authors
Urvi Thacker
Manjusha Pandey
Siddharth S Rautaray
Copyright Year
2018
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-3376-6_1

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