This processor comes packaged with manyreusable patterns.

If you need help building patterns to match your logs, you will find theGrok Debugger tool quite useful! TheGrok Constructor is also a useful tool.

Here is an example of using the provided patterns to extract out and name structured fields from a string field in a document.

POST _ingest/pipeline/_simulate{  "pipeline": {    "description" : "...",    "processors": [      {        "grok": {          "field": "message",          "patterns": ["%{IP:client} %{WORD:method} %{URIPATHPARAM:request} %{NUMBER:bytes:int} %{NUMBER:duration:double}"]        }      }    ]  },  "docs":[    {      "_source": {        "message": "55.3.244.1 GET /index.html 15824 0.043"      }    }  ]}

This pipeline will insert these named captures as new fields within the document, like so:

{  "docs": [    {      "doc": {        "_index": "_index",        "_id": "_id",        "_version": "-3",        "_source" : {          "duration" : 0.043,          "request" : "/index.html",          "method" : "GET",          "bytes" : 15824,          "client" : "55.3.244.1",          "message" : "55.3.244.1 GET /index.html 15824 0.043"        },        "_ingest": {          "timestamp": "2016-11-08T19:43:03.850+0000"        }      }    }  ]}

The Grok processor comes pre-packaged with a base set of patterns. These patterns may not always have what you are looking for. Patterns have a very basic format. Each entry has a name and the pattern itself.

You can add your own patterns to a processor definition under thepattern_definitions option. Here is an example of a pipeline specifying custom pattern definitions:

{  "description" : "...",  "processors": [    {      "grok": {        "field": "message",        "patterns": ["my %{FAVORITE_DOG:dog} is colored %{RGB:color}"],        "pattern_definitions" : {          "FAVORITE_DOG" : "beagle",          "RGB" : "RED|GREEN|BLUE"        }      }    }  ]}

Sometimes one pattern is not enough to capture the potential structure of a field. Let’s assume we want to match all messages that contain your favorite pet breeds of either cats or dogs. One way to accomplish this is to provide two distinct patterns that can be matched, instead of one really complicated expression capturing the sameor behavior.

Here is an example of such a configuration executed against the simulate API:

POST _ingest/pipeline/_simulate{  "pipeline": {  "description" : "parse multiple patterns",  "processors": [    {      "grok": {        "field": "message",        "patterns": ["%{FAVORITE_DOG:pet}", "%{FAVORITE_CAT:pet}"],        "pattern_definitions" : {          "FAVORITE_DOG" : "beagle",          "FAVORITE_CAT" : "burmese"        }      }    }  ]},"docs":[  {    "_source": {      "message": "I love burmese cats!"    }  }  ]}

response:

{  "docs": [    {      "doc": {        "_index": "_index",        "_id": "_id",        "_version": "-3",        "_source": {          "message": "I love burmese cats!",          "pet": "burmese"        },        "_ingest": {          "timestamp": "2016-11-08T19:43:03.850+0000"        }      }    }  ]}

Both patterns will set the fieldpet with the appropriate match, but what if we want to trace which of our patterns matched and populated our fields? We can do this with thetrace_match parameter. Here is the output of that same pipeline, but with"trace_match": true configured:

{  "docs": [    {      "doc": {        "_index": "_index",        "_id": "_id",        "_version": "-3",        "_source": {          "message": "I love burmese cats!",          "pet": "burmese"        },        "_ingest": {          "_grok_match_index": "1",          "timestamp": "2016-11-08T19:43:03.850+0000"        }      }    }  ]}

In the above response, you can see that the index of the pattern that matched was"1". This is to say that it was the second (index starts at zero) pattern inpatterns to match.

This trace metadata enables debugging which of the patterns matched. This information is stored in the ingest metadata and will not be indexed.

The Grok processor comes packaged with its own REST endpoint for retrieving the patterns included with the processor.

GET _ingest/processor/grok

The above request will return a response body containing a key-value representation of the built-in patterns dictionary.

{  "patterns" : {    "BACULA_CAPACITY" : "%{INT}{1,3}(,%{INT}{3})*",    "PATH" : "(?:%{UNIXPATH}|%{WINPATH})",    ...}

By default, the API returns a list of legacy Grok patterns. These legacy patterns predate theElastic Common Schema (ECS) and don’t use ECS field names. To return patterns that extract ECS field names, specifyv1 in the optionalecs_compatibility query parameter.

GET _ingest/processor/grok?ecs_compatibility=v1

By default, the API returns patterns in the order they are read from disk. This sort order preserves groupings of related patterns. For example, all patterns related to parsing Linux syslog lines stay grouped together.

You can use the optional booleans query parameter to sort returned patterns by key name instead.

GET _ingest/processor/grok?s

The API returns the following response.

{  "patterns" : {    "BACULA_CAPACITY" : "%{INT}{1,3}(,%{INT}{3})*",    "BACULA_DEVICE" : "%{USER}",    "BACULA_DEVICEPATH" : "%{UNIXPATH}",    ...}

This can be useful to reference as the built-in patterns change across versions.

Grok expressions that take too long to execute are interrupted and the grok processor then fails with an exception. The grok processor has a watchdog thread that determines when evaluation of a grok expression takes too long and is controlled by the following settings:

It is advised to use theGrok Debugger to debug grok patterns. From there you can test one or more patterns in the UI against sample data. Under the covers it uses the same engine as ingest node processor.

Additionally, it is recommended to enable debug logging for Grok so that any additional messages may also be seen in the Elasticsearch server log.

PUT _cluster/settings{  "persistent": {    "logger.org.elasticsearch.ingest.common.GrokProcessor": "debug"  }}