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Elasticsearch Essentials

Elasticsearch Essentials

By : Bharvi Dixit
4.3 (6)
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Elasticsearch Essentials

Elasticsearch Essentials

4.3 (6)
By: Bharvi Dixit

Overview of this book

With constantly evolving and growing datasets, organizations have the need to find actionable insights for their business. ElasticSearch, which is the world's most advanced search and analytics engine, brings the ability to make massive amounts of data usable in a matter of milliseconds. It not only gives you the power to build blazing fast search solutions over a massive amount of data, but can also serve as a NoSQL data store. This guide will take you on a tour to become a competent developer quickly with a solid knowledge level and understanding of the ElasticSearch core concepts. Starting from the beginning, this book will cover these core concepts, setting up ElasticSearch and various plugins, working with analyzers, and creating mappings. This book provides complete coverage of working with ElasticSearch using Python and performing CRUD operations and aggregation-based analytics, handling document relationships in the NoSQL world, working with geospatial data, and taking data backups. Finally, we’ll show you how to set up and scale ElasticSearch clusters in production environments as well as providing some best practices.
Table of Contents (12 chapters)
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11
Index

Multi get and multi search APIs


Until now, you have seen the execution of a single get request to fetch a document and hit a single query at a time to search for documents. However, life will be easier with the following two APIs offered by Elasticsearch.

Multi get

Multi get is all about combining more than one get request in a single request. I remember once I had a requirement to check the existence of multiple documents in an index and create a bulk update request against only those IDs that did not already exist. The one way to do this was by hitting a single HEAD request for each document ID, and based on the response of Elasticsearch, create a bulk update request for the documents that did not exist. However, multi get requests can solve this problem in a single API call instead of multiple HEAD requests.

All you need to do is create an array of document IDs and hit them on Elasticsearch using the _mget endpoint of Elasticsearch. The following is a simple curl request to show how you...

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