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 in version 7.0


Prior to and along with the release of version 7.0, Splunk posted material that touted "optimizations to core search technology," promising decreases in the time and resources required to run data model accelerations as well as accelerated searches with faster search and DMA performance.

From reported experiences, Splunk Enterprise users appear to be constantly gaining threefold improvements on their data model acceleration time.

Note

Note: For other types of searches, search performance improvements of two to ten times have been reported.

Splunk indicates that version 7.0 uses supplementary parallelization to convert sequential processing into multi-threaded processing in order to utilize multiple processors simultaneously in a shared-memory. And it uses improved refactored techniques to improve search performance for some types of searches or (at minimal) achieve the same performance using one-third of the previously required resources.

To be clear, these improvements...