Search Reference

 


chart

chart

Description

The chart command is a transforming command that returns your results in a tabular data structure that can be used to display the data as a chart, such as a column, line, area, or pie chart. See the Visualization Reference in the Dashboards and Visualizations manual.

You must specify a statistical function in the chart command. See "Statistical and charting functions" in the Search Reference.

Syntax

chart [sep=<string>] [format=<string>] [cont=<bool>] [limit=<int>] [agg=<stats-agg-term>] ( <stats-agg-term> | <sparkline-agg-term> | <eval-expression>...) [ by <field> (<bins-option> )... [<split-by-clause>] ] | [ over <field> (<bins-option>)... (by <split-by-clause>] ]

Required arguments

agg
Syntax: agg=<stats-agg-term> | agg=<stats-agg-term>
Description: Specify an aggregator or function. For a list of stats functions with descriptions and examples, see "Statistical and charting functions".
sparkline-agg-term
Syntax: <sparkline-agg> [AS <wc-field>]
Description: A sparkline specifier. Optionally, rename the result to a new field.
eval-expression
Syntax: <eval-math-exp> | <eval-concat-exp> | <eval-compare-exp> | <eval-bool-exp> | <eval-function-call>
Description: A combination of literals, fields, operators, and functions that represent the value of your destination field. For more information, see the "Evaluation functions".
For these evaluations to work, your values need to be valid for the type of operation. For example, with the exception of addition, arithmetic operations may not produce valid results if the values are not numerical. Additionally, Splunk can concatenate the two operands if they are both strings. When concatenating values with a period, the search treats both values as strings regardless of their actual type.

Optional arguments

bins-option
Syntax: bins | span | <start-end>
Description: Discretization options. See the bin command reference.
Default: bins=300
cont
Syntax: cont=<bool>
Description: If false, replot the x-axis so that a noncontinuous sequence of x-value buckets shows up adjacently in the output. If true (the default), some buckets that have no values will show up with a count of 0 or null fields etc.
format
Syntax: format=<string>
Description: Used to construct output field names when multiple data series are used in conjunction with a split-by-field. format takes precedence over sep and allows you to specify a parameterized expression with the stats aggregator and function ($AGG$) and the value of the split-by-field ($VALUE$).
limit
Syntax: limit=<int>
Description: Specifies the limit for series filtering. When you set limit=N, the top N values are filtered based on the sum of each series. If limit=0, no filtering occurs.
single-agg
Syntax: count | <stats-func>(<field>)
Description: A single aggregation applied to a single field. Can be an evaluated field. No wildcards are allowed. The field must be specified, except when using the special count aggregator that applies to events as a whole.
sep
Syntax: sep=<string>
Description: Used to construct output field names when multiple data series are used in conjunctions with a split-by field. This is equivalent to setting format to $AGG$<sep>$VALUE$.
split-by-clause
Syntax: <field> (<tc-option>)* [<where-clause>]
Description: Specifies a field to split by. If field is numerical, default discretization is applied; discretization is defined with tc-option.

Statistical functions

stats-agg-term
Syntax: <stats-func>( <evaled-field> | <wc-field> ) [AS <wc-field>]
Description: A statistical specifier optionally renamed to a new field name. The specifier can be by an aggregation function applied to a field or set of fields, or an aggregation function applied to an arbitrary eval expression.
stats-function
Syntax: avg() | c() | count() | dc() | distinct_count() | earliest() | estdc() | estdc_error() | exactperc<int>() | first() | last() | latest() | list() | max() | median() | min() | mode() | p<in>() | perc<int>() | range() | stdev() | stdevp() | sum() | sumsq() | upperperc<int>() | values() | var() | varp()
Description: Functions used with the stats command. Each time you invoke the stats command, you can use more than one function; however, you can only use one by clause.

For a list of stats functions with descriptions and examples, see "Statistical and charting functions".

Sparkline function options

Sparklines are inline charts that appear within table cells in search results and display time-based trends associated with the primary key of each row.

See "Add sparklines to your search results" in the Search Manual.

sparkline-agg
Syntax: sparkline (count(<wc-field>), <span-length>) | sparkline (<sparkline-func>(<wc-field>), <span-length>)
Description: A sparkline specifier, which takes the first argument of an aggregation function on a field and an optional timespan specifier. If no timespan specifier is used, an appropriate timespan is chosen based on the time range of the search. If the sparkline is not scoped to a field, only the count aggregator is permitted.
sparkline-func
Syntax: c() | count() | dc() | mean() | avg() | stdev() | stdevp() | var() | varp() | sum() | sumsq() | min() | max() | range()
Description: Aggregation function to use to generate sparkline values. Each sparkline value is produced by applying this aggregation to the events that fall into each particular time bucket.

Bins options

bins
Syntax: bins=<int>
Description: Sets the maximum number of bins to discretize into. For example, if bin=300, the search finds the smallest bin size that results in no more than 300 distinct bins.
Default: 300
span
Syntax: span=<log-span> | span=<span-length>
Description: Sets the size of each bin, using a span length based on time or log-based span.
<start-end>
Syntax: end=<num> | start=<num>
Description:Sets the minimum and maximum extents for numerical bins. Data outside of the [start, end] range is discarded.

Log span syntax

<log-span>
Syntax: [<num>]log[<num>]
Description: Sets to log-based span. The first number is a coefficient. The second number is the base. If the first number is supplied, it must be a real number >= 1.0 and < base. Base, if supplied, must be real number > 1.0 (strictly greater than 1).

Span length syntax

span-length
Syntax: [<timescale>]
Description: A span length based on time.
<span>
Syntax: <int>
Description: The span of each bin. If using a timescale, this is used as a time range. If not, this is an absolute bucket "length."
<timescale>
Syntax: <sec> | <min> | <hr> | <day> | <month> | <subseconds>
Description: Time scale units.
<sec>
Syntax: s | sec | secs | second | seconds
Description: Time scale in seconds.
<min>
Syntax: m | min | mins | minute | minutes
Description: Time scale in minutes.
<hr>
Syntax: h | hr | hrs | hour | hours
Description: Time scale in hours.
<day>
Syntax: d | day | days
Description: Time scale in days.
<month>
Syntax: mon | month | months
Description: Time scale in months.
<subseconds>
Syntax: us | ms | cs | ds
Description: Time scale in microseconds (us), milliseconds (ms), centiseconds (cs), or deciseconds (ds).

tc options

tc-option
Syntax: <bins-option> | usenull=<bool> | useother=<bool> | nullstr=<string> | otherstr=<string>
Description: Options for controlling the behavior of splitting by a field.
usenull
Syntax: usenull=<bool>
Description: Controls whether or not a series is created for events that do not contain the split-by field.
nullstr
Syntax: nullstr=<string>
Description: If usenull is true, this series is labeled by the value of the nullstr option, and defaults to NULL.
useother
Syntax: useother=<bool>
Description: Specifies if a series should be added for data series not included in the graph because they did not meet the criteria of the <where-clause>.
otherstr
String: otherstr=<string>
Description: If useother is true, this series is labeled by the value of the otherstr option, and defaults to OTHER.

where clause

where clause
Syntax: <single-agg> <where-comp>
Description: Specifies the criteria for including particular data series when a field is given in the tc-by-clause. The most common use of this option is to select for spikes rather than overall mass of distribution in series selection. The default value finds the top ten series by area under the curve. Alternately one could replace sum with max to find the series with the ten highest spikes.This has no relation to the where command.
<where-comp>
Syntax: <wherein-comp> | <wherethresh-comp>
Description: The criteria for the where-clause.
<wherein-comp>
Syntax: (in | notin) (top | bottom)<int>
Description: A grouping criteria for the where-clause. The aggregated series value be in or not in some top or bottom grouping.
<wherethresh-comp>
Syntax: ( < | > ) <num>
Description: A threshold for the where-clause. The aggregated series value must be greater than or less than the specified numeric threshold.

Usage

Evaluation expressions

You can use the eval command with an expression. Unless you specify a split-by clause, the eval expression must be renamed.

X-axis

You can specify which field is tracked on the x-axis of the chart. The x-axis variable is specified with a by field and is discretized if necessary. Charted fields are converted to numerical quantities if necessary.

Unlike the timechart command which generates a chart with the _time field as the x-axis, the chart command produces a table with an arbitrary field as the x-axis.

You can also specify the x-axis field after the over keyword, before any by and subsequent split-by clause. The limit and agg options allow easier specification of series filtering. The limit and agg options are ignored if an explicit where-clause is provided.

Usage of split-by fields

When a split-by field is included, the output is a table where each column represents a distinct value of the split-by field. This is in contrast with the stats command, where each row represents a single unique combination of values of the group-by fields. The number of columns included is limited to 10 by default. You can change the number of columns by including a where-clause.

If you use chart or timechart, you cannot use a field that you specify in a function as your split-by field as well.

For example, you cannot run this search. The field A is specified in the sum function and the by argument. You must specify a different field as in the by argument.

... | chart sum(A) by A span=log2

However, you can work around this problem by using an eval expression. For example:

... | eval A1=A | chart sum(A) by A1 span=log2

Basic Examples

1: Chart the max(delay) for each value of foo

Return max(delay) for each value of foo.

... | chart max(delay) over foo

2: Chart the max(delay) for each value of foo, split by the value of bar

Return max( delay) for each value of foo split by the value of bar.

... | chart max(delay) over foo by bar

3: Chart the ratio of the average to the maximum "delay" for each distinct "host" and "user" pair

Return the ratio of the average (mean) "size" to the maximum "delay" for each distinct "host" and "user" pair.

... | chart eval(avg(size)/max(delay)) AS ratio by host user

4: Chart the maximum "delay" by "size" and separate "size" into bins

Return the maximum "delay" by "size", where "size" is broken down into a maximum of 10 equal sized bins.

... | chart max(delay) by size bins=10

5: Chart the average size for each distinct host

Return the average (mean) "size" for each distinct "host".

... | chart avg(size) by host

6: Chart the number of events, grouped by date and hour

Return the number of events, grouped by date and hour of the day, using span to group per 7 days and 24 hours per half days. The span applies to the field immediately prior to the command.

... | chart count by date_mday span=3 date_hour span=12

Extended Examples

7: Chart the number of different page requests for each Web server

This example uses the sample dataset from the Search Tutorial but should work with any format of Apache Web access log. Download the data set from this topic in the Search Tutorial and follow the instructions to upload it to Splunk.

Chart the number of different page requests, GET and POST, that occurred for each Web server.

sourcetype=access_* | chart count(eval(method="GET")) AS GET, count(eval(method="POST")) AS POST by host

This example uses eval expressions to specify the different field values for the stats command to count. The first clause uses the count() function to count the Web access events that contain the method field value GET. Then, it renames the field that represents these results to "GET" (this is what the "AS" is doing). The second clause does the same for POST events. The counts of both types of events are then separated by the Web server, indicated by the host field, from which they appeared.

This returns the following table:

Searchref stats ex1.png


Click the Visualizations tab to format the report as a column chart:

Searchref chart ex1.2.png


This chart displays the total count of events for each event type, GET or POST, based on the host value. The logarithmic scale is used for the y-axis because of the difference in range of vales between the number of GET and POST events.

8: Chart the number of transactions by duration

This example uses the sample dataset from the Search Tutorial. Download the data set from this topic in the Search Tutorial and follow the instructions to upload it to Splunk. Then, run this search using the time range, All time.

Create a chart to show the number of transactions based on their duration (in seconds).

sourcetype=access_* status=200 action=purchase | transaction clientip maxspan=10m | chart count by duration span=log2

This search uses the transaction command to define a transaction as events that share the clientip field and fit within a ten minute time span. The transaction command creates a new field called duration, which is the difference between the timestamps for the first and last events in the transaction. (Because maxspan=10s, the duration value should not be greater than this.)

The transactions are then piped into the chart command. The count() function is used to count the number of transactions and separate the count by the duration of each transaction. Because the duration is in seconds and you expect there to be many values, the search uses the span argument to bucket the duration into bins of log2 (span=log2). This produces the following table:

Searchref chart ex2.1.png

Click the Visualizations tab to format the report as a column chart:

Searchref chart ex2.2.png


As you would expect, most transactions take between 0 and 2 seconds to complete. Here, it looks like the next greater number of transactions spanned between 256 and 512 seconds (approximately, 4-8 minutes). (In this case however, the numbers may be a bit extreme because of the way that the data was generated.)

9: Chart the average number of events in a transaction, based on transaction duration

This example uses the sample dataset from the Search Tutorial. Download the data set from this topic in the Search Tutorial and follow the instructions to upload it to Splunk. Then, run this search using the time range, All time.

Create a chart to show the average number of events in a transaction based on the duration of the transaction.

sourcetype=access_* status=200 action=purchase | transaction clientip maxspan=30m | chart avg(eventcount) by duration span=log2

This example uses the same transaction defined in Example 2. The transaction command also creates a new field called eventcount, which is the number of events in a single transaction.

The transactions are then piped into the chart command and the avg() function is used to calculate the average number of events for each duration. Because the duration is in seconds and you expect there to be many values, the search uses the span argument to bucket the duration into bins of log2 (span=log2). This produces the following table:

Searchref chart ex3.1.png


Click the Visualizations tab to format the report as a pie chart:

Searchref chart ex3.2.png


Each wedge of the pie chart represents the average number of events in the transactions of the corresponding duration. After you create the pie chart, you can mouseover each of the sections to see these values (in Splunk Web).

10: Chart customer purchases

This example uses the sample dataset from the Search Tutorial. Download the data set from this topic in the Search Tutorial and follow the instructions to upload it to Splunk. Then, run this search using the time range, Other > Yesterday.

Chart how many different people bought something and what they bought at the Buttercup Games online store Yesterday.

sourcetype=access_* status=200 action=purchase | chart dc(clientip) over date_hour by categoryId usenull=f

This search takes the purchase events and pipes it into the chart command. The dc() or distinct_count() function is used to count the number of unique visitors (characterized by the clientip field). This number is then charted over each hour of the day and broken out based on the category_id of the purchase. Also, because these are numeric values, the search uses the usenull=f argument to exclude fields that don't have a value.

This produces the following table:

Searchref chart ex4.1.png


Click the Visualizations tab to format the report as a line chart:

Searchref chart ex4.2.png

Each line represents a different type of product that is sold at the Buttercup Games online store. The height of each line shows the number of different people who bought the product during that hour. In general, it looks like the most popular items at the online shop were Strategy games.

You can format the report as a stacked column chart, which will show you the total purchases at each hour of day:

Searchref chart 4.3.png

11: Chart the number of earthquakes and the magnitude of each earthquake

This example uses recent earthquake data downloaded from the USGS Earthquakes website. The data is a comma separated ASCII text file that contains magnitude (mag), coordinates (latitude, longitude), region (place), etc., for each earthquake recorded.

You can download a current CSV file from the USGS Earthquake Feeds and add it as an input to Splunk.

Create a chart that shows the number of earthquakes and the magnitude of each one that occurred in and around California.

source=usgs place=*California* | chart count over mag by place useother=f

This search counts the number of earthquakes that occurred in the California regions. The count is then broken down for each place based on the magnitude of the quake. Because the place value is non-numeric, the search uses the useother=f argument to exclude events that don't match.

This produces the following table:

Searchref chart usgsex1.1.png


Click on the Visualizations tab to view the report as a chart:


Searchref chart usgsex1.2.png

See also

timechart, bin, sichart

Answers

Have questions? Visit Splunk Answers and see what questions and answers the Splunk community has using the chart command.

This documentation applies to the following versions of Splunk: 6.0 , 6.0.1 , 6.0.2 , 6.0.3 , 6.0.4 , 6.0.5 , 6.0.6 , 6.0.7 , 6.0.8 , 6.0.9 , 6.0.10 , 6.1 , 6.1.1 , 6.1.2 , 6.1.3 , 6.1.4 , 6.1.5 , 6.1.6 , 6.1.7 , 6.1.8 , 6.1.9 , 6.2.0 , 6.2.1 , 6.2.2 , 6.2.3 , 6.2.4 , 6.2.5 View the Article History for its revisions.


Comments

Thanks for your comment, we have added that information.

Lstewart splunk, Splunker
August 10, 2015

Looks like limit option filtes top values just like the same option for timechart: "By default, setting limit=N would filter the top N values based on the sum of each series." But it is not mentioned here. Please, add it, if it's true.

0range
January 28, 2015

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