Book Image

Improving Your Splunk Skills

By : James D. Miller, Paul R. Johnson, Josh Diakun, Derek Mock
Book Image

Improving Your Splunk Skills

By: James D. Miller, Paul R. Johnson, Josh Diakun, Derek Mock

Overview of this book

Splunk makes it easy for you to take control of your data and drive your business with the cutting edge of operational intelligence and business analytics. Through this Learning Path, you'll implement new services and utilize them to quickly and efficiently process machine-generated big data. You'll begin with an introduction to the new features, improvements, and offerings of Splunk 7. You'll learn to efficiently use wildcards and modify your search to make it faster. You'll learn how to enhance your applications by using XML dashboards and configuring and extending Splunk. You'll also find step-by-step demonstrations that'll walk you through building an operational intelligence application. As you progress, you'll explore data models and pivots to extend your intelligence capabilities. By the end of this Learning Path, you'll have the skills and confidence to implement various Splunk services in your projects. This Learning Path includes content from the following Packt products: Implementing Splunk 7 - Third Edition by James Miller Splunk Operational Intelligence Cookbook - Third Edition by Paul R Johnson, Josh Diakun, et al
Table of Contents (21 chapters)
Title Page

When to use a summary index

When the question you want to answer requires looking at all or most events for a given source type, the number of events can become huge very quickly. This is what is generally referred to as a dense search.

For example, if you want to know how many page views happened on your website, the query to answer this question must inspect every event. Since each query uses a processor, we are essentially timing how fast our disk can retrieve the raw data and how fast a single processor can decompress that data. Doing a little math, we get the following:

1,000,000 hits per day /

10,000 events processed per second =

100 seconds

If we use multiple indexers, or possibly buy much faster disks, we can cut this time, but only linearly. For instance, if the data is evenly split across four indexers, without changing disks, this query will take roughly 25 seconds.

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