[discrete]
[[esql-grok]]
=== `GROK`

`GROK` enables you to <<esql-process-data-with-dissect-and-grok,extract
structured data out of a string>>.

**Syntax**

[source,esql]
----
GROK input "pattern"
----

*Parameters*

`input`::
The column that contains the string you want to structure. If the column has
multiple values, `GROK` will process each value.

`pattern`::
A grok pattern.

*Description*

`GROK` enables you to <<esql-process-data-with-dissect-and-grok,extract
structured data out of a string>>. `GROK` matches the string against patterns,
based on regular expressions, and extracts the specified patterns as columns.

Refer to <<esql-process-data-with-grok>> for the syntax of grok patterns.

*Examples*

// tag::examples[]
The following example parses a string that contains a timestamp, an IP address,
an email address, and a number:

[source.merge.styled,esql]
----
include::{esql-specs}/docs.csv-spec[tag=basicGrok]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/docs.csv-spec[tag=basicGrok-result]
|===

By default, `GROK` outputs keyword string columns. `int` and `float` types can
be converted by appending `:type` to the semantics in the pattern. For example
`{NUMBER:num:int}`:

[source.merge.styled,esql]
----
include::{esql-specs}/docs.csv-spec[tag=grokWithConversionSuffix]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/docs.csv-spec[tag=grokWithConversionSuffix-result]
|===

For other type conversions, use <<esql-type-conversion-functions>>:

[source.merge.styled,esql]
----
include::{esql-specs}/docs.csv-spec[tag=grokWithToDatetime]
----
[%header.monospaced.styled,format=dsv,separator=|]
|===
include::{esql-specs}/docs.csv-spec[tag=grokWithToDatetime-result]
|===
// end::examples[]
