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Elasticsearch 8.x Cookbook

Elasticsearch 8.x Cookbook

By : Alberto Paro
4 (6)
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Elasticsearch 8.x Cookbook

Elasticsearch 8.x Cookbook

4 (6)
By: Alberto Paro

Overview of this book

Elasticsearch is a Lucene-based distributed search engine at the heart of the Elastic Stack that allows you to index and search unstructured content with petabytes of data. With this updated fifth edition, you'll cover comprehensive recipes relating to what's new in Elasticsearch 8.x and see how to create and run complex queries and analytics. The recipes will guide you through performing index mapping, aggregation, working with queries, and scripting using Elasticsearch. You'll focus on numerous solutions and quick techniques for performing both common and uncommon tasks such as deploying Elasticsearch nodes, using the ingest module, working with X-Pack, and creating different visualizations. As you advance, you'll learn how to manage various clusters, restore data, and install Kibana to monitor a cluster and extend it using a variety of plugins. Furthermore, you'll understand how to 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 Elasticsearch cookbook, you'll have gained in-depth knowledge of implementing the Elasticsearch architecture and be able to manage, search, and store data efficiently and effectively using Elasticsearch.
Table of Contents (20 chapters)
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Chapter 16: Plugin Development

Elasticsearch is designed to be extended with plugins to improve its capabilities. In the previous chapters, we installed and used many of them (new queries, REST endpoints, and scripting plugins).

Plugins are application extensions that can add many features to Elasticsearch. They can have several usages, including the following:

  • Adding a new scripting language (that is, Python and JavaScript plugins)
  • Adding new aggregation types
  • Adding a new ingest processor
  • Extending Lucene-supported analyzers and tokenizers
  • Using native scripting to speed up the computation of scores, filters, and field manipulation
  • Extending node capabilities, for example, creating a node plugin that can execute your logic
  • Monitoring and administering clusters

In this chapter, the Java language will be used to develop a native plugin, but it is possible to use any Java virtual machine (JVM) language that generates JAR files.

The standard...

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