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 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.

If we use summary indexing, we should be able to improve our time dramatically.

Let's assume we have calculated the hit...