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Elasticsearch 8.x Cookbook

Elasticsearch 8.x Cookbook

By : Alberto Paro
4 (6)
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Elasticsearch 8.x Cookbook

Elasticsearch 8.x Cookbook

4 (6)
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)
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Mapping a document

The document mapping is also referred to as the root object. This has special parameters that control its behavior, and they are mainly used internally to do special processing, such as routing or time-to-live of documents.

In this recipe, we'll look at these special fields and learn how to use them.

Getting ready

You will need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe of Chapter 1Getting Started.

To execute the commands in this recipe, you can use any HTTP client, such as curl (https://curl.haxx.se/), Postman (https://www.getpostman.com/), or similar. I suggest using the Kibana console, which provides code completion and better character escaping for Elasticsearch.

How to do it…

We can extend the preceding order example by adding some of the special fields, like so:

PUT test/_mapping
{ "_source": { "store": true },
    "_routing": { "required": true },
    "_index": { "enabled": true },
    "properties": {} }

How it works…

Every special field has parameters and value options, such as the following:

  • _id: This allows you to index only the ID part of the document. All the ID queries will speed up using the ID value (by default, this is not indexed and not stored).
  • _index: This controls whether or not the index must be stored as part of the document. It can be enabled by setting the "enabled": true parameter (enabled=false is the default).
  • _source: This controls how the document's source is stored. Storing the source is very useful, but it's a storage overhead, so it is not required. Consequently, it's better to turn it off (enabled=true is the default).
  • _routing: This defines the shard that will store the document. It supports additional parameters, such as required (true/false). This is used to force the presence of the routing value, raising an exception if it's not provided.

Controlling how to index and process a document is very important and allows you to resolve issues related to complex data types.

Every special field has parameters to set particular configurations, and some of their behaviors could change in different releases of Elasticsearch.

See also

Please refer to the Using dynamic templates in document mapping recipe in this chapter and the Putting a mapping in an index recipe of Chapter 3Basic Operations, to learn more.

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