Overview of the New Charting Enhancements in Splunk 7.0

Charts are highly configurable in Splunk and in Splunk 7.0 they have added more charting options to use in your dashboards. These charting enhancements improve metrics and multi-series monitoring use cases while elevating user experience.

In this blog post, I will provide an overview of the new charting options available with Splunk 7.0 and give you examples you can use for reference. 


The first charting option allows you to change the line width of your charts in pixels.   

In the the XML example below, I've taken it a little further...

Does Your WebCenter Sites Deployment Need a Health Check?

Just as you and I (should) go to the doctor regularly for checkups, major IT systems like WebCenter Sites should also get an occasional health check to ensure that they are operating at peak efficiency. As a complicated enterprise product, there are many potential issues that can impact WebCenter Sites’ performance, uptime, and ease of use. Some issues result from misconfigured settings, or problems that develop over time, including: performance issues due to memory usage, disk space, and database size.

If your environments are experiencing unexpected downtimes and users (or...

Controllers, At Last!

The buzz-worthy release of Oracle's WebCenter Sites 12c gives us many reasons to perk up and take note. Among the unveiled features is a long-overdue Model-View-Controller framework; this shiny, new implementation finally provides developers with a clear and clean path for wiring together and rendering content. In the past, we had described a few work-arounds that were viable stand-ins for this piece...

Splunking The Billboard Hot 100 with help from the Spotify API

There's a lot of data out there and once we put it into Splunk, there's a lot of interesting information we can pull out of it, so why not have a trip down memory lane and see what sort of songs pop up when going through the Billboard Hot 100 charts from now back to 2000?

First, I found a scraper for the data - thank you Allen Guo for your Billboard charts scraper - and output the data in this format: 

date | title | artist | weeks | delta | current | peak | previous | spotifyID

I did so with this python script:


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