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

Related Content you might be interested in

Current Title:

Small book image
Improving Your Splunk Skills
James D. Miller | Paul R. Johnson | Josh Diakun | Derek Mock
Aug, 2019 | 11 hours 20 minutes
No titles found
Table of Contents (21 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Improving Your Splunk Skills
About Packt
About Packt
Why subscribe?
Packt.com
Contributors
Contributors
About the authors
Packt is searching for authors like you
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
The Splunk Interface
The Splunk Interface
Logging in to Splunk
The home app
The top bar
The Search & Reporting app
Using the time picker
Using the field picker
The settings section
Splunk Cloud
Try before you buy
A quick cloud tour
The top bar in Splunk Cloud
Splunk reference app – PAS
Universal forwarder
eventgen
Next steps
2
Understanding Search
Understanding Search
Using search terms effectively
Boolean and grouping operators
Clicking to modify your search
Using fields to search
Using wildcards efficiently
All about time
Making searches faster
Sharing results with others
Searching job settings
Saving searches for reuse
Creating alerts from searches
Event annotations
3
Tables, Charts, and Fields
Tables, Charts, and Fields
About the pipe symbol
Using top to show common field values
Using stats to aggregate values
Using chart to turn data
Using timechart to show values over time
Working with fields
Chart enhancements in version 7.0
4
Data Models and Pivots
Data Models and Pivots
What is a data model?
What does a data model search?
Acceleration in version 7.0
Creating a data model
Lookup attributes
What is a pivot?
A quick example
Sparklines
5
Simple XML Dashboards
Simple XML Dashboards
The purpose of dashboards
Using wizards to build dashboards
Converting the panel to a report
Back to the dashboard
Editing XML directly
UI examples app
Building forms
Features replaced
Autorun dashboard
Scheduling the generation of dashboards
6
Extending Search
Extending Search
Using tags to simplify search
Using event types to categorize results
Using lookups to enrich data
Using macros to reuse logic
Creating workflow actions
Using external commands
7
Working with Apps
Working with Apps
Defining an app
Included apps
Installing apps
Building your first app
Editing navigation
Customizing the appearance of your app
Object permissions
App directory structure
Self-service app management
8
Building Advanced Dashboards
Building Advanced Dashboards
Reasons for working with advanced XML
Reasons for not working with advanced XML
Development process
Advanced XML structure
Converting simple XML to advanced XML
Module logic flow
Understanding layoutPanel
Reusing a query
Using intentions
Creating a custom drilldown
Third-party add-ons
9
Summary Indexes and CSV Files
Summary Indexes and CSV Files
Understanding summary indexes
When to use a summary index
When to not 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
10
Configuring Splunk
Configuring Splunk
Locating Splunk configuration files
The structure of a Splunk configuration file
The configuration merging logic
An overview of Splunk.conf files
User interface resources
11
Play Time – Getting Data In
Play Time – Getting Data In
Introduction
Indexing files and directories
Getting data through network ports
Using scripted inputs
Using modular inputs
Using the Universal Forwarder to gather data
Receiving data using the HTTP Event Collector
Getting data from databases using DB Connect
Loading the sample data for this book
Data onboarding – defining field extractions
Data onboarding - defining event types and tags
Installing the Machine Learning Toolkit
12
Building an Operational Intelligence Application
Building an Operational Intelligence Application
Introduction
Creating an Operational Intelligence application
Adding dashboards and reports
Organizing the dashboards more efficiently
Dynamically drilling down on activity reports
Creating a form for searching web activity
Linking web page activity reports to the form
Displaying a geographical map of visitors
Highlighting average product price
Scheduling the PDF delivery of a dashboard
13
Diving Deeper – Advanced Searching, Machine Learning and Predictive Analytics
Diving Deeper – Advanced Searching, Machine Learning and Predictive Analytics
Introduction
Calculating the average session time on a website
Calculating the average execution time for multi-tier web requests
Displaying the maximum concurrent checkouts
Analyzing the relationship of web requests
Predicting website traffic volumes
Finding abnormally-sized web requests
Identifying potential session spoofing
Detecting outliers in server response times
Forecasting weekly sales
14
Speeding Up Intelligence – Data Summarization
Speeding Up Intelligence – Data Summarization
Introduction
Calculating an hourly count of sessions versus completed transactions
Backfilling the number of purchases by city
Displaying the maximum number of concurrent sessions over time
15
Above and Beyond – Customization, Web Framework, HTTP Event Collector, REST API, and SDKs
Above and Beyond – Customization, Web Framework, HTTP Event Collector, REST API, and SDKs
Introduction
Customizing the application navigation
Adding a Sankey diagram of web hits
Developing a tag cloud of purchases by country
Adding Cell Icons to Highlight Average Product Price
Remotely querying Splunk's REST API for unique page views
Creating a Python application to return unique IP addresses
Creating a custom search command to format product names
Collecting data from remote scanning devices
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

When to not use a summary index

There are several cases where summary indexes are either inappropriate or inefficient. Consider the following:

  • When you need to see the original events: In most cases, summary indexes are used to store aggregate values. A summary index could be used to store a separate copy of events, but this is not usually the case. The more events you have in your summary index, the less advantage it has over the original index.
  • When the possible number of categories of data is huge: For example, if you want to know the top IP addresses seen per day, it may be tempting to simply capture a count of every IP address seen. This can still be a huge amount of data, and may not save you a lot of search time, if any. Likewise, simply storing the top 10 addresses per slice of time may not give an accurate picture over a long period of time. We will discuss this scenario...
Previous Section
End of Section 4
Next Section