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Mastering Kibana 6.x

You're reading from   Mastering Kibana 6.x Visualize your Elastic Stack data with histograms, maps, charts, and graphs

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788831031
Length 376 pages
Edition 1st Edition
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Author (1):
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Anurag Srivastava Anurag Srivastava
Author Profile Icon Anurag Srivastava
Anurag Srivastava
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Table of Contents (16) Chapters Close

Preface 1. Revising the ELK Stack FREE CHAPTER 2. Setting Up and Customizing the Kibana Dashboard 3. Exploring Your Data 4. Visualizing the Data 5. Dashboarding to Showcase Key Performance Indicators 6. Handling Time Series Data with Timelion 7. Interact with Your Data Using Dev Tools 8. Tweaking Your Configuration with Kibana Management 9. Understanding X-Pack Features 10. Machine Learning with Kibana 11. Create Super Cool Dashboard from a Web Application 12. Different Use Cases of Kibana 13. Creating Monitoring Dashboards Using Beats 14. Best Practices 15. Other Books You May Enjoy

Avoiding wildcard searches


Wildcard searches are useful when we don't know the exact search text and know only certain parts of the text. So, in that case, we will provide some text plus a wildcard character to get all the matches to partial text and also the text against the wildcard characters, which can be numbers of characters or can be without any character limit, which again depends on the type of the wildcard we have used. Take a look at the following example where we want to search for the name of a blogger, but we do not remember the exact name and only knows that it starts with pa and ends with l:

GET /_search
{"query":{"wildcard":{"name":"pa*l"}}}

On executing this search, Elasticsearch will return all the results with the name starting with pa and ending with l, such as paul. This is a good feature and quite helpful in those situations where we do not know the exact search text.

Now, let's return to the problem part and understand why we should avoid wildcards in certain situations...

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