Book Image

Elasticsearch Essentials

Book Image

Elasticsearch Essentials

Overview of this book

With constantly evolving and growing datasets, organizations have the need to find actionable insights for their business. ElasticSearch, which is the world's most advanced search and analytics engine, brings the ability to make massive amounts of data usable in a matter of milliseconds. It not only gives you the power to build blazing fast search solutions over a massive amount of data, but can also serve as a NoSQL data store. This guide will take you on a tour to become a competent developer quickly with a solid knowledge level and understanding of the ElasticSearch core concepts. Starting from the beginning, this book will cover these core concepts, setting up ElasticSearch and various plugins, working with analyzers, and creating mappings. This book provides complete coverage of working with ElasticSearch using Python and performing CRUD operations and aggregation-based analytics, handling document relationships in the NoSQL world, working with geospatial data, and taking data backups. Finally, we’ll show you how to set up and scale ElasticSearch clusters in production environments as well as providing some best practices.
Table of Contents (18 chapters)
Elasticsearch Essentials
Credits
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
Index

Text search


Searching is broadly divided into two types: exact term search and full text search. An exact term search is something in which we look out for the exact terms; for example, any named entity such as the name of a person, location, or organization or date. These searches are easier to make since the search engine simply looks out for a yes or no and returns the documents.

However, full text search is different as well as challenging. Full text search refers to the search within text fields, where the text can be unstructured as well as structured. The text data can be in the form of any human language and based on the natural languages, which are very hard for a machine to understand and give relevant results. The following are some examples of full text searches:

  • Find all the documents with search in the title or content fields, and return the results with matches in titles with the higher score

  • Find all the tweets in which people are talking about terrorism and killing and return...