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)

Chapter 15: Python Integration

In the previous chapter, we learned how to use a native client to access the Elasticsearch server via Java. This chapter is dedicated to the Python language and how to manage common tasks via its clients.

Apart from Java, the Elasticsearch team supports official clients for Perl, PHP, Python, .NET, and Ruby (see the announcement post on the Elasticsearch blog at http://www.elasticsearch.org/blog/unleash-the-clients-ruby-python-php-perl/). These clients have a lot of advantages over other implementations. A few of them are as follows:

  • They are strongly tied to the Elasticsearch API. These clients are direct translations of the native Elasticsearch REST interface – the Elasticsearch team.
  • They handle dynamic node detection and failovers. They are built with a strong networking base for communicating with the cluster.
  • They have full coverage of the REST API. They share the same application approach for every language that they are...