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Implementing Splunk: Big Data Reporting and Development for Operational Intelligence

You're reading from  Implementing Splunk: Big Data Reporting and Development for Operational Intelligence

Product type Book
Published in Jan 2013
Publisher Packt
ISBN-13 9781849693288
Pages 448 pages
Edition 1st Edition
Languages
Concepts
Author (1):
VINCENT BUMGARNER VINCENT BUMGARNER
Profile icon VINCENT BUMGARNER

Table of Contents (19) Chapters

Implementing Splunk: Big Data Reporting and Development for Operational Intelligence
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. The Splunk Interface 2. Understanding Search 3. Tables, Charts, and Fields 4. Simple XML Dashboards 5. Advanced Search Examples 6. Extending Search 7. Working with Apps 8. Building Advanced Dashboards 9. Summary Indexes and CSV Files 10. Configuring Splunk 11. Advanced Deployments 12. Extending Splunk 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, very quickly the number of events can become huge. 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:

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 times dramatically. Let's assume we have calculated hit counts per five...

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