Grok is basically used for pattern matching, and we can structure an arbitrary text by parsing it through grok expressions. Grok patterns can be used for the grok processor in Dev Tools and in the Logstash grok filter. There are more than 120 grok patterns that Elastic Stack supports.
There are different data sources from where we can get arbitrary data such as syslog logs, Apache logs, MySQL logs, or any other type of log. Now, these types of data are not labeled with a field name, and without that, we cannot process it in Elasticsearch or Kibana. To overcome this issue, we need to parse the log data with grok expression. For that, we need to map the log values with field names in the grok expression and then simulate it to get the values in the field. Once this is done, we can use that expression into Logstash grok filter to filtering this log data.
To create the grok expression, we need to click on the Grok Debugger
link after Search Profiler
, which will open the following...