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Learning Elasticsearch

You're reading from   Learning Elasticsearch Structured and unstructured data using distributed real-time search and analytics

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787128453
Length 404 pages
Edition 1st Edition
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Author (1):
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Abhishek Andhavarapu Abhishek Andhavarapu
Author Profile Icon Abhishek Andhavarapu
Abhishek Andhavarapu
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Table of Contents (11) Chapters Close

Preface 1. Introduction to Elasticsearch FREE CHAPTER 2. Setting Up Elasticsearch and Kibana 3. Modeling Your Data and Document Relations 4. Indexing and Updating Your Data 5. Organizing Your Data and Bulk Data Ingestion 6. All About Search 7. More Than a Search Engine (Geofilters, Autocomplete, and More) 8. How to Slice and Dice Your Data Using Aggregations 9. Production and Beyond 10. Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

Aggregation basics

Aggregation is one of many reasons why Elasticsearch is nothing like anything out there; it is an analytics engine on steroids. Aggregation operations, such as distinct, count, and average on large data sets, are traditionally run on batch processing systems, such as Hadoop, due to the heavy computation involved. As running these kind of queries on a large dataset using a traditional SQL database can be very challenging. Elasticsearch enables these queries to run in real-time sub-second queries. In my first project with Elasticsearch, we solely used Elasticsearch for its aggregation capabilities and few search capabilities.

Aggregations in Elasticsearch are very powerful as you can nest aggregations. Let's take a query from the SQL world:

select avg(rating) from Product group by category; 

To execute the query, the products are first grouped by category...

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