Managing data patterns

A data pattern defines the way in which the data collected (semi-structured data) can be structured, indexed, and made available for searching. One of the primary functions of creating a data pattern is to specify fields that must be extracted from the data collected. Fields are name=value pairs that represent a grouping by which your data can be categorized. The fields that you specify at the time of creating a data pattern are added to each record in the data indexed, enabling you to both search effectively and carry out advanced analysis by using search commands. You can also assign a field type (category: an integer, a string, or a long integer) for each of the fields that you intend to get extracted. Assigning a field type enables you to run specific search commands on the fields of a certain type and perform advanced analysis.    

The Data Patterns tab allows you to configure data patterns that can be used by the data collectors for collecting data in the specified way. While creating a data collector, it is important that you select an appropriate data pattern. This is necessary so that the indexed data looks as you expected, with events categorized in multiple lines (raw event data), fields extracted, and time stamp extracted. The more appropriate the data pattern, the more chances that your search will be effective.

What is a data pattern made up of?

A data pattern is made up of a Java regular expression used for parsing the data file and eventually displaying it in the form of search results. The usefulness of your search results is determined by the definition of your data pattern. The subpatterns included in the data pattern are displayed as fields in your search results. You can either provide a sample time stamp and sample text to enable automatic detection of the primary pattern and date format, or you can specify a custom primary pattern, date format, and subpatterns.

While performing a search, you can find data of a certain type (data pattern) compared to data coming from a single source file. You can do this by clicking the DATA_PATTERN field in one of your search results, so that it is added to your search criteria. For more information, see Understanding fields and tags.

When do I need to create a data pattern?

The product provides a list of default data patterns (mostly for log formats) that you can use directly to index your data files. Data patterns are available for most of the common log formats. Therefore, at most times you might not need to create a data pattern. You can directly use the defaults available. For more information about the default data patterns, see List of data patterns supported.

If you find that you need a custom data pattern for your particular data file, you can:

• Clone an existing data pattern and customize it to suit your needs.
• Create a new data pattern.

How do I know which data pattern is appropriate for my data file?

When you create a data collector, after you point to the actual data file, you might need to assign a data pattern. You can find the most relevant data patterns (available by default) that match your data file by using the Auto-Detect button. Along with the most relevant data patterns, you can also find the most relevant date formats. When you select a data pattern, you can also see a preview of how the records will be parsed. If you are not satisfied with the current preview, select another data pattern, and see the preview results. You can repeat this cycle until you are satisfied with the results shown.

If there is no suitable data pattern available, then you can select a suitable date format. By selecting a date format, you can index and categorize your data in a simple way whereby the time stamp is considered one category and the rest of the data in your data file is considered another category. Selecting a date format, as compared to a usual data pattern, will generally provide search results that might not accommodate a richer categorization of data. Furthermore, this might restrict advanced searching capabilities. If you select both a data pattern and a date format, then the date format is used to index the timestamp in the file and the data pattern is used to index rest of the data appearing in the file. For examples of date formats, see Sample date formats.

If there is no suitable data pattern found, you can do one of the following:

• Create a new data pattern and use that for indexing the data.

• Either select the Free Text with Timestamp option or create a new date format and use that for indexing the timestamp.

  This approach captures the timestamp while the rest of the data appears in raw format.

• If no time stamp exists in your data, select Free Text without Timestamp as an option.
  By selecting free text as a data pattern, you can index all of your data to appear in a raw format based on the time when it is indexed by the product.

  Note

  If you use free text as a data pattern, each line in the data file is indexed as a new record.

• If you find a data pattern that provides results that partly match your expectations, you can either retain that selection or choose to clone that data pattern and customize it to suit your needs.

Constructing a data pattern

A data pattern is formed of Java regular expressions used for parsing the data and eventually displaying it in the form of search results. The usefulness of your search results is determined by the definition of your data pattern. When the data pattern is used, the Indexer captures all of the lines in your data, interprets the data structure, and eventually allows you to effectively search the data with the help of fields (name=value).

Fields act as a grouping mechanism that represents various categories for consistently repeated trends in your data. Before you create a data pattern, you need to identify the fields by which you would like to eventually search the data. For more information about identifying fields for creating a data pattern, see Identifying fields in the data file.

You can construct your data pattern depending on how searchable your data needs to be. You need to read your data file to identify additional sections or expressions that you might be interested in searching and investigating.

A data pattern constitutes of a primary pattern and supporting subpatterns that helps index your data in the specified way.

Data pattern = Primary pattern + Supporting subpatterns

where,

%{Mytimestamp:timestamp} 
\[%{Data:debuglevel}\] 
%{Data:component} - 
\[Thread=%{Data:threadid}\] 
%{Ip:clientip} - 
%{MultilineEntry:details}

Ip=(?<![0-9])(?:(?:25[0-5]|2[0-4]
[0-9]|[0-1][0-9]{1,2})[.](?:25[0-5]|
2[0-4][0-9]|[0-1]?[0-9]{1,2})[.]
(?:25[0-5]|2[0-4][0-9]|[0-1]?
[0-9]{1,2})[.](?:25[0-5]|2[0-4]
[0-9]|[0-1]?[0-9]{1,2}))(?![0-9])

For more information about an overall use case of creating a data pattern, see Examples of creating a data pattern.

For more information about data pattern examples, see Sample data patterns.

For more information about subpatterns examples, see Sample subpatterns.

For more information about the various field inputs for creating a data pattern, see Adding a new data pattern.

How to identify fields in the data file

Before you create a data pattern, you need to analyze your data file to find out if the file follows a certain pattern that can be captured while creating the data pattern.

Suppose you want create a data pattern to index the following data:

In the preceding lines, every new line starts with the time stamp. If you try to analyze a consistently followed format in these lines, you will notice that each line provides the following information, presented in the same order as they appear in the lines:

1. Time stamp
2. Component name
3. Debug information
4. Application message

To identify what can be fields, you need to identify the format that is being consistently followed in the data file, and then identify how this information can be grouped. You can create a field for each grouping. This ensures that all information that can be categorized into a group is indexed and is available for search.

Suppose that you think that the word CONFIGURATIONS might be useful to extract as a group. However, you must not create a field for this word, because it does not appear consistently on each line. If you choose this word as a field, the product might skip the other lines that do not contain the word, and those lines will not be available for searching. You can use search commands such as extract to find such expressions that you might have wanted to choose while creating a data pattern.

Functions available for creating and using data patterns

When you are creating a data pattern, the following functions are available:

At the time of creating a data collector, the following functions related to data patterns are available:

Functions available while creating a data collector

Viewing configured data patterns

The Administration > Data Patterns tab allows you to view and manage your data patterns. From here, you can perform the following actions:

Action	Icon	Description
Add Data Pattern		Add a new data pattern. For instructions about adding a data pattern, see Adding a new data pattern.
Edit Data Pattern		Edit the selected data pattern. You can modify the same details that you provided while adding a new data pattern.
Delete Data Pattern		Delete the selected data pattern. Note: You cannot delete default data patterns provided with the product.
View Data Pattern		View details of the selected data pattern.
Clone Data Pattern		Create a copy of the selected data pattern.
Search		Search for a data pattern by entering an appropriate search string in the search bar at the top right of your screen. You can search for a data pattern by name, category, or by the primary pattern.
Hide Pattern	None	Select this check box to hide the Primary Pattern column.

The Data Patterns tab provides the following information: