grok
editgrok
editParse 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 Basics
editGrok 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
editGrok 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
editSometimes 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 calledextra
(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]> [source,ruby] 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.
Synopsis
editThis plugin supports the following configuration options:
Required configuration options:
grok { }
Available configuration options:
Setting | Input type | Required | Default value |
---|---|---|---|
No |
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No |
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No |
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No |
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No |
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No |
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No |
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No |
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No |
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No |
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No |
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No |
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No |
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Details
edit
add_field
edit- 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}" } } } [source,ruby] # 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
edit- 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}" ] } } [source,ruby] # 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_match
edit- 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_captures
edit- Value type is boolean
-
Default value is
false
If true
, keep empty captures as event fields.
match
edit- 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_only
edit- Value type is boolean
-
Default value is
true
If true
, only store named captures from grok.
overwrite
edit- 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_dir
edit- 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 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+
patterns_files_glob
edit- Value type is string
-
Default value is
"*"
Glob pattern, used to select the pattern files in the directories specified by patterns_dir
periodic_flush
edit- Value type is boolean
-
Default value is
false
Call the filter flush method at regular interval. Optional.
remove_field
edit- 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}" ] } } [source,ruby] # 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
edit- 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}" ] } } [source,ruby] # 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.