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)

Using AsyncElasticsearch

Python is not a language that's famous for its performance due to the Global Interpreter Lock (GIL To speed up a program that is using I/O, a new module must be created called asyncio ( that allows you to write concurrent code using the async/await syntax. This is the modern approach to writing Python applications and many frameworks are using it by default, such as Flask (, FastAPI (, and Starlette (

The Elasticsearch Python client (version 7.9.x or above) allows you to write concurrent code that uses asyncio.

Getting ready

You will 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 can be found in the ch15/fastapi-es directory...