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transpose

Description

Returns the specified number of rows (search results) as columns (list of field values), such that each search row becomes a column.

Syntax

The required syntax is in bold.

transpose
[int]
[column_name=<string>]
[header_field=<field>]
[include_empty=<bool>]

Required arguments

None.

Optional arguments

column_name
Syntax: column_name=<string>
Description: The name of the first column that you want to use for the transposed rows. This column contains the names of the fields.
Default: column
header_field
Syntax: header_field=<field>
Description: The field in your results to use for the names of the columns (other than the first column) in the transposed data.
Default: row 1, row 2, row 3, and so on.
include_empty
Syntax: include_empty=<bool>
Description: Specify whether to include (true) or not include (false) fields that contain empty values.
Default: true
int
Syntax: <int>
Description: Limit the number of rows to transpose. To transpose all rows, specify | transpose 0, which indicates that the number of rows to transpose is unlimited.
Default: 5

Usage

When you use the transpose command the field names used in the output are based on the arguments that you use with the command. By default the field names are: column, row1, row2, and so forth.

Examples

1. Transpose the results of a chart command

Use the default settings for the transpose command to transpose the results of a chart command.

Suppose you run a search like this:

sourcetype=access_* status=200 | chart count BY host

The search produces the following search results:

host count
www1 11835
www2 11186
www3 11261

When you add the transpose command to the end of the search, the results look something like this:

column row 1 row 2 row 3
host www1 www2 www3
count 11835 11186 11261

2. Count the number of events by sourcetype and transpose the results to display the 3 highest counts

Count the number of events by sourcetype and display the sourcetypes with the highest count first.

index=_internal | stats count by sourcetype | sort -count

An image that shows 2 columns. The first column lists the source types. The second column is a count of the number of events for each source type.

Use the transpose command to convert the rows to columns and show the source types with the 3 highest counts.

index=_internal | stats count by sourcetype | sort -count | transpose 3

An image that shows 4 columns. The first column are labels that for the information in the rows. The labels are sourcetype and count. The other 3 columns list the top 3 source types and the count, the number of events, for each source type.

3. Transpose a set of data into a series to produce a chart

This example uses the sample dataset from the Search Tutorial.
  • Download the data set from Add data tutorial and follow the instructions to get the tutorial data into your Splunk deployment.

Search all successful events and count the number of views, the number of times items were added to the cart, and the number of purchases.

sourcetype=access_* status=200 | stats count AS views count(eval(action="addtocart")) AS addtocart count(eval(action="purchase")) AS purchases

This search produces a single row of data. This screen image shows one row of data with three columns. The first column is "views" with a count of 34282. The second column is "add to cart" with a count of 5292. The third column is "purchases" with a count of 5224.

The value for count AS views is the total number of the events that match the criteria sourcetype=access_* status=200, or the total count for all actions. The values for addtocart and purchases show the number of events for those specific actions.

When you switch to the Visualization tab, the data displays a chart with the "34282 views" as the X axis label and two columns, one for "addtocart "and one for "purchases". Because the information about the views is placed on the X axis, this chart is confusing.

This screen image shows a column chart. There are two columns "add to cart" and "purchases". The X axis label is "34282 views". with a count of 34282. The second column is "add to cart" with a count of 5292. The third column is "purchases" with a count of 5224.

If you change to a pie chart, you see only the "views".

This screen image shows a pie chart with only the "views" information included in the chart.

Use the transpose command to convert the columns of the single row into multiple rows.

sourcetype=access_* status=200 | stats count AS views count(eval(action="addtocart")) AS addtocart count(eval(action="purchase")) AS purchases | transpose

This screen image shows three rows, one for "views", one for "add to cart" and one for "purchases". Each row displays the corresponding count.

Now these rows can be displayed in a column or pie chart where you can compare the values.

This screen image shows a pie chart with a slice for each of the rows of data. There is one for "views", one for "add to cart" and one for "purchases". Views has the biggest slice.

In this particular example, using a pie chart is misleading. The views is a total count of all the actions, not just the addtocart and purchases actions. Using a pie chart implies that views is an action like addtocart and purchases. The pie chart implies that the value for views is 1 part of the total, when in fact views is the total.

There are a few ways to fix this issue:

  • Use a column chart
  • You can remove the count AS views criteria from your search
  • You can add the table command before the transpose command in the search, for example:

sourcetype=access_* status=200 | stats count AS views count(eval(action="addtocart")) AS addtocart count(eval(action="purchase")) AS purchases | table addtocart purchases | transpose

See also

Commands
fields
stats
untable
xyseries
Last modified on 05 November, 2021
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This documentation applies to the following versions of Splunk® Enterprise: 6.5.7, 7.0.1, 7.0.2, 7.0.3, 7.0.4, 7.0.5, 7.0.6, 7.0.7, 7.0.8, 7.0.9, 7.0.10, 7.0.11, 7.0.13, 7.1.0, 7.1.1, 7.1.2, 7.1.3, 7.1.4, 7.1.5, 7.1.6, 7.1.7, 7.1.8, 7.1.9, 7.1.10, 7.2.0, 7.2.1, 7.2.2, 7.2.3, 7.2.4, 7.2.5, 7.2.6, 7.2.7, 7.2.8, 7.2.9, 7.2.10, 7.3.0, 7.3.1, 7.3.2, 7.0.0, 7.3.3, 7.3.4, 7.3.8, 7.3.9, 8.0.0, 8.0.1, 8.0.2, 8.0.3, 8.0.4, 8.0.5, 8.0.6, 8.0.7, 8.0.8, 8.0.9, 8.0.10, 8.1.0, 8.1.1, 8.1.2, 8.1.3, 8.1.4, 8.1.5, 8.1.6, 8.1.7, 8.1.8, 8.1.9, 8.1.10, 8.2.0, 8.2.1, 8.2.2, 8.2.3, 8.2.4, 8.2.5, 8.2.6, 8.2.7, 9.0.0, 7.3.5, 7.3.6, 7.3.7

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