grokedit

Parse arbitrary text and structure it.

Grok is currently the best way in logstash to parse crappy 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 Basicsedit

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 Expressionsedit

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 Onigiruma site.

Custom Patternsedit

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=<20130101142543.5828399CCAF@mailserver14.example.com>
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=<20130101142543.5828399CCAF@mailserver14.example.com>

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

 

Synopsisedit

This plugin supports the following configuration options:

Required configuration options:

grok {
}

Available configuration options:

Detailsedit

 

add_fieldedit

  • 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_tagedit

  • 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).

break_on_matchedit

  • 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.

keep_empty_capturesedit

  • Value type is boolean
  • Default value is false

If true, keep empty captures as event fields.

matchedit

  • Value type is hash
  • Default value is {}

A hash of matches of field ⇒ value

For example:

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}" ] } }
}

named_captures_onlyedit

  • Value type is boolean
  • Default value is true

If true, only store named captures from grok.

overwriteedit

  • 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.

pattern (DEPRECATED)edit

  • DEPRECATED WARNING: This configuration item is deprecated and may not be available in future versions.
  • Value type is array
  • There is no default value for this setting.

Specify a pattern to parse with. This will match the message field.

If you want to match other fields than message, use the match setting. Multiple patterns is fine.

patterns_diredit

  • 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 and assume its 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+

periodic_flushedit

  • Value type is boolean
  • Default value is false

Call the filter flush method at regular interval. Optional.

remove_fieldedit

  • Value type is array
  • Default value is []

If this filter is successful, remove arbitrary fields from this event. 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_tagedit

  • 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.

singles (DEPRECATED)edit

  • DEPRECATED WARNING: This configuration item is deprecated and may not be available in future versions.
  • Value type is boolean
  • Default value is true

If true, make single-value fields simply that value, not an array containing that one value.

tag_on_failureedit

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

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

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