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

Acceleration


With Splunk, you can search through enormous volumes of data (proportional to the ever-growing number of events to summarize, as well as the number of users accessing the data), potentially increasing the amount of time it takes to complete beyond acceptable lengths.

Big data – summary strategy

Generally speaking, you create summaries of data to report on big data. These summaries are created by background runs of the search on which your report is based. When a user runs a report against data that has been pre-summarized, it runs considerably faster because the summaries it uses are much smaller bits of the total number of events to be searched.

This concept of pre-summarizing events can be used with your reports, data models, and indexes. Right now, we will focus on report summary acceleration.

Report acceleration

As we've already stated, summary acceleration uses summaries of events to speed up completion times for certain kinds of reports, and report summary acceleration uses...