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Implementing Splunk: Big Data Reporting and Development for Operational Intelligence

You're reading from  Implementing Splunk: Big Data Reporting and Development for Operational Intelligence

Product type Book
Published in Jan 2013
Publisher Packt
ISBN-13 9781849693288
Pages 448 pages
Edition 1st Edition
Languages
Concepts
Author (1):
VINCENT BUMGARNER VINCENT BUMGARNER
Profile icon VINCENT BUMGARNER

Table of Contents (19) Chapters

Implementing Splunk: Big Data Reporting and Development for Operational Intelligence
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. The Splunk Interface 2. Understanding Search 3. Tables, Charts, and Fields 4. Simple XML Dashboards 5. Advanced Search Examples 6. Extending Search 7. Working with Apps 8. Building Advanced Dashboards 9. Summary Indexes and CSV Files 10. Configuring Splunk 11. Advanced Deployments 12. Extending Splunk Index

Working with fields


All of the fields we have used so far were either indexed fields (such as host, sourcetype, and _time) or fields that were automatically extracted from key=value pairs. Unfortunately, most logs don't follow this format, especially for the first few values in each event. New fields can be created either inline, by using commands, or through configuration.

A regular expression primer

Most of the ways to create new fields in Splunk involve regular expressions. There are many books and sites dedicated to regular expressions, so we will only touch upon the subject here.

Given the log snippet ip=1.2.3.4, let's pull out the subnet (1.2.3) into a new field called subnet. The simplest pattern would be the literal string:

ip=(?P<subnet>1.2.3).4

This is not terribly useful as it will only find the subnet of that one IP address. Let's try a slightly more complicated example:

ip=(?P<subnet>\d+\.\d+\.\d+)\.\d+

Let's step through this pattern:

  • ip= simply looks for the raw string...

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