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ElasticSearch Cookbook - Second Edition

You're reading from   ElasticSearch Cookbook - Second Edition Over 130 advanced recipes to search, analyze, deploy, manage, and monitor data effectively with ElasticSearch

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783554836
Length 472 pages
Edition 2nd Edition
Languages
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Author (1):
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Alberto Paro Alberto Paro
Author Profile Icon Alberto Paro
Alberto Paro
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Table of Contents (20) Chapters Close

ElasticSearch Cookbook Second Edition
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Downloading and Setting Up 3. Managing Mapping 4. Basic Operations 5. Search, Queries, and Filters 6. Aggregations 7. Scripting 8. Rivers 9. Cluster and Node Monitoring 10. Java Integration 11. Python Integration 12. Plugin Development Index

Mapping an IP field


ElasticSearch is used to collect and search logs in a lot of systems, such as Kibana (http://www.elasticsearch.org/overview/kibana/ or http://kibana.org/) and logstash (http://www.elasticsearch.org/overview/logstash/ or http://logstash.net/). To improve searching in these scenarios, it provides the IPv4 type that can be used to store IP addresses in an optimized way.

Getting ready

You need a working ElasticSearch cluster.

How to do it...

You need to define the type of the field that contains an IP address as "ip".

Using the preceding order example, you can extend it by adding the customer IP:

  "customer_ip": {
    "type": "ip",
    "store": "yes"
  }

The IP must be in the standard point notation form, as shown in the following code:

"customer_ip":"19.18.200.201"

How it works...

When ElasticSearch is processing a document, if a field is an IP one, it tries to convert its value to a numerical form and generate tokens for fast value searching.

The IP has special properties:

  • index...

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