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Learning Elastic Stack 7.0

You're reading from   Learning Elastic Stack 7.0 Distributed search, analytics, and visualization using Elasticsearch, Logstash, Beats, and Kibana

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789954395
Length 474 pages
Edition 2nd Edition
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Authors (2):
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Sharath Kumar Sharath Kumar
Author Profile Icon Sharath Kumar
Sharath Kumar
Pranav Shukla Pranav Shukla
Author Profile Icon Pranav Shukla
Pranav Shukla
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Introduction to Elastic Stack and Elasticsearch FREE CHAPTER
2. Introducing Elastic Stack 3. Getting Started with Elasticsearch 4. Section 2: Analytics and Visualizing Data
5. Searching - What is Relevant 6. Analytics with Elasticsearch 7. Analyzing Log Data 8. Building Data Pipelines with Logstash 9. Visualizing Data with Kibana 10. Section 3: Elastic Stack Extensions
11. Elastic X-Pack 12. Section 4: Production and Server Infrastructure
13. Running Elastic Stack in Production 14. Building a Sensor Data Analytics Application 15. Monitoring Server Infrastructure 16. Other Books You May Enjoy

Searching from structured data

In certain situations, we may want to find out whether a given document should be included or not; that is, a simple binary answer. On the other hand, there are other types of queries that are relevance-based. Such relevance-based queries also return a score against each document to say how well that document fits the query. Most structured queries do not need relevance-based scoring, and the answer is a simple yes/no for any item to be included or excluded from the result. These structured search queries are also referred to as term-level queries.

Let's understand the flow of a term-level query's execution:

Figure 3.2: Term-level query flow

As you can see, the figure is divided into two parts. The left-hand half of the figure depicts what happens at the time of indexing, and the right-hand half depicts what happens at the time of a query...

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