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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

Using stats to aggregate values


While top is very convenient, stats is extremely versatile. The basic structure of a stats statement is:

stats functions by fields

Many of the functions available in stats mimic similar functions in SQL or Excel, but there are many functions unique to Splunk. The simplest stats function is count. Given the following query, the results will contain exactly one row, with a value for the field count:

sourcetype="impl_splunk_gen" error | stats count

Using the by clause, stats will produce a row per unique value for each field listed, which is similar to the behavior of top. Run the following query:

sourcetype="impl_splunk_gen" error | stats count by logger user

It will produce a table like that shown in the following screenshot:

There are a few things to notice about these results:

  1. The results are sorted against the values of the "by" fields, in this case logger followed by user. Unlike top, the largest value will not necessarily be at the top of the list. You can sort...

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