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

Managing Mapping

Mapping is a very important concept in Elasticsearch, as it defines how the search engine should process a document and its fields.

Search engines perform the following two main operations:

  • Indexing: This is the action to receive a document and to process it and store it in an index
  • Searching: This is the action to retrieve the data from the index

These two parts are strictly connected; an error in the indexing step leads to unwanted or missing search results.

Elasticsearch has explicit mapping on an index level. When indexing, if a mapping is not provided, a default one is created, and guesses the structure from the data fields that the document is composed of. This new mapping is then automatically propagated to all cluster nodes.

The default type mapping has sensible default values, but when you want to change their behavior or customize several other aspects...