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

Scheduling the generation of dashboards


As we stepped through the wizard interface to create panels, we accepted the default value of running the search each time the dashboard loads. As we mentioned earlier, this means that the user is penalized each and every time the dashboard is loaded in their web browser. It is silly (and a waste of resources) to rerun what may be multiple searches that are within a dashboard panel if the data that the search is based upon does not change very often. For example, if the indexed data is updated every evening, then rerunning a search on that data multiple times within the same day will not yield different results and would be a waste of resources.

A more prudent approach would be to convert the dashboard panels to not use inline, executed-at-load-time searches but reference reports instead (earlier in this chapter, we covered Convert to Report), or make it use scheduled queries.

If we use reports or scheduled queries in our dashboard, when the dashboard...