Lear more about the Cortex Query Language view
stage that configures the display of the result set.
Syntax
view highlight fields = <field1>[,<field2>,...] values = <value1>[,<value2>,...]
view graph type = column|line|pie xaxis = <field1> yaxis = <field2> [<optional parameters>]
view column order = default|populated
Description
The view()
stage configures the display of the result set in the following ways:
highlight
: Highlights specified strings that Cortex XSIAM finds on specified fields. The highlight values that you provide are performed as a substring search, so only partial value can be highlighted in the final results table.graph type
: Creates a column, line, or pie chart based on the values found for the fields specified in thexaxis
andyaxis
parameters. In this mode,view
also offers a large number of parameters that allow you to control colors, decorations, and other behavior used for the final chart. You can also define a graphsubtype
, when setting thegraph type
to eithercolumn
orpie
.Note
If you use
graph type
, the fields specified forxaxis
andyaxis
must be collatable or the query will fail.column order
: Enables you to list the query results by popularity, where the most non-null returned fields are displayed first using the syntaxview column order = populated
. By default, ifcolumn order
is not defined (orview column order=default
), the original column order is used.Note
This option does not apply to Cortex Query Language (XQL) queries in widgets, Correlation Rules, public APIs, reports, and dashboards. If you include the
view column order
syntax in these types of queries, Cortex XSIAM disregards the stage from the query and completes the rest of the query.
Examples
Use the dedup stage collect unique combinations of event_type
and event_sub_type
values. Highlight the word "STREAM" when it appears in the result set.
dataset = xdr_data | fields event_type, event_sub_type | dedup event_type, event_sub_type by asc _time | view highlight fields = event_sub_type values = "STREAM"
Count the number of unique files accessed by each user, and show a column graph of the results. This query uses comp count_distinct to calculate the number of unique files per username.
dataset = xdr_data | fields actor_effective_username as username, action_file_path as file_path | filter file_path != null and username != null | comp count_distinct(file_path) as file_count by username | view graph type = column xaxis = username yaxis = file_count
Count the number of unique files accessed by each user, and display the results by popularity according to the most non-null values returned fields. This query uses comp count_distinct to calculate the number of unique files per username.
dataset = xdr_data | fields actor_effective_username as username, action_file_path as file_path | filter file_path != null and username != null | comp count_distinct(file_path) as file_count by username | view column order = populated