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)

Adding metadata to a mapping

Sometimes, when we are working with our mapping, we may need to store some additional data to be used for display purposes, ORM facilities, permissions, or simply to track them in the mapping.

Elasticsearch allows you to store every kind of JSON data you want in the mapping with the special _meta field.

Getting ready

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

How to do it…

The _meta mapping field can be populated with any data we want in JSON format, like so:

{ "_meta": {
    "attr1": ["value1", "value2"],
    "attr2": { "attr3": "value3" }
  } }

How it works…

When Elasticsearch processes a new mapping and finds a _meta field, it stores it as-is in the global mapping status and propagates the information to all the cluster nodes. The content of the _meta files is only checked to ensure it's a valid JSON format. Its content is not taken into consideration by Elasticsearch. You can populate it with everything you need to be in JSON format.

_meta is only used for storing purposes; it's not indexed and searchable. It can be used to enrich your mapping with custom information that can be used by your applications.

It can be used for the following reasons:

  • Storing type metadata:
    {"name": "Address", "description": "This entity store address information"}
  • Storing object relational mapping (ORM)-related information (such as mapping class and mapping transformations):
    {"class": "", "properties" : { "address":{"class": ""}} }
  • Storing type permission information:
    {"read":["user1", "user2"], "write":["user1"]}
  • Storing extra type information (that is, the icon filename, which is used to display the type):
    {"icon":"fa fa-alert" }
  • Storing template parts for rendering web interfaces:
    {"fragment":"<div><h1>$name</h1><p>$description</p></div>" }