Splunk Stats | A Complete Guide On Splunk Stats (2024)

What is Splunk

Splunk is a very well-known platform for the big data associated with its collections as well as for analytics. The main requirement of Splunk is to extract insights from a huge amount of data. It also helps to monitor, analyze and visualize the data generated from the machine data algorithms in real-time. The user can also perform processes like indexing, capturing, and relating the machine-derived data by putting it in a container for the searching process which helps to produce alerts, graphs, visuals, and dashboards. It helps in contributing to the building of infrastructure and business related to the IT field.

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About Splunk stats command

The Splunk stats command is a command that is used for calculating the summary of stats on the basis of the results derived from a search history or some events that have been retrieved from some index. This command only returns the field that is specified by the user, as an output. A user can use more than one function by invoking the stats command, however, a user can make the use of BY clause only once. A user can perform a lot of functions such as finding the average, grouping the results by a field, performing multiple aggregations, finding the range, finding mean and variance, etc. using the Splunk stats command.

Splunk-stats commands

1. Finding the average: a user can use the avg() function for finding the average of a numeric field the function takes up the name of the field as the input. If the user does not use the BY clause, he gives only one record showing the average number of the field containing all the events. However, if the user uses a BY clause, he will get more than one row that will depend on the grouping of the fields along with an additional field.

Let us see the example below and try to find the average byte size of a file that is grouped by an HTTP code. The syntax is given below:

host = ”web application” | stats avg(bytes) by status

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2. Finding mean and variance: We define mean as an average of all the given numbers whereas variance is the average of the difference squared from the value of the mean. Both these functions are also calculated in a much similar way to the average in the above section. However, the functions that we use are mean() and var().

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The syntax is given below:

host = ”web application” | stats mean(bytes) var(bytes) by status

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The output depicts the variance and the mean of all the field values which are named bytes and all of them are organized by the HTTP events.

3. Finding Range: This function is used for displaying the range of values of a field which is numeric type. The function that is used to find the range of several values is range(). Along with the range function, we make use of min() and max() for calculating the maximum and minimum of the ranges. We can also calculate the range by first calculating the max() and then min() and then subtracting both so that we get the desired range. The syntax is given below:

host = ”web application” | stats min(bytes) max(bytes) range(bytes) by status

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Stats function options

The stats function options help the user in calculating the aggregation statistics with the results set, like the count, average, or sum. The stats functions are very much like SQL aggregation. If the user uses the stats without the BY clause, he will only get a single row as an output. But if he uses a BY clause, then one row per different value will be returned as an output. Everything that a stats command can calculate, will be based on the statistics of the fields present in the events.

Syntax:

Basic syntax:

 stats (stats_fucntion (field_anme) [AS field]).......... [BY_clause field_list]

Complete syntax:

 stats [partition=] [all_numbers=] [delim=] ( .... | .... ) [ ]

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Function 1: stats-agg-term

The syntax for this function is ( | ) [AS]. The function is called the function for statical aggregation. A user can apply this function to a single field, a set of fields, or an eval function as well. This function outs the field in the form of a new field with a name that can be specified by the user. He can also make use of wildcard characters as field names.

Function 2: allnum

The syntax for this function is allnum=. If the value of allnum= computes equals true, then the user can perform numerical stats on the numeric field values. This function is false by default.


Function 3: delim

The syntax for this function is delim=. It shows the delamination of the values present in the list() or values() field aggregation. This function has a default value which is a single space.

Function 4: By clause

The syntax for this function is BY. a user can make use of wildcard characters as multiple field names using the same name. The only need is for the specification of each field to be separately defined.

Function 5: partitions

The syntax for this function is partitions=. A user can use this function to partition the input data that is based on split type multithreaded reduce. This function has a default value which is1.

Function 6: sparkline-agg-term

The syntax for this function is [AS]. This function is called the sparkline aggregation function. The [AS] clause is used for placing the outcome in a new field with any name that the user wishes. He can also make use of wildcard characters as field names.

Other stats functions: avg(), model(), count(), min(0, exactperc(), median(), first(), latest(), last(), c(), dc(), values(), upperperc(), varp(), var(), etc.

Sparkline function options

These functions help in working on majorly 3 fields which are win-loss, columns as well as line. They are used for the visualization of continuous data. For example, if a user wants to compare two types of data in a scenario, etc. They are only visible as table cells and mainly display the outcomes which are related to time-based scenarios. There exists a primary key for every row in a sparkline function.

Function 1: sparkline-agg

The syntax for this function is sparkline count() and sparkline ((),). In this function, there exists a sparkline specific that helps to specify the field’s aggregation function. If the user does not specify any timespan, then it picks the timespan of his own mostly based on the search time. A user can also make use of wildcard characters as field names.

Function 2: sparkline-func

The syntax for this function is sparkline count(), c(), dc(), avg(), stdev(), sum(), varp(), var(), min(0, sumsq(), range(), max().

These functions help the user in generating the sparkline values. The sparkline values are formed by applying the function to all the events present in the aggregation scenario.

Memory usage with functions

There can be a lot of functions that are expensive from the memory point of view as compared to the other functions. For an instance, a function such as distinct_count needs a lot more memory as compared to the count() function. The function contains a lot of values as well as list functions which also tend to require a huge amount of memory.

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Conclusion

In this article, we have discussed Splunk stats commands in detail. Splunk is a very well-known platform for the big data associated with its collections as well as for analytics. The main requirement of Splunk is to extract insights from a huge amount of data. We have also discussed a few commands for performing methods such as finding the average of the numbers, finding the range, etc. Then we talked about various Spunk and sparkline function options such as C90, count(), stdev(), avg(), etc., along with their uses.

Related Articles:

  • Splunk API
  • Splunk Enterprise
Splunk Stats | A Complete Guide On Splunk Stats (2024)

FAQs

What is the average in Splunk stats? ›

Finding Average

We can find the average value of a numeric field by using the avg() function. This function takes the field name as input. Without a BY clause, it will give a single record which shows the average value of the field for all the events.

How do I get stats in Splunk? ›

The SPL2 stats command calculates aggregate statistics, such as average, count, and sum, over the incoming search results set. This is similar to SQL aggregation. If the stats command is used without a BY clause, only one row is returned, which is the aggregation over the entire incoming result set.

What is the difference between stats and eventstats in Splunk? ›

Eventstats calculates a statistical result same as stats command only difference is it does not create statistical results, it aggregates them to the original raw data. Streamstats command uses events before the current event to compute the aggregate statistics that are applied to each event.

What is the difference between stats and eval? ›

Difference between stats and eval commands

The command Stats measures statistics in your events based on fields. The eval command uses existing fields and an optional expression to construct new fields in your events.

What is the best statistical average? ›

Nevertheless, the arithmetic mean is by far the most useful of statistical averages.

What is an average in stats? ›

In statistics, an average is defined as the number that measures the central tendency of a given. set of numbers. There are a number of different averages including but not limited. to: mean, median, mode and range.

What is the difference between stats and chart in Splunk? ›

In Summary

Use the stats command when you want to specify 3 or more fields in the BY clause. Use the chart command when you want to create results tables that show consolidated and summarized calculations. Use the chart command to create visualizations from the results table data.

Where do I find stats? ›

Statistical Sites on the World Wide Web
  • Bureau of Economic Analysis.
  • Bureau of Justice Statistics.
  • Bureau of Labor Statistics.
  • Bureau of Transportation Statistics.
  • U.S. Census Bureau.
  • Economic Research Service.
  • Energy Information Administration.
  • National Agricultural Statistics Service.
Jun 11, 2024

What are the common functions used with stats command? ›

Common statistical functions in Excel: Some of the most commonly used statistical functions in Excel include the AVERAGE function, MAX, MIN, SUM, COUNT, and STDEV.

What are the 4 types of searches in Splunk by performance? ›

How search types affect Splunk Enterprise performance
Search typeRef. indexer throughputPerformance impact
DenseUp to 50,000 matching events per second.CPU-bound
SparseUp to 5,000 matching events per second.CPU-bound
Super-sparseUp to 2 seconds per index bucket.I/O bound
RareFrom 10 to 50 index buckets per second.I/O bound

What is Streamstats in Splunk stats? ›

Streamstats builds upon the basics of the stats command but it provides a way for statistics to be generated as each event is seen. This can be very useful for things like running totals or looking for averages as data is coming into the result set.

What is the difference between stats and transaction commands in Splunk? ›

Stats provides the aggregation. transaction provides the unique number / count. Like you perform 10 steps as part of one transaction.

What is coalesce in Splunk? ›

Coalesce takes the first non-null value to combine. In these use cases you can imagine how difficult it would be to try and build a schema around this in a traditional relational database, but with Splunk we make it easy.

How do you evaluate stats? ›

Factors to Consider When Evaluating Statistics
  1. Who collected it?
  2. Was it an individual or organization or agency?
  3. The data source and the reporter or citer are not always the same. ...
  4. If the data are repackaged, is there proper documentation to lead you to the primary source?
Feb 5, 2024

How to check if a field exists in Splunk? ›

there is a SPL function called isnull() and isnotnull() you can use these together with the if function to check if fields/fieldvalues exist or not. Hi @avtandil, there is a SPL function called isnull() and isnotnull() you can use these together with the if function to check if fields/fieldvalues exist or not.

What is average in statistical function? ›

Statistical functions (reference)
FunctionDescription
AVERAGE functionReturns the average of its arguments
AVERAGEA functionReturns the average of its arguments, including numbers, text, and logical values
AVERAGEIF functionReturns the average (arithmetic mean) of all the cells in a range that meet a given criteria
108 more rows

What is the average of the statistical data? ›

The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set. The median is the middle value when a data set is ordered from least to greatest. The mode is the number that occurs most often in a data set.

What is average in data interpretation? ›

Averages can be defined as the central value in a set of data. The average can be calculated simply by dividing the sum of all values in a set by the total number of values. In other words, an average value represents the middle value of a data set.

What is the average performance in statistics? ›

The average is equal to the sum of all data points divided by the number of items, where 'n' represents the number of data samples. Median is the middle score for a set of data that has been arranged in the order of magnitude. Let us consider a set of data point as [12, 31, 44, 47, 22, 18, 60, 75, 80].

References

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