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

When to not use a summary index


There are several cases where summary indexes are either inappropriate or inefficient. Consider the following:

  • When you need to see the original events: In most cases, summary indexes are used to store aggregate values. A summary index could be used to store a separate copy of events, but this is not usually the case. The more events you have in your summary index, the less advantage it has over the original index.
  • When the possible number of categories of data is huge: For example, if you want to know the top IP addresses seen per day, it may be tempting to simply capture a count of every IP address seen. This can still be a huge amount of data, and may not save you a lot of search time, if any. Likewise, simply storing the top 10 addresses per slice of time may not give an accurate picture over a long period of time. We will discuss this scenario under the Calculating top for a large time frame section.
  • When it is impractical to slice the data across sufficient...