FME Transformers: 2024.2

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Data Quality

Data Quality

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Filters and Joins

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Categories
Data Quality

Data Quality

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Filters and Joins

Workflows

Workflows

Sampler

Preserves either a total number of features or a sampling of features, depending on the Sampling Type selection. The remainder of the input features are output through the NotSampled port.

This transformer is typically used during testing to reduce data volume by arbitrarily discarding data.

Configuration

Output Ports

Preserved features are output through this port.

Features that are not preserved are output through this port.

Parameters

Group By

When specified, this parameter affects the behavior of the sampling types. For example, say the Sampler receives a set of 15 features. Using the Group By parameter, the Sampler breaks the features into 3 groups.

  • Group 1 has 3 features
  • Group 2 has 5 features
  • Group 3 has 7 features

This table shows how sampling amount and type affect the Group By results:

Sampling Amount Sampling Type Result
4 Every Nth Feature
  • 0 features from Group 1 will be sampled
  • 1 feature from Group 2 will be sampled: the 4th feature
  • 1 feature from Group 3 will be sampled: the 4th feature
4 First N Features
  • 3 features from Group 1 will be sampled
  • 4 features from Group 2 will be sampled: the first 4 features
  • 4 features from Group 3 will be sampled: the first 4 features
4 Last N Features
  • 3 features from Group 1 will be sampled
  • 4 features from Group 2 will be sampled: the last 4 features
  • 4 features from Group 3 will be sampled: the last 4 features

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.

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.

Sampling Rate (N)/Sampling Type

Determines the number of features to send to the Sampled port: either a total number of features, or a sampling of features.

If the Sampling Rate (N) is 0, all input data will be sent to the NotSampled port.

For example:

Sampling Rate Sampling Type Result
1 Every Nth Feature Every feature will be sampled.
2 Every Nth Feature Every second feature will be sampled.
10 First N Features Only the first 10 features will be sampled. All subsequent features will be sent to the NotSampled port.
10 Last N Features Only the last 10 features will be sampled. All earlier features will be sent to the NotSampled port.

Randomize Sampling

When this parameter is left at the default setting of No, the transformer works with features in the order in which they are received.

If this parameter is Yes, the input features are shuffled before they are sampled. All features are blocked in the Sampler until the last feature arrives. The output ports will return the features in the original order they were received by this transformer.

Editing Transformer Parameters

Transformer parameters can be set by directly entering values, using expressions, or referencing other elements in the workspace such as attribute values or user parameters. Various editors and context menus are available to assist. To see what is available, click beside the applicable parameter.

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.

String Functions

These functions manipulate and format strings.

Special Characters

 A set of control characters is available in the Text Editor.

Math Functions

Math functions are available in both editors.
Date/Time Functions Date and time functions are available in the Text Editor.

Math Operators

These operators are available in the Arithmetic Editor.

FME Feature Functions

These return primarily feature-specific values.

FME Parameters

FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters Create your own editable parameters.

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.

For more information, see Transformer Parameter Menu Options.

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