Grok filter plugin

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  • Plugin version: v4.4.3
  • Released on: 2022-10-28
  • Changelog

For other versions, see the Versioned plugin docs.

Getting help

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For questions about the plugin, open a topic in the Discuss forums. For bugs or feature requests, open an issue in Github. For the list of Elastic supported plugins, please consult the Elastic Support Matrix.

Description

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Parse arbitrary text and structure it.

Grok is a great way to parse unstructured log data into something structured and queryable.

This tool is perfect for syslog logs, apache and other webserver logs, mysql logs, and in general, any log format that is generally written for humans and not computer consumption.

Logstash ships with about 120 patterns by default. You can find them here: https://github.com/logstash-plugins/logstash-patterns-core/tree/master/patterns. You can add your own trivially. (See the patterns_dir setting)

If you need help building patterns to match your logs, you will find the http://grokdebug.herokuapp.com and http://grokconstructor.appspot.com/ applications quite useful!

Grok or Dissect? Or both?

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The dissect filter plugin is another way to extract unstructured event data into fields using delimiters.

Dissect differs from Grok in that it does not use regular expressions and is faster. Dissect works well when data is reliably repeated. Grok is a better choice when the structure of your text varies from line to line.

You can use both Dissect and Grok for a hybrid use case when a section of the line is reliably repeated, but the entire line is not. The Dissect filter can deconstruct the section of the line that is repeated. The Grok filter can process the remaining field values with more regex predictability.

Grok Basics

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Grok works by combining text patterns into something that matches your logs.

The syntax for a grok pattern is %{SYNTAX:SEMANTIC}

The SYNTAX is the name of the pattern that will match your text. For example, 3.44 will be matched by the NUMBER pattern and 55.3.244.1 will be matched by the IP pattern. The syntax is how you match.

The SEMANTIC is the identifier you give to the piece of text being matched. For example, 3.44 could be the duration of an event, so you could call it simply duration. Further, a string 55.3.244.1 might identify the client making a request.

For the above example, your grok filter would look something like this:

%{NUMBER:duration} %{IP:client}

Optionally you can add a data type conversion to your grok pattern. By default all semantics are saved as strings. If you wish to convert a semantic’s data type, for example change a string to an integer then suffix it with the target data type. For example %{NUMBER:num:int} which converts the num semantic from a string to an integer. Currently the only supported conversions are int and float.

Examples:With that idea of a syntax and semantic, we can pull out useful fields from a sample log like this fictional http request log:

    55.3.244.1 GET /index.html 15824 0.043

The pattern for this could be:

    %{IP:client} %{WORD:method} %{URIPATHPARAM:request} %{NUMBER:bytes} %{NUMBER:duration}

A more realistic example, let’s read these logs from a file:

    input {
      file {
        path => "/var/log/http.log"
      }
    }
    filter {
      grok {
        match => { "message" => "%{IP:client} %{WORD:method} %{URIPATHPARAM:request} %{NUMBER:bytes} %{NUMBER:duration}" }
      }
    }

After the grok filter, the event will have a few extra fields in it:

  • client: 55.3.244.1
  • method: GET
  • request: /index.html
  • bytes: 15824
  • duration: 0.043

Regular Expressions

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Grok sits on top of regular expressions, so any regular expressions are valid in grok as well. The regular expression library is Oniguruma, and you can see the full supported regexp syntax on the Oniguruma site.

Custom Patterns

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Sometimes logstash doesn’t have a pattern you need. For this, you have a few options.

First, you can use the Oniguruma syntax for named capture which will let you match a piece of text and save it as a field:

    (?<field_name>the pattern here)

For example, postfix logs have a queue id that is an 10 or 11-character hexadecimal value. I can capture that easily like this:

    (?<queue_id>[0-9A-F]{10,11})

Alternately, you can create a custom patterns file.

  • Create a directory called patterns with a file in it called extra (the file name doesn’t matter, but name it meaningfully for yourself)
  • In that file, write the pattern you need as the pattern name, a space, then the regexp for that pattern.

For example, doing the postfix queue id example as above:

    # contents of ./patterns/postfix:
    POSTFIX_QUEUEID [0-9A-F]{10,11}

Then use the patterns_dir setting in this plugin to tell logstash where your custom patterns directory is. Here’s a full example with a sample log:

    Jan  1 06:25:43 mailserver14 postfix/cleanup[21403]: BEF25A72965: message-id=<[email protected]>
    filter {
      grok {
        patterns_dir => ["./patterns"]
        match => { "message" => "%{SYSLOGBASE} %{POSTFIX_QUEUEID:queue_id}: %{GREEDYDATA:syslog_message}" }
      }
    }

The above will match and result in the following fields:

  • timestamp: Jan 1 06:25:43
  • logsource: mailserver14
  • program: postfix/cleanup
  • pid: 21403
  • queue_id: BEF25A72965
  • syslog_message: message-id=<[email protected]>

The timestamp, logsource, program, and pid fields come from the SYSLOGBASE pattern which itself is defined by other patterns.

Another option is to define patterns inline in the filter using pattern_definitions. This is mostly for convenience and allows user to define a pattern which can be used just in that filter. This newly defined patterns in pattern_definitions will not be available outside of that particular grok filter.

Migrating to Elastic Common Schema (ECS)

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To ease migration to the Elastic Common Schema (ECS), the filter plugin offers a new set of ECS-compliant patterns in addition to the existing patterns. The new ECS pattern definitions capture event field names that are compliant with the schema.

The ECS pattern set has all of the pattern definitions from the legacy set, and is a drop-in replacement. Use the ecs_compatibility setting to switch modes.

New features and enhancements will be added to the ECS-compliant files. The legacy patterns may still receive bug fixes which are backwards compatible.

Grok Filter Configuration Options

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This plugin supports the following configuration options plus the Common options described later.

Also see Common options for a list of options supported by all filter plugins.

 

break_on_match

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  • Value type is boolean
  • Default value is true

Break on first match. The first successful match by grok will result in the filter being finished. If you want grok to try all patterns (maybe you are parsing different things), then set this to false.

ecs_compatibility

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  • Value type is string
  • Supported values are:

    • disabled: the plugin will load legacy (built-in) pattern definitions
    • v1,v8: all patterns provided by the plugin will use ECS compliant captures
  • Default value depends on which version of Logstash is running:

    • When Logstash provides a pipeline.ecs_compatibility setting, its value is used as the default
    • Otherwise, the default value is disabled.

Controls this plugin’s compatibility with the Elastic Common Schema (ECS). The value of this setting affects extracted event field names when a composite pattern (such as HTTPD_COMMONLOG) is matched.

keep_empty_captures

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  • Value type is boolean
  • Default value is false

If true, keep empty captures as event fields.

match

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  • Value type is hash
  • Default value is {}

A hash that defines the mapping of where to look, and with which patterns.

For example, the following will match an existing value in the message field for the given pattern, and if a match is found will add the field duration to the event with the captured value:

    filter {
      grok {
        match => {
          "message" => "Duration: %{NUMBER:duration}"
        }
      }
    }

If you need to match multiple patterns against a single field, the value can be an array of patterns:

    filter {
      grok {
        match => {
          "message" => [
            "Duration: %{NUMBER:duration}",
            "Speed: %{NUMBER:speed}"
          ]
        }
      }
    }

To perform matches on multiple fields just use multiple entries in the match hash:

    filter {
      grok {
        match => {
          "speed" => "Speed: %{NUMBER:speed}"
          "duration" => "Duration: %{NUMBER:duration}"
        }
      }
    }

However, if one pattern depends on a field created by a previous pattern, separate these into two separate grok filters:

    filter {
      grok {
        match => {
          "message" => "Hi, the rest of the message is: %{GREEDYDATA:rest}"
        }
      }
      grok {
        match => {
          "rest" => "a number %{NUMBER:number}, and a word %{WORD:word}"
        }
      }
    }

named_captures_only

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  • Value type is boolean
  • Default value is true

If true, only store named captures from grok.

overwrite

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  • Value type is array
  • Default value is []

The fields to overwrite.

This allows you to overwrite a value in a field that already exists.

For example, if you have a syslog line in the message field, you can overwrite the message field with part of the match like so:

    filter {
      grok {
        match => { "message" => "%{SYSLOGBASE} %{DATA:message}" }
        overwrite => [ "message" ]
      }
    }

In this case, a line like May 29 16:37:11 sadness logger: hello world will be parsed and hello world will overwrite the original message.

If you are using a field reference in overwrite, you must use the field reference in the pattern. Example:

    filter {
      grok {
        match => { "somefield" => "%{NUMBER} %{GREEDYDATA:[nested][field][test]}" }
        overwrite => [ "[nested][field][test]" ]
      }
    }

pattern_definitions

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  • Value type is hash
  • Default value is {}

A hash of pattern-name and pattern tuples defining custom patterns to be used by the current filter. Patterns matching existing names will override the pre-existing definition. Think of this as inline patterns available just for this definition of grok

patterns_dir

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  • Value type is array
  • Default value is []

Logstash ships by default with a bunch of patterns, so you don’t necessarily need to define this yourself unless you are adding additional patterns. You can point to multiple pattern directories using this setting. Note that Grok will read all files in the directory matching the patterns_files_glob and assume it’s a pattern file (including any tilde backup files).

    patterns_dir => ["/opt/logstash/patterns", "/opt/logstash/extra_patterns"]

Pattern files are plain text with format:

    NAME PATTERN

For example:

    NUMBER \d+

The patterns are loaded when the pipeline is created.

patterns_files_glob

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  • Value type is string
  • Default value is "*"

Glob pattern, used to select the pattern files in the directories specified by patterns_dir

tag_on_failure

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  • Value type is array
  • Default value is ["_grokparsefailure"]

Append values to the tags field when there has been no successful match

tag_on_timeout

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  • Value type is string
  • Default value is "_groktimeout"

Tag to apply if a grok regexp times out.

target

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  • Value type is string
  • There is no default value for this setting

Define target namespace for placing matches.

timeout_millis

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  • Value type is number
  • Default value is 30000

Attempt to terminate regexps after this amount of time. This applies per pattern if multiple patterns are applied This will never timeout early, but may take a little longer to timeout. Actual timeout is approximate based on a 250ms quantization. Set to 0 to disable timeouts

timeout_scope

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  • Value type is string
  • Default value is "pattern"
  • Supported values are "pattern" and "event"

When multiple patterns are provided to match, the timeout has historically applied to each pattern, incurring overhead for each and every pattern that is attempted; when the grok filter is configured with timeout_scope => event, the plugin instead enforces a single timeout across all attempted matches on the event, so it can achieve similar safeguard against runaway matchers with significantly less overhead.

It’s usually better to scope the timeout for the whole event.

Common options

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These configuration options are supported by all filter plugins:

add_field

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  • Value type is hash
  • Default value is {}

If this filter is successful, add any arbitrary fields to this event. Field names can be dynamic and include parts of the event using the %{field}.

Example:

    filter {
      grok {
        add_field => { "foo_%{somefield}" => "Hello world, from %{host}" }
      }
    }
    # You can also add multiple fields at once:
    filter {
      grok {
        add_field => {
          "foo_%{somefield}" => "Hello world, from %{host}"
          "new_field" => "new_static_value"
        }
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would add field foo_hello if it is present, with the value above and the %{host} piece replaced with that value from the event. The second example would also add a hardcoded field.

add_tag

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  • Value type is array
  • Default value is []

If this filter is successful, add arbitrary tags to the event. Tags can be dynamic and include parts of the event using the %{field} syntax.

Example:

    filter {
      grok {
        add_tag => [ "foo_%{somefield}" ]
      }
    }
    # You can also add multiple tags at once:
    filter {
      grok {
        add_tag => [ "foo_%{somefield}", "taggedy_tag"]
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would add a tag foo_hello (and the second example would of course add a taggedy_tag tag).

enable_metric

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  • Value type is boolean
  • Default value is true

Disable or enable metric logging for this specific plugin instance. By default we record all the metrics we can, but you can disable metrics collection for a specific plugin.

  • Value type is string
  • There is no default value for this setting.

Add a unique ID to the plugin configuration. If no ID is specified, Logstash will generate one. It is strongly recommended to set this ID in your configuration. This is particularly useful when you have two or more plugins of the same type, for example, if you have 2 grok filters. Adding a named ID in this case will help in monitoring Logstash when using the monitoring APIs.

    filter {
      grok {
        id => "ABC"
      }
    }

Variable substitution in the id field only supports environment variables and does not support the use of values from the secret store.

periodic_flush

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  • Value type is boolean
  • Default value is false

Call the filter flush method at regular interval. Optional.

remove_field

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  • Value type is array
  • Default value is []

If this filter is successful, remove arbitrary fields from this event. Fields names can be dynamic and include parts of the event using the %{field} Example:

    filter {
      grok {
        remove_field => [ "foo_%{somefield}" ]
      }
    }
    # You can also remove multiple fields at once:
    filter {
      grok {
        remove_field => [ "foo_%{somefield}", "my_extraneous_field" ]
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would remove the field with name foo_hello if it is present. The second example would remove an additional, non-dynamic field.

remove_tag

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  • Value type is array
  • Default value is []

If this filter is successful, remove arbitrary tags from the event. Tags can be dynamic and include parts of the event using the %{field} syntax.

Example:

    filter {
      grok {
        remove_tag => [ "foo_%{somefield}" ]
      }
    }
    # You can also remove multiple tags at once:
    filter {
      grok {
        remove_tag => [ "foo_%{somefield}", "sad_unwanted_tag"]
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would remove the tag foo_hello if it is present. The second example would remove a sad, unwanted tag as well.