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ElasticSearch Cookbook

ElasticSearch Cookbook

By : Alberto Paro
4.1 (7)
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ElasticSearch Cookbook

ElasticSearch Cookbook

4.1 (7)
By: Alberto Paro

Overview of this book

ElasticSearch is one of the most promising NoSQL technologies available and is built to provide a scalable search solution with built-in support for near real-time search and multi-tenancy. This practical guide is a complete reference for using ElasticSearch and covers 360 degrees of the ElasticSearch ecosystem. We will get started by showing you how to choose the correct transport layer, communicate with the server, and create custom internal actions for boosting tailored needs. Starting with the basics of the ElasticSearch architecture and how to efficiently index, search, and execute analytics on it, you will learn how to extend ElasticSearch by scripting and monitoring its behaviour. Step-by-step, this book will help you to improve your ability to manage data in indexing with more tailored mappings, along with searching and executing analytics with facets. The topics explored in the book also cover how to integrate ElasticSearch with Python and Java applications. This comprehensive guide will allow you to master storing, searching, and analyzing data with ElasticSearch.
Table of Contents (14 chapters)
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13
Index

Preface

One of the main requirements of today applications is the search capability. In the market we can find a lot of solutions to answer this need, both in the commercial and in the open source world. One of the frequently used libraries for searching is Apache Lucene. This library is the base of a large number of search solutions such as Apache Solr, Indextank, and ElasticSearch.

ElasticSearch is one of the younger solutions, written with the cloud, and distributed computing in mind. Its main author, Shay Banon, famous for having developed Compass (http://www.compass-project.org), released the first version of ElasticSearch in March 2010.

Thus the main scope of ElasticSearch is to be a search engine; it also provides a lot of features that allows it to be used also as data store and analytic engine via facets.

ElasticSearch contains a lot of innovative features: JSON REST-based, natively distributed in a map/reduce approach, easy to set up, and extensible with plugins. In this book, we will study in depth about these features and many others available in ElasticSearch.

Before ElasticSearch, only Apache Solr was able to provide some of these functionalities, but it was not designed for the cloud and it is not using JSON REST API. In the last year, this situation has changed a bit with the release of Solr Cloud in 2012. For users who want to have a deeper comparison between these two products, I suggest to read posts by Rafal Kuc available at http://blog.sematext.com/2012/08/23/solr-vs-elasticsearch-part-1-overview/.

ElasticSearch is also a product in continuous evolution and new functionalities are released both by the ElasticSearch Company (the company founded by Shay Banon to provide commercial support for ElasticSearch) and by ElasticSearch users as a plugin (mainly available on GitHub).

In my opinion, ElasticSearch is probably one of the most powerful and easy-to-use search solutions in the market. In writing this book and these recipes, the book reviewers and I have tried to transmit our knowledge, our passion, and the best practices to manage it in a better way.

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