Splunk® Enterprise

User Manual

Download manual as PDF

Splunk version 4.x reached its End of Life on October 1, 2013. Please see the migration information.
This documentation does not apply to the most recent version of Splunk. Click here for the latest version.
Download topic as PDF

Add sparklines to your search results

If you are working with stats and chart searches, you can increase their usefulness and overall information density by adding sparklines to their result tables. Sparklines are inline charts that appear within table cells in search results, and are designed to display time-based trends associated with the primary key of each row.

For example, say you have this search, set to run over events from the past 15 minutes:

index=_internal | chart count by sourcetype

This search returns a two-column results table that shows event counts for the source types that have been indexed to _internal in the last 15 minutes. The first column lists each sourcetype found in the past hour's set of _internal index events; this is the primary key for the table. The second column, count, displays the event counts for each listed source type:

Sparklines basic example-1.png

You can add sparklines to the results of this search by adding the sparkline function to the search itself:

index=_internal | chart sparkline count by sourcetype

This results in a table that is almost the same as the preceding one, except that now, for each row you have a sparkline chart that shows the event count trend for each listed source type over the past 15 minutes.

Sparklines basic example-2.png

Now you can easily see patterns in your data that may have been invisible before. Some search activity apparently caused a bump in most index=_internal source types about three quarters into the 15 minute span. And splunkd has what almost looks like a regular heartbeat running over the entire span of time.

Note: Each sparkline in a table displays information in relation to the other events represented in that sparkline, but not in relation to the other sparklines. A peak in one sparkline does not necessarily have the same value as a peak in another sparkline.

Using sparklines with the stats and chart commands

You always use the sparklines feature in conjunction with chart and stats searches, because it is a function of those two search commands. It is not a command by itself. The functionality of sparklines is the same for both search commands.

Note: Sparklines are not available as a dashboard chart visualization by themselves, but you can set up a dashboard panel with a table visualization that displays sparklines. For more information, see the "Visualization reference" topic in this manual.

For more information about the chart and stats commands, including details on the syntax around the sparkline function, see "chart" and "stats" in the Search Reference.

Example:Stats, sparklines, and earthquake data

Here are some examples of stats searches that use sparklines to provide additional information about earthquake data.

These examples use earthquake data downloaded from the USGS Earthquakes website, for the seven-day period extending from September 13-20, 2011. The data is a comma-separated ASCII text file containing the source network (Src), ID (Eqid), version, date, location, magnitude, depth (km) and number of reporting stations (NST) for each earthquake over the last 7 days.

Download the text file, M 2.5+ earthquakes, past 7 days, save it as a CSV file, and upload it to Splunk. Splunk should extract the fields automatically. Note that you'll be seeing data from the 7 days previous to your download, so your results will vary from the ones displayed below.

Let's say you want to use the USGS Earthquakes data to show the regions that had the most earthquakes over the past week, with a column that shows the average quake magnitude for each region. You could use the following search:

source=eqs7day-M2.5.csv | stats sparkline count, avg(Magnitude) by Region | sort count

This search returns the following table, with sparklines that illustrate the quake count over the course of the week for each of the top earthquake regions (in this case, Region is the table's primary key):

Spk quakeCount example.png

Right away you can see differences in quake distribution between the top 10 quake regions. Some areas, like Southern Alaska and the Virgin Islands, had a pretty steady series of quakes, while the Fox Islands and Vanuatu experienced their seismic activity all at one point.

You can easily get the minimum and maximum count for a particular region by mousing over the sparkline; in this example you can see that in Southern Alaska, the minimum count of quakes experienced in a single day during the 7-day period was 1, while the maximum count per day was 6.

But what if you want your sparkline to represent not only the earthquake count, but also the relative average magnitude of the quakes affecting each region? In other words, how can you make the sparkline line chart represent average quake magnitude for each "time bucket" (segment) of the chart?

Try a search like this:

source=eqs7day-M2.5.csv | stats sparkline(avg(Magnitude),6h) as magnitude_trend, count, avg(Magnitude) by Region | sort count

This search produces a sparkline for each region that shows the average quake magnitude for the quake events that fall into each segment of the sparkline..

But it does a bit more than that. It also asks that the sparkline divide itself up into smaller chunks of time. The preceding table had a sparkline that was divided up by day, so each data point in the sparkline represented an event count for a full 24 hour period. This is why those sparklines were so short.

The addition of the 6h to the search language overrides this default and makes Splunk display sparklines that are broken up into discrete six-hour chunks, which makes it easier to see the distribution of events along the sparkline for the chosen time range.

The search also renames the sparkline column as "magnitude_trend" to make it easier to understand.

Spk magTrend example.png

Now you can see that the quakes off Honshu, Japan, were all of roughly similar magnitude, while the quakes in Puerto Rico varied in intensity. And it's now easier to see that Southern California's relatively mild quakes hit at the start and end of the 7-day period. You can also discern that the quakes in the Virgin Islands didn't occur with the steady frequency that the previous search suggested, while the quakes off Honshu were slightly more regular than previously indicated.

Create and use search macros
About capturing knowledge

This documentation applies to the following versions of Splunk® Enterprise: 4.3, 4.3.1, 4.3.2, 4.3.3, 4.3.4, 4.3.5, 4.3.6, 4.3.7


Minor point - the last paragraph makes reference to quakes off of Honshu, Japan, but Honshu is not listed in any of the screenshots.

February 7, 2012

Was this documentation topic helpful?

Enter your email address, and someone from the documentation team will respond to you:

Please provide your comments here. Ask a question or make a suggestion.

You must be logged into splunk.com in order to post comments. Log in now.

Please try to keep this discussion focused on the content covered in this documentation topic. If you have a more general question about Splunk functionality or are experiencing a difficulty with Splunk, consider posting a question to Splunkbase Answers.

0 out of 1000 Characters