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)

Executing a standard search

After document insertion, the most commonly executed action in Elasticsearch is the search action. The official Elasticsearch client APIs for searching are similar to the ones for the REST API.

Getting ready

You will need an up-and-running Elasticsearch installation, which we described how to get in the Downloading and installing Elasticsearch recipe in Chapter 1, Getting Started.

You will also need the Python packages that were installed in the Creating a client recipe in this chapter.

The code for this recipe can be found in the ch15/code/searching.py file.

How to do it…

To execute a standard query, the client search method must be called by passing the query parameters, as we saw in Chapter 4, Exploring Search Capabilities. The required parameters are index_name, type_name, and the query's DSL. In this recipe, you will learn how to call a match_all query, a term query, and a filter query. Perform the following steps:

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