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 the function score query

This kind of query is one of the most powerful queries that are available. This is because it allows extensive customization of a scoring algorithm. The function_score query allows us to define a function that controls the score of the documents that are returned by a query.

Generally, these functions are CPU-intensive, and executing them on a large dataset (for instance, millions of records) requires a lot of memory and time, but computing them on a small subset can significantly improve the search quality.

The common scenarios used for this query are listed as follows:

  • Creating a custom score function (for example, with the decay function)
  • Creating a custom boost factor, for example, based on another field (that is, boosting a document by distance from a point)
  • Creating a custom filter score function, for example, based on scripting Elasticsearch capabilities
  • Ordering the documents randomly

Getting ready

You will...