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

Sorting data using scripts

Elasticsearch provides scripting support for sorting functionality. In real-world applications, there is often a need to modify the default sorting using an algorithm that is dependent on the context and some external variables. Some common scenarios are as follows:

  • Sorting places near a point
  • Sorting by most read articles
  • Sorting items by custom user logic
  • Sorting items by revenue
Because the computing of scores on a large dataset is very CPU-intensive, if you use scripting, then it's better to execute it on a small dataset using standard score queries for detecting the top documents, and then execute a rescoring on the top subset.

Getting ready

You will need an up-and-running Elasticsearch installation...