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ElasticSearch Cookbook

ElasticSearch Cookbook

By : Alberto Paro
4.1 (7)
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ElasticSearch Cookbook

ElasticSearch Cookbook

4.1 (7)
By: Alberto Paro

Overview of this book

ElasticSearch is one of the most promising NoSQL technologies available and is built to provide a scalable search solution with built-in support for near real-time search and multi-tenancy. This practical guide is a complete reference for using ElasticSearch and covers 360 degrees of the ElasticSearch ecosystem. We will get started by showing you how to choose the correct transport layer, communicate with the server, and create custom internal actions for boosting tailored needs. Starting with the basics of the ElasticSearch architecture and how to efficiently index, search, and execute analytics on it, you will learn how to extend ElasticSearch by scripting and monitoring its behaviour. Step-by-step, this book will help you to improve your ability to manage data in indexing with more tailored mappings, along with searching and executing analytics with facets. The topics explored in the book also cover how to integrate ElasticSearch with Python and Java applications. This comprehensive guide will allow you to master storing, searching, and analyzing data with ElasticSearch.
Table of Contents (14 chapters)
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13
Index

Managing documents

The APIs for managing the documents (index, update, and delete) are the most important ones after the search ones. In this recipe, we will see how to use them in a standard way and in bulk actions to improve the performance.

Getting ready

You need a working ElasticSearch cluster and required packages of the Creating a client recipe of this chapter.

The full code of this recipe is in the chapter_11/document_management.py and chapter_11/document_management_pyes.py files.

How to do it...

The main operations to manage documents are as follows:

  • index: This stores a document in ElasticSearch. It is mapped on the Index API call.
  • update: This allows updating some values in a document. This operation is composed internally (via the Lucene nature) by deleting the previous document and reindexing of the document with the new values. It is mapped on the Update API call.
  • delete: This deletes a document from the index. It is mapped on the Delete API call.

With the ElasticSearch Python client...

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