Those with an eagle eye will notice Splunk has inadvertently included a trailing space with this capture (underlined above). Take a copy of this regex and put it in Notepad. When you get back to the save screen, you’ll notice Splunk has built another regex for this use case. Now go through the same process, but selecting the userID from in front of “User.” Then use the small back arrow (highlighted in red) to move back through the wizard to the Select Sample screen again. (Be sure to use the validate step in the wizard as this will allow you to eliminate any false positives and automatically refines your regex to ensure it works accurately)Ĭopy the regular expression from this screen and save it in Notepad. If you work through the wizard using the Regular Expression option and select the user value in your log file (from in front of “undername:domainname”) you’ll reach the save screen. So how can you combine two different regex strings to build a single field in Splunk? The easiest way to let Splunk build the regex for you in the field extractor.

You could write these regex expressions yourself, but be warned, although Splunk adheres to the pcre (php) implementation of regex, in practice there are subtle differences (such as no formal use of look forward or look back). The question then arises, how can we combine these into a single field within Splunk? Context – SessionId: 123456- User x987654: Group ABCDEFGĪs you can see, the log file contains the following userID and IP address, but in different formats.For example, userIDs and source IP addresses appear in two different locations on different lines. When working with something like Netscaler log files, it’s not uncommon to see the same information represented in different ways within the same log file. The Splunk field extractor is a WYSIWYG regex editor. You can write your own regex to retrieve information from machine data, but it’s important to understand that Splunk does this behind the scenes anyway, so rather than writing your own regex, let Splunk do the heavy lifting for you. Splunk allows you to cater for this and retrieve meaningful information using regular expressions (regex).

So basically regex used to identify fields and list them in proper manner which later can be used for reporting,sorting and dashboard.Splunk is an extremely powerful tool for extracting information from machine data, but machine data is often structured in a way that makes sense to a particular application or process while appearing as a garbled mess to the rest of us.

They have their own grammar and syntax rules.splunk uses regex for identifying interesting fields in logs like username,credit card number,ip address etc.By default splunk automatically extracts interesting fields and display them at left column is search result -only condition is log must contain key value pairs which means logs should contains field name and its value - like for username it should appear in log like usename=x or user:x.Extracted fields can be used later for sorting data,making specialized reports,creating valueable dashboards.But if logs do not contain field name in key value pair- like username or other fileds appears in log at random place then splunk will not detect the username automatically.In this condition regex comes for your help.You have to teach splunk to extract the field using regex. Regular expressions match patterns of characters in text. Regular expressions are extremely useful in extracting information from text such as code, log files, spreadsheets, or even documents.Regular expressions or regex is a specialized language for defining pattern matching rules.

Splunk regex