Book Image

Elasticsearch 8.x Cookbook - Fifth Edition

By : Alberto Paro
Book Image

Elasticsearch 8.x Cookbook - Fifth Edition

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)

Integrating with DeepLearning.scala

In the previous chapter, we learned how to use DeepLearning4j with Java. This library can be used natively in Scala to provide deep learning capabilities for our Scala applications.

In this recipe, we will learn how to use Elasticsearch as a source of training data in a machine learning algorithm.

Getting ready

You need an up-and-running Elasticsearch installation, as described in the Downloading and installing Elasticsearch recipe of Chapter 1, Getting Started.

Additionally, Maven, or an IDE that natively supports Java programming, such as Eclipse or IntelliJ IDEA, must be installed.

The code for this recipe can be found in the ch14/deeplearningscala directory.

We will use the iris dataset (https://en.wikipedia.org/wiki/Iris_flower_data_set) that we used in Chapter 13, Java Integration. To prepare your iris index dataset, we need to populate it by executing the PopulatingIndex class, which is available in the source code of Chapter...