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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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Toc

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 the full text

Full-text queries can work on unstructured text fields. These queries are aware of the analysis process. Full-text queries apply the analyzer on the search terms before performing the actual search operation. That determines the right analyzer to be applied by first checking whether a field-level search_analyzer is defined, and then by checking whether a field-level analyzer is defined. If analyzers at the field level are not defined, it tries the analyzer defined at the index level.

Full-text queries are thus aware of the analysis process on the underlying field and apply the right analysis process before forming actual search queries. These analysis-aware queries are also called high-level queries. Let's understand how the high-level query flow works.

Here, we can see how one high-level query on the title field will be executed. Remember from...

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