Book Image

Elasticsearch 7.0 Cookbook - Fourth Edition

By : Alberto Paro
Book Image

Elasticsearch 7.0 Cookbook - Fourth Edition

By: Alberto Paro

Overview of this book

Elasticsearch is a Lucene-based distributed search server that allows users to index and search unstructured content with petabytes of data. With this book, you'll be guided through comprehensive recipes on what's new in Elasticsearch 7, and see how to create and run complex queries and analytics. Packed with recipes on performing index mapping, aggregation, and scripting using Elasticsearch, this fourth edition of Elasticsearch Cookbook will get you acquainted with numerous solutions and quick techniques for performing both every day and uncommon tasks such as deploying Elasticsearch nodes, integrating other tools to Elasticsearch, and creating different visualizations. You will install Kibana to monitor a cluster and also extend it using a variety of plugins. Finally, you will integrate your Java, Scala, Python, and big data applications such as Apache Spark and Pig with Elasticsearch, and create efficient data applications powered by enhanced functionalities and custom plugins. By the end of this book, you will have gained in-depth knowledge of implementing Elasticsearch architecture, and you'll be able to manage, search, and store data efficiently and effectively using Elasticsearch.
Table of Contents (23 chapters)
Title Page

Using a Boolean query

Most people using a search engine have, at some time or another, used the syntax with minus (-) and plus (+) to include or exclude query terms. The Boolean query allows the user to programmatically define queries to include, exclude, optionally include (should), or filter in the query.

This kind of query is one of the most important ones because it allows the user to aggregate a lot of simple queries or filters that we will see in this chapter to build a big complex one.

Two main concepts are important in searches: query and filter. The query means that the matched results are scored using an internal Lucene scoring algorithm; for the filter, the results are matched without scoring. Because the filter doesn't need to compute the score, it is generally faster and can be cached.

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