#2 Splunk sub(Commands) [eval, trim, chart, showperc, stats, avg]

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TRIM: Basically it helps you to create more meaning full data from existing data i.e. it helps you to remove noise from the results.
  • Example: index=idx_messages sourcetype=linux_logs | eval new_rt=trim(replace(response_time, "ms.", ""))


  • In above example, response_time is an existing field which consists of some unwanted data like "ms." which we don't want. So, by using eval and trim we can remove that unwanted data.
  • If you will notice, above search will create a new field called new_rt which contains our intended results i.e. without "ms."


CHART: It basically results your finished data in a table format, further that data can be used to visualize via different mechanism.
  • Example: index=idx_messages sourcetype=linux_logs | chart count by date client useother=f




  • Honestly the example is not so good but I believe, you are able to reach to the crux of it, i.e. it will show you which client on which date made how many hits?
  • Also it can be helpful to find out, if there are some malicious client which are making hits on daily basis, so that an appropriate action can be taken on them.

SHOWPERC: Helps you to show percentage of your fetched results.
  • Example: index=_internal | top component limit=5 showperc=t





  • PS: Replacing showperc=t to showperc=f will remove the percentage column.

STATS: This commands have many usage, it can help you to calculate average, count, sum etc.  
  • Example: index=idx_messages | stats count as "Total Count of events"
    • Above example helps you to count total number of events based on the time range selected.




  • Example: index=idx_messages | stats count as "Total count of events per day" by date
    • Above example is a extension of query used before, the difference is the statistics are created based on daily basis. 





  • Example: index=idx_messages sourcetype=linux_logs | eval new_rt=trim(replace(response_time, "ms.", "")) | eval new_rt=new_rt/1000 | stats avg(new_rt) AS "Response Time(Secs)" by date
    • Above example is bit of a complex query, firstly with the help of eval trim we are removing some noise from the events (i.e. "ms.") then converting milliseconds into seconds with the help of eval command (since the response time was in milliseconds), then calculating the average of response time on daily basis with the help of stats and avg function.
    • Another point to add, "AS" clause is basically used to rename things, in this example we are renaming "avg(new_rt)" AS "Response Time(Secs)" i.e. to a more understandable name.

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