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Timechart Versus Stats

Timechart and stats are very similar in many ways. They have access to the same (mostly) functions, and they both do aggregation. The biggest difference lies with how Splunk thinks you’ll use them. Timechart is much more user friendly. You can run a | timechart span=1d sum(MB) by series and it will create take each series and create a column name for it. It’s appropriate for tossing in a SimpleResultsTable, and then tossing in front of the user.

Stats is more behind the scenes. As a rule, you’re going to have more luck doing calculations and the like with stats than you are with timechart, because it will retain the column names you’re familiar with.

Converting
Converting from Timechart to Stats, when you figure out something isn’t working right, is easy. The biggest difference is that the timechart combines stats and the bucket command. Moving | timechart span=1d sum(MB) by series to stats would yield | bucket _time span=1d | stats sum(MB) by series, _time.

Why Convert
There are two reasons to convert. The first, is that you don’t have a choice. You often can’t do back-to-back timecharts, because the fields will be renamed. Take a look at the first example below, and try replacing the first bucket+stats with a timechart, and you’ll see what I mean.

The other big reason to convert is that Stats offers much better support for three or more dimensions of analysis. Suppose you wanted to analyze the amount of data being put through the license_usage.log, metrics.log and splunkd_access.log files, and get a few different metrics for each. (Following up on the example from Analyzing Trends.) Compare the output of these two searches:

Timechart
index=_internal source="*metrics.log" group="per_source_thruput" series="*license_usage.log" OR series="*metrics.log" OR series="*splunkd_access.log" [email protected] [email protected]
    | eval MB=kb/1024
    | bucket _time span=1h
    | stats sum(MB) as MB by series, _time
    | timechart span=1d avg(MB) as AvgMB, sum(MB) as SumMB, stdev(MB) as StDevMB by series

Stats
index=_internal source="*metrics.log" group="per_source_thruput" series="*license_usage.log" OR series="*metrics.log" OR series="*splunkd_access.log" [email protected] [email protected]
    | eval MB=kb/1024
    | bucket _time span=1h
    | stats sum(MB) as MB by series, _time
    | bucket _time span=1d
    | stats avg(MB) as AvgMB, sum(MB) as SumMB, stdev(MB) as StDevMB by series, _time

Where this really becomes even more useful is when you’re building a dashboard. If I wanted to graph the license usage for each of those files, in three different graphs, I could have my main search be this:

index=_internal source="*metrics.log" group="per_source_thruput" series="*license_usage.log" OR series="*metrics.log" OR series="*splunkd_access.log" [email protected] [email protected]
     | eval MB=kb/1024
     | bucket _time span=1h
     | stats sum(MB) as MB by series, _time
     | bucket _time span=1d
     | stats avg(MB) as AvgMB, sum(MB) as SumMB, stdev(MB) as StDevMB by series, _time

And then put a postprocess for each graph:
| search series="*metrics.log"
     | bucket _time span=1d
     | stats avg(MB) as AvgMB, sum(MB) as SumMB, stdev(MB) as StDevMB by _time

This way, you only have to do one search looking at the data, and you can do the rest of the processing in memory, improving the speed of your dashboards.

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