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

Integrating with NumPy and scikit-learn

Elasticsearch can be easily integrated with many Python machine learning libraries. One of the most used libraries for works with datasets is NumPy—a NumPy array is a building block dataset for many Python machine learning libraries. In this recipe will we seen how it's possible to use Elasticsearch as dataset for the scikit-learn library (https://scikit-learn.org/).

Getting ready

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

The code for this recipe is in the ch15/code directory and the file used in the following section is the kmeans_example.py.

We will use the iris dataset...