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

Summary Indexes and CSV Files

As the number of events retrieved by a query increases, the performance decreases linearly. Summary indexing allows you to calculate the statistics in advance and then run reports against these roll ups, dramatically increasing performance.

In this chapter, we will cover the following topics:

  • Understanding summary indexes
  • When to use a summary index
  • When not to use a summary index
  • Populating summary indexes with saved searches
  • Using summary index events in a query
  • Using sistats, sitop, and sitimechart
  • How latency affects summary queries
  • How and when to backfill summary data
  • Reducing summary index size
  • Calculating top for a large time frame
  • Using CSV files to store transient data
  • Speeding up queries and backfilling