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chart

Description

The chart command is a transforming command that returns your results in a table format. The results can then 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 when you use the chart command. See Statistical and charting functions.

Syntax

chart [<chart-options>] [agg=<stats-agg-term>]
( <stats-agg-term> | <sparkline-agg-term> | "("<eval-expression>")" )...
[ BY <row-split> <column-split> ] | [ OVER <row-split> ] [BY <column-split>] ]

Required arguments

You must include one of the following arguments when you use the chart command.

stats-agg-term
Syntax: <stats-func> ( <evaled-field> | <wc-field> ) [AS <wc-field>]
Description: A statistical aggregation function. See Stats function options. The function can be applied to an eval expression, or to a field or set of fields. Use the AS clause to place the result into a new field with a name that you specify. You can use wild card characters in field names.
sparkline-agg-term
Syntax: <sparkline-agg> [AS <wc-field>]
Description: A sparkline aggregation function. Use the AS clause to place the result into a new field with a name that you specify. You can use wild card characters in field names. See Sparkline options.
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. See Usage.

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 might not produce valid results if the values are not numerical. If both operands are strings, they can be concatenated. When concatenating values with a period, the search treats both values as strings regardless of their actual type.

Optional arguments

agg
Syntax: 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.
chart-options
Syntax: cont | format | limit | sep
Description: Options that you can specify to refine the result. See the Chart options section in this topic.
Default:
column-split
Syntax: <field> [<tc-options>]... [<where-clause>]
Description: Specifies a field to use as the columns in the result table. By default, when the result are visualized, the columns become the data series in the chart. If the field is numerical, default discretization is applied as defined with the tc-options argument. See the tc options and the where clause sections in this topic.
Default: The number of columns included is limited to 10 by default. You can change the number of columns by including a <where-clause>.
Note: When a column-split 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 by-clause, where each row represents a single unique combination of values of the group-by fields. For additional information see the Usage section in this topic.
row-split
Syntax: <field> [<bin-options>]...
Description: The field that you specify becomes the first column in the results table. The field values become the row labels in the results table. In a chart, the field name is used to label the X-axis. The field values become the X-axis values. See the Bin options section in this topic.
Default: None.

Chart options

cont
Syntax: cont=<bool>
Description: Specifies if the bins are continuous. If cont=false, replots the x-axis so that a noncontinuous sequence of x-value bins show up adjacently in the output. If cont=true, bins that have no values will display with a count of 0 or null values.
Default: true
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 ($VAL$).
limit
Syntax: limit=<int>
Description: Only valid when a column-split is specified. Use the limit option to specify the number of results that should appear in the output. When you set limit=N the top N values are retained, based on the sum of each series. If limit=0, all results are returned.
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>$VAL$.

Stats function options

stats-func
Syntax: The syntax depends on the function you use. Refer to the table below.
Description: Statistical and charting functions that you can use with the chart command. Each time you invoke the chart command, you can use one or more functions. However, you can only use one BY clause. See Usage.
The following table lists the supported functions by type of function. Use the links in the table to see descriptions and examples for each function. For an overview about using functions with commands, see Statistical and charting functions.
Type of function Supported functions and syntax
Aggregate functions avg()

count()
distinct_count()
estdc()
estdc_error()

exactperc<int>()

max()
median()
min()
mode()

perc<int>()

range()
stdev()
stdevp()

sum()

sumsq()
upperperc<int>()
var()
varp()

Event order functions earliest()
first()
last()
latest()
Multivalue stats and chart functions list(X)
values(X)

Sparkline 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.

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 aggregate function is permitted. You can use wild card characters in field names.
span-length
See the Span options section in this topic.
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 bin.

For more information see Add sparklines to your search results in the Search Manual.

Bin options

Syntax: bins | span | <start-end> | aligntime
Description: Discretization options.
Default: bins=300
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. See the Span options section in this topic.
<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.
aligntime
Syntax: aligntime=(earliest | latest | <time-specifier>)
Description: Align the bin times to something other than base UNIX time (epoch 0). The aligntime option is valid only when doing a time-based discretization. Ignored if span is in days, months, or years.

Span options

<log-span>
Syntax: [<num>]log[<num>]
Description: Sets to a logarithm-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>[<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.
Time scale Syntax Description
<sec> s | sec | secs | second | seconds Time scale in seconds.
<min> m | min | mins | minute | minutes Time scale in minutes.
<hr> h | hr | hrs | hour | hours Time scale in hours.
<day> d | day | days Time scale in days.
<month> mon | month | months Time scale in months.
<subseconds> us | ms | cs | ds Time scale in microseconds (us), milliseconds (ms), centiseconds (cs), or deciseconds (ds)

tc options

The tc-options is part of the <column-split> argument.

tc-options
Syntax: <bin-options> | usenull=<bool> | useother=<bool> | nullstr=<string> | otherstr=<string>
Description: Options for controlling the behavior of splitting by a field.
bin-options
See the Bin options section in this topic.
nullstr
Syntax: nullstr=<string>
Description: If usenull is true, this series is labeled by the value of the nullstr option, and defaults to NULL.
otherstr
String: otherstr=<string>
Description: If useother is true, this series is labeled by the value of the otherstr option, and defaults to OTHER.
usenull
Syntax: usenull=<bool>
Description: Controls whether or not a series is created for events that do not contain the split-by field.
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>.

where clause

The <where-clause> is part of the <column-split> argument.

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.
single-agg
Syntax: count | <stats-func>(<field>)
Description: A single aggregation applied to a single field, including an evaluated field. No wildcards are allowed. The field must be specified, except when using the count aggregate function, which applies to events as a whole.
<stats-func>
See the Statistical functions section in this topic.
<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 chart command with an eval expression. Unless you specify a split-by clause, the eval expression must be renamed.

Functions and memory usage

Some functions are inherently more expensive, from a memory standpoint, than other functions. For example, the distinct_count function requires far more memory than the count function. The values and list functions also can consume a lot of memory.

If you are using the distinct_count function without a split-by field or with a low-cardinality split-by by field, consider replacing the distinct_count function with the the estdc function (estimated distinct count). The estdc function might result in significantly lower memory usage and run times.

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.

Using row-split and column-split fields

When a column-split field is included, the output is a table where each column represents a distinct value of the column-split 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.

With the chart and timechart commands, you cannot specify the same field in a function and as the row-split field.

For example, you cannot run this search. The field A is specified in the sum function and the row-split argument.

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

You must specify a different field as in the row-split argument.

Alternatively, 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

7. Align the chart time bins to local time

Align the time bins to 5am (local time). Set the span to 12h. The bins will represent 5am - 5pm, then 5pm - 5am (the next day), and so on.

...| chart _time span=12h aligntime=@d+5h

Extended Examples

7. Specify <row-split> and <column-split> values with the chart command

This example uses events that list the numeric sales for each product and quarter, for example:

products quarter sales
ProductA QTR1 1200
ProductB QTR1 1400
ProductC QTR1 1650
ProductA QTR2 1425
ProductB QTR2 1175
ProductC QTR2 1550
ProductA QTR3 1300
ProductB QTR3 1250
ProductC QTR3 1375
ProductA QTR4 1550
ProductB QTR4 1700
ProductC QTR4 1625

To summarize the data by product for each quarter, run this search:

source="addtotalsData.csv" | chart sum(sales) BY products quarter

In this example, there are two fields specified in the BY clause with the chart command.

  • The products field is referred to as the <row-split> field. In the chart, this field forms the X-axis.
  • The quarter field is referred to as the <column-split> field. In the chart, this field forms the data series.

The results appear on the Statistics tab and look something like this:

products QTR1 QTR2 QTR3 QTR4
ProductA 1200 1425 1300 1550
ProductB 1400 1175 1250 1700
ProductC 1650 1550 1375 1625

Click on the Visualization tab to see the results as a chart.

See the addtotals command for an example that adds a total column for each product.

8. Chart the number of different page requests for each Web server

This example uses the sample data from the Search Tutorial but should work with any format of Apache web access log. To try this example on your own Splunk instance, you must download the sample data and follow the instructions to get the tutorial data into Splunk. Use the time range All time when you run the search.

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, using the AS keyword, the field that represents these results is renamed GET.

The second clause does the same for POST events. The counts of both types of events are then separated by the web server, using the BY clause with the host field.

The results appear on the Statistics tab and look something like this:

host GET POST
www1 8431 5197
www2 8097 4815
www3 8338 4654

Click the Visualization tab. If necessary, format the results as a column chart. This chart displays the total count of events for each event type, GET or POST, based on the host value.

This image shows a column chart. There are 2 series in the chart, GET and POST. The host values are on the X axis.

9. Chart the number of transactions by duration

This example uses the sample data from the Search Tutorial. To try this example on your own Splunk instance, you must download the sample data and follow the instructions to get the tutorial data into Splunk. Use the time range All time when you run the search.

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).

The results appear on the Statistics tab and look something like this:

duration count
0 970
1-2 593
2-4 208
4-8 173
8-16 26
64-128 3
128-256 3
256-512 12
512-1024 2


Click the Visualization tab. If necessary, format the results as a column chart.

This image shows the duration along the X-axis and count of transactions along the Y-axis. The first column shows zero duration and 970 transactions.


In this data set, most transactions take between 0 and 2 seconds to complete.

10. Chart the average number of events in a transaction, based on transaction duration

This example uses the sample data from the Search Tutorial. To try this example on your own Splunk instance, you must download the sample data and follow the instructions to get the tutorial data into Splunk. Use the time range All time when you run the search.

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

The transaction command adds two fields to the results duration and eventcount. The eventcount field tracks the number of events in a single transaction.

In this search, the transactions are piped into the chart command. 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 using logarithm with a base of 2.

Use the field format option to enable number formatting.

This images shows the results on the Statistics tab. There are 2 columns in the results: duration and average of event count.


Click the Visualization tab and change the display to a pie chart.

This images shows the results on the Visualization tab.  The chart type has been changed to a pie chart.


Each wedge of the pie chart represents a duration for the event transactions. You can hover over a wedge to see the average values.

11. Chart customer purchases

This example uses the sample data from the Search Tutorial. To try this example on your own Splunk instance, you must download the sample data and follow the instructions to get the tutorial data into Splunk. Use the time range Yesterday when you run the search.

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.

The results appear on the Statistics tab and look something like this:

date_hour ACCESSORIES ARCADE SHOOTER SIMULATION SPORTS STRATEGY TEE
0 2 6 0 4 0 4 4
1 4 7 2 3 0 10 5
2 2 2 2 1 1 2 0
3 3 5 3 5 0 7 1
4 3 4 0 0 1 4 0
5 3 0 3 0 1 6 1


Click the Visualization tab. If necessary, format the results as a line chart:

This image shows a line chart. The X-axis displays the date_hour values.

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 Arcade games.

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

  1. Change the chart type to a Column Chart.
  2. Use the Format menu, and on the General tab select stacked.

This image shows a stacked chart from hours 0000 (midnight) to 1800 (6:00 pm).

12. 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.

Create a chart that list the number of earthquakes, and the magnitude of each earthquake that occurred in and around Alaska. Run the search using the time range All time.

source=all_month.csv place=*alaska* mag>=3.5 | chart count BY mag place useother=f | rename mag AS Magnitude

This search counts the number of earthquakes that occurred in the Alaska 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.

The results appear on the Statistics tab and look something like this:

Magnitude 145km ENE of Chirikof Island, Alaska 225km SE of Kodiak, Alaska 250km SE of Kodiak, Alaska 252km SE of Kodiak, Alaska 254km SE of Kodiak, Alaska 255km SE of Kodiak, Alaska 259km SE of Kodiak, Alaska 264km SE of Kodiak, Alaska 265km SE of Kodiak, Alaska Gulf of Alaska
3.5 1 1 0 1 0 1 0 0 2 2
3.6 0 0 1 0 0 0 0 1 0 1
3.7 0 0 0 0 1 0 0 0 0 2
3.8 0 1 0 0 0 0 1 1 0 3
3.9 0 0 1 0 1 0 0 0 0 0
4 0 0 0 0 1 1 0 0 0 1
4.1 0 0 0 0 0 0 0 0 0 1
4.2 0 0 0 1 0 0 0 0 0 1
4.3 0 0 0 0 0 0 0 0 0 1
4.4 0 0 0 0 0 0 1 0 0 1
4.6 1 0 0 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0 1


Click on the Visualization tab to view the results on a chart. This chart shows the number of earthquakes by magnitude.

This image shows a column chart. The X-axis lists the magnitudes ranging from 3.5 to 5. The Y-axis displays the count. Each region where earthquakes occurred is a data series.

See also

Commands
timechart
bin
sichart
Blogs
Search commands > stats, chart, and timechart

Answers

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

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This documentation applies to the following versions of Splunk® Enterprise: 7.2.0, 7.2.1


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