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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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Toc

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)

Routing

We discussed before that an index contains one or more shards. During indexing, the document ID is used to determine which shard the document belongs to, using a simple formula as follows:

hash(document_id) % no_of_shards

To retrieve a document using the document ID, the same formula is used to determine the shard the document belongs to, and the document is retrieved:

When executing a search query, the node that receives the request is known as the coordinating node. The coordinating node (Node2) sends the query to all the shards of the index, aggregates the results, and sends them back to the client.

By default, a query has to be executed on all the shards of the index. But if you have a way to group similar data together, routing can be used to send the requests to a single shard instead of all the shards in the index.

For example, you want to use Elasticsearch...

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