本篇文章为你整理了Grok filter plugin（）的详细内容，包含有 Grok filter plugin，希望能帮助你了解 Grok filter plugin。
Descriptionedit
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?edit
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 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:
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 Onigurumasite.
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.
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
patterns_dir = ["./patterns"]
match = { "message" = "%{SYSLOGBASE} %{POSTFIX_QUEUEID:queue_id}: %{GREEDYDATA:syslog_message}" }
}
The above will match and result in the following fields:
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)edit
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.
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.
When Logstash provides a pipeline.ecs_compatibility setting, its value is used as the default
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.
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}"
}
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]" ]
}
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
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.
Glob pattern, used to select the pattern files in the directories
specified by patterns_dir
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
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.
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}" }
}
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.
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}" ]
}
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).
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.
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.
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}" ]
}
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.
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}" ]
}
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.
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