Setting up data patterns to extract fields

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 (data type) for the field values that you intend to extract at the time of creating a data pattern. 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 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. Doing this can be useful while performing advanced field extraction at the time of editing or cloning a 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. And you will notice that the file follows a consistent pattern.

The following information (groups) appears in the preceding lines from left to right:

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

For each of the preceding groups, you can assign a field.

Can I use data patterns created in English in other languages?

TrueSight IT Data Analytics is tested on localized operating system platforms and browsers in a list of supported languages. For more information, see  Language information for IT Data Analytics

You can use data patterns created in English in the list of supported languages. To do this, you need to edit or clone the data pattern and change the Locale setting to the desired language and save the data pattern. After changing the Locale setting, you can also specify date formats and subpatterns in the language selected. For more information, see Editing or cloning data patterns.

Understanding field types

Data can be be of various types such as, numeric and alphanumeric. A field type defines the type of data that the value of a field can take. Also, the field type defines how the fields are stored in the data store. Storing fields with the correct field type enables you to perform an effective search and also enables you to use numeric operations of search commands.

The following example can help you understand how field types can be useful.

When you create a new data pattern, you plan the fields that you want to extract, and you set the field type for each field. For example, if you want to extract a field that is expected to have alphanumeric values, you set the field type to STRING, but if you want to extract a field that is expected to have numeric values only, you set the field type to INTEGER, LONG, DOUBLE, OR FLOAT.

The following table describes the various field types supported:

The preceding field types are available for selection only if they are found relevant to the field value.

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

Icons and associated functions on the Data Patterns tab

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. Notes: You can modify the same details that you provided while adding a new data pattern. You cannot modify a data pattern that was imported via a content pack. However, you can clone the data pattern, customize it, and then save it.
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		In the search bar, at the top-right side of your screen, you can filter data patterns in the following ways: Name Category Primary pattern Content pack name
Hide Pattern	None	Select this check box to hide the Primary Pattern column.

The Data Patterns tab provides the following information: