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* Source commands * Missing word * Processing commands * Apply suggestions from code review Co-authored-by: Alexander Spies <alexander.spies@elastic.co> * Review feedback * Add sort detail for mv * More review feedback --------- Co-authored-by: Alexander Spies <alexander.spies@elastic.co>
67 lines
1.6 KiB
Text
67 lines
1.6 KiB
Text
[discrete]
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[[esql-grok]]
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=== `GROK`
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**Syntax**
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[source,esql]
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----
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GROK input "pattern"
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----
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*Parameters*
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`input`::
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The column that contains the string you want to structure. If the column has
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multiple values, `GROK` will process each value.
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`pattern`::
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A grok pattern.
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*Description*
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`GROK` enables you to <<esql-process-data-with-dissect-and-grok,extract
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structured data out of a string>>. `GROK` matches the string against patterns,
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based on regular expressions, and extracts the specified patterns as columns.
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Refer to <<esql-process-data-with-grok>> for the syntax of grok patterns.
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*Examples*
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// tag::examples[]
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The following example parses a string that contains a timestamp, an IP address,
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an email address, and a number:
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[source.merge.styled,esql]
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----
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include::{esql-specs}/docs.csv-spec[tag=basicGrok]
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----
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[%header.monospaced.styled,format=dsv,separator=|]
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include::{esql-specs}/docs.csv-spec[tag=basicGrok-result]
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|===
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By default, `GROK` outputs keyword string columns. `int` and `float` types can
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be converted by appending `:type` to the semantics in the pattern. For example
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`{NUMBER:num:int}`:
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[source.merge.styled,esql]
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----
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include::{esql-specs}/docs.csv-spec[tag=grokWithConversionSuffix]
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----
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[%header.monospaced.styled,format=dsv,separator=|]
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include::{esql-specs}/docs.csv-spec[tag=grokWithConversionSuffix-result]
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|===
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For other type conversions, use <<esql-type-conversion-functions>>:
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[source.merge.styled,esql]
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----
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include::{esql-specs}/docs.csv-spec[tag=grokWithToDatetime]
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----
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[%header.monospaced.styled,format=dsv,separator=|]
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|===
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include::{esql-specs}/docs.csv-spec[tag=grokWithToDatetime-result]
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|===
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// end::examples[]
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