CommonSegmentFinder

Group By

The default behavior is to use the entire set of input features as the group. This option allows you to select attributes that define which groups to form. Each set of features that have the same value for all of these attributes will be processed as an independent group.

Complete Groups

Select the point in processing at which groups are processed: When All Features Received This is the default behavior. Processing will only occur in this transformer once all input is present. When Group Changes (Advanced) This transformer will process input groups in order. Changes of the value of the Group By parameter on the input stream will trigger processing on the currently accumulating group. This may improve overall speed (particularly with multiple, equally-sized groups), but could cause undesired behavior if input groups are not truly ordered. Considerations for Using Group By There are two typical reasons for using When Group Changes (Advanced) . The first is incoming data that is intended to be processed in groups (and is already so ordered). In this case, the structure dictates Group By usage - not performance considerations. The second possible reason is potential performance gains. Performance gains are most likely when the data is already sorted (or read using a SQL ORDER BY statement) since less work is required of FME. If the data needs ordering, it can be sorted in the workspace (though the added processing overhead may negate any gains). Sorting becomes more difficult according to the number of data streams. Multiple streams of data could be almost impossible to sort into the correct order, since all features matching a Group By value need to arrive before any features (of any feature type or dataset) belonging to the next group. In this case, using Group By with When All Features Received may be the equivalent and simpler approach. Note  Multiple feature types and features from multiple datasets will not generally naturally occur in the correct order. As with many scenarios, testing different approaches in your workspace with your data is the only definitive way to identify performance gains.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

Table Tools

Transformers with table-style parameters have additional tools for populating and manipulating values.

Row Reordering	Enabled once you have clicked on a row item. Choices include: Add a row Remove a row Move current row up one Move current row down one Move current row to top Move current row to bottom
Cut, Copy, and Paste	Enabled once you have clicked on a row item. Choices include: Cut a row - delete and copy to clipboard Copy a row to the clipboard Paste a row from the clipboard Cut, copy, and paste may be used within a transformer, or between transformers.
Filter	Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.
Import	Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.
Reset/Refresh	Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.