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Elasticsearch  Indexing
Elasticsearch  Indexing

Elasticsearch Indexing: How to Improve User's Search Experience

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Profile Icon Huseyin Akdogan
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Paperback Dec 2015 176 pages 1st Edition
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Arrow left icon
Profile Icon Huseyin Akdogan
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€20.99
Paperback Dec 2015 176 pages 1st Edition
eBook
€10.99 €16.99
Paperback
€20.99
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Free Trial
Renews at €18.99p/m
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€10.99 €16.99
Paperback
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Elasticsearch Indexing

Chapter 2. What is an Elasticsearch Index

In the previous chapter, we looked at what the difference is when a field becomes indexed and searchable and at how the analysis process affects the relevant search results. In this chapter, we will dive deep into the concept of the Elasticsearch index. Therefore, we will first go through basic concepts. Then we will examine the inverted index data structure. By the end of this chapter, we will have covered the following topics:

  • What is dynamic mapping?
  • What is denormalization?
  • Is index flexible?
  • What is the inverted index?

Nature of the Elasticsearch index

Let's go through the basic concepts of Elasticsearch indices and their features.

Indices

Elasticsearch is document-oriented. Each record in Elasticsearch is a document. Elasticsearch uses JSON (JavaScript Object Notation) as the serialization format for documents. Therefore, each piece of data that is sent to Elasticsearch for indexing is a JSON document.

Tip

JSON is an open standard format that uses human-readable text to transmit data objects consisting of attribute–value pairs. If you want more information, please refer to https://en.wikipedia.org/wiki/JSON.

Elasticsearch indices compared to database management systems may be considered to be databases. How a database is a collection of regular information, Elasticsearch indices are a collection of structured JSON document. In other words, an index is a logical partition for user data.

Documents are stored in the same index of similar characteristics, for example, your member data in the member...

Document

In the Elasticsearch world (that is, in Lucene world), a document is the main entity and basic unit of user data.

As mentioned earlier, the document format is JSON. Documents consist of fields (that is, properties) and value pairs. Each field has a name and a type supporting existing data types. A field is stored physically in a type within an index as an element of a document that has a unique ID.

Now, let's send a document to Elasticsearch:

curl -XPOST localhost:9200/premierleague/topscorer -d '{
  "fullname": "Robin van Persie",
  "age": 32,
  "birthdate": "1983-08-06",
"current_club": "Fenerbahce SK"
}'
{"_index":"premierleague","_type":"topscorer","_id":"AU8I47O90qdql2fUT1Oh","_version":1,"created":true}

As seen, we indexed the document without any preparation. Because Elasticsearch is schema-less, it does not request...

Summary

In this chapter, we looked at the nature of the Elasticsearch index and reviewed the basic concepts: what dynamic mapping is, what it means, flexibility in the index, and what the inverted index is.

In the next chapter, you'll learn about the basic mapping concept: what basic mapping is, what the relationship between mapping and relevant search results is, Is the claim about 'Elasticsearch stands for the schema-free model' always true?, and some other subjects related to this topic.

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Key benefits

  • Improve user’s search experience with the correct configuration
  • Deliver relevant search results – fast!
  • Save time and system resources by creating stable clusters

Description

Beginning with an overview of the way ElasticSearch stores data, you’ll begin to extend your knowledge to tackle indexing and mapping, and learn how to configure ElasticSearch to meet your users’ needs. You’ll then find out how to use analysis and analyzers for greater intelligence in how you organize and pull up search results – to guarantee that every search query is met with the relevant results! You’ll explore the anatomy of an ElasticSearch cluster, and learn how to set up configurations that give you optimum availability as well as scalability. Once you’ve learned how these elements work, you’ll find real-world solutions to help you improve indexing performance, as well as tips and guidance on safety so you can back up and restore data. Once you’ve learned each component outlined throughout, you will be confident that you can help to deliver an improved search experience – exactly what modern users demand and expect.

Who is this book for?

If you understand the importance of a great search experience this book will show you exactly how to build one with ElasticSearch, one of the world’s leading search servers.

What you will learn

  • Learn how ElasticSearch efficiently stores data – and find out how it can reduce costs
  • Control document metadata with the correct mapping strategies and by configuring indices
  • Use ElasticSearch analysis and analyzers to incorporate greater intelligence and organization across your documents and data
  • Find out how an ElasticSearch cluster works – and learn the best way to configure it
  • Perform high-speed indexing with low system resource cost
  • Improve query relevance with appropriate mapping, suggest API, and other ElasticSearch functionalities
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Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 30, 2015
Length: 176 pages
Edition : 1st
Language : English
ISBN-13 : 9781783987023
Vendor :
Elastic
Category :

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Product Details

Publication date : Dec 30, 2015
Length: 176 pages
Edition : 1st
Language : English
ISBN-13 : 9781783987023
Vendor :
Elastic
Category :

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Table of Contents

9 Chapters
1. Introduction to Efficient Indexing Chevron down icon Chevron up icon
2. What is an Elasticsearch Index Chevron down icon Chevron up icon
3. Basic Concepts of Mapping Chevron down icon Chevron up icon
4. Analysis and Analyzers Chevron down icon Chevron up icon
5. Anatomy of an Elasticsearch Cluster Chevron down icon Chevron up icon
6. Improving Indexing Performance Chevron down icon Chevron up icon
7. Snapshot and Restore Chevron down icon Chevron up icon
8. Improving the User Search Experience Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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