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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

Executing a standard search

After having inserted documents, the most common executed action in ElasticSearch is the search. The official ElasticSearch client APIs for searching are similar to the REST one.

Getting ready

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

The code of this recipe is in the chapter_11/searching.py and chapter_11/searching_pyes.py files.

How to do it...

To execute a standard query, the client search method must be called passing the query parameters as we saw in Chapter 5, Search, Queries, and Filters. The required parameters are at least the index name, the type name, and the query DSL. In the following example I'll show how to call a match all query, a term query and a filter query. We need to perform the following steps:

  1. We will initialize the client and populate the index as follows:
    import elasticsearch
    from pprint import pprint
    
    es = elasticsearch.Elasticsearch()
    index_name = "my_index&quot...
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