Book Image

Implementing Splunk 7, Third Edition - Third Edition

Book Image

Implementing Splunk 7, Third Edition - Third Edition

Overview of this book

Splunk is the leading platform that fosters an efficient methodology and delivers ways to search, monitor, and analyze growing amounts of big data. This book will allow you to implement new services and utilize them to quickly and efficiently process machine-generated big data. We introduce you to all the new features, improvements, and offerings of Splunk 7. We cover the new modules of Splunk: Splunk Cloud and the Machine Learning Toolkit to ease data usage. Furthermore, you will learn to use search terms effectively with Boolean and grouping operators. You will learn not only how to modify your search to make your searches fast but also how to use wildcards efficiently. Later you will learn how to use stats to aggregate values, a chart to turn data, and a time chart to show values over time; you'll also work with fields and chart enhancements and learn how to create a data model with faster data model acceleration. Once this is done, you will learn about XML Dashboards, working with apps, building advanced dashboards, configuring and extending Splunk, advanced deployments, and more. Finally, we teach you how to use the Machine Learning Toolkit and best practices and tips to help you implement Splunk services effectively and efficiently. By the end of this book, you will have learned about the Splunk software as a whole and implemented Splunk services in your tasks at projects
Table of Contents (19 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Using sistats, sitop, and sitimechart


First, let's define some new functions:

  • sistats: sistats is the summary indexing version of the stats command, which calculates the aggregate statistics over the dataset
  • sitop: This is the summary indexing version of the top command, which returns the most frequent value of a field or a combination of fields
  • sitimechart: sitimechart is the summary indexing version of the timechart command, which creates a time series chart visualization with the corresponding table of statistics

So far, we have used the stats command to populate our summary index. While this works very well, the si* variants have a couple of advantages:

  • The remaining portion of the query does not have to be rewritten. For instance, stats count still works as if you were counting the raw events.
  • The stats functions that require more data than what happened in that slice of time, will still work. For example, if your time slices each represent an hour, it is not possible to calculate the average...