MRF2DDangleRemover

Each feature that is output through the Cleaned port will have a new attribute "mrf_clean_status" added to specify whether the feature was modified, created, or unchanged in the cleaning process. The possible values of this attribute are "Modified", "Created" and "Original".

If Dangle Action is set to Remove Short and Flag Long, for each feature that is a dangle but is longer than this tolerance, a point, label, or circle (depending on the value of Flag Type) will be output through the Flagged port.

If selected, each group of features with the same values in the Group By attributes will be processed separately from other groups.

Note: How parallel processing works with FME: see About Parallel Processing for detailed information.

This parameter determines whether or not the transformer should perform the work across parallel processes. If it is enabled, a process will be launched for each group specified by the Group By parameter.

Parallel Processing Levels

For example, on a quad-core machine, minimal parallelism will result in two simultaneous FME processes. Extreme parallelism on an 8-core machine would result in 16 simultaneous processes.

You can experiment with this feature and view the information in the Windows Task Manager and the Workbench Log window.

No: This is the default behavior. Processing will only occur in this transformer once all input is present.

By Group: This transformer will process input groups in order. Changes of the value of the Group By parameter on the input stream will trigger batch processing on the currently accumulating group. This will improve overall speed if groups are large/complex, but could cause undesired behavior if input groups are not truly ordered.

Considerations for Using Input is Ordered By

Using Ordered input can provide performance gains in some scenarios, however, it is not always preferable, or even possible. Consider the following when using it, with both one- and two-input transformers.

Single Datasets/Feature Types: Are generally the optimal candidates for Ordered processing. If you know that the dataset is correctly ordered by the Group By attribute, using Input is Ordered By can improve performance, depending on the size and complexity of the data.

If the input is coming from a database, using ORDER BY in a SQL statement to have the database pre-order the data can be an extremely effective way to improve performance. Consider using a Database Readers with a SQL statement, or the SQLCreator transformer.

Multiple Datasets/Feature Types: 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, using Ordering with multiple feature types is more complicated than processing a single feature type.

Multiple feature types and features from multiple datasets will not generally naturally occur in the correct order.

One approach is to send all features through a Sorter, sorting on the expected Group By attribute. The Sorter is a feature-holding transformer, collecting all input features, performing the sort, and then releasing them all. They can then be sent through an appropriate filter (TestFilter, AttributeFilter, GeometryFilter, or others), which are not feature-holding, and will release the features one at a time to the transformer using Input is Ordered By, now in the expected order.

The processing overhead of sorting and filtering may negate the performance gains you will get from using Input is Ordered By. In this case, using Group By without using Input is Ordered By may be the equivalent and simpler approach.

In all cases when using Input is Ordered By, if you are not sure that the incoming features are properly ordered, they should be sorted (if a single feature type), or sorted and then filtered (for more than one feature or geometry type).

As with many scenarios, testing different approaches in your workspace with your data is the only definitive way to identify performance gains.

Cleaning tolerance is used as the default tolerance unless the Feature Tolerance Attribute is specified and valid. The minimum tolerance allowed is 0.0.

The number of layers used in cleaning the data is determined by the number of different tolerance values of input features. Features that have the same tolerances are processed as being on the same layer.

The default value is 1.0 and the minimum value is 0.0.

If Dangle Action is set to Remove Short and Flag Long, for each feature that is a dangle but is longer than this tolerance, a point, label, or circle (depending on the value of Flag Type) will be output through the Flagged port.

For each feature that is a dangle but is longer than the flag size tolerance, this parameter sets the flag type as a point, label or circle, if Dangle Action is set to Remove Short and Flag Long.