BoundingBoxAccumulator

One bounding box feature is output for each unique combination of values of the attributes specified in the Group By parameter. The bounding box is the smallest rectangle that contains all the features that were members of the group. If Group By attributes are not specified, a single feature is output that represents the bounding box of all the features.

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.

The Topfer index is a rule of thumb that dictates the number of source and destination features a generalized map should maintain. This parameter defaults to No. If you choose Yes, the Topfer Index Attribute and Source and Destination Scale parameters are enabled.

If you set the Calculate Topfer Index parameter to Yes, an attribute containing the Topfer index will be attached to both the BoundingBox and Original output features.

The following formulas are used to calculate the scale factors of the source and destination data:

N-destination = N-source*((S-source/S-destination)**0.5)

where:

N-destination = number of features output

N-source = number of features in input

S-source = scale of source map

S-destination = scale of destination map

Specifies how attributes should be accumulated. If Drop Incoming Attributes is selected, all incoming attributes are removed from the features. Merge Incoming Attributes merges all attributes from overlapping features. Use Attributes From One Feature takes all attributes from one representative feature.

Allows you to specify a List Name.

If a List Name is supplied, for each output feature, a list is created of all the attributes of input features that contain the output feature. A list with the same name is created for traits.

Note: List attributes are not accessible from the output schema in Workbench unless they are first processed using a transformer that operates on them, such as ListExploder or ListConcatenator. All list attribute transformers are displayed in the Contents pane of the Transformer Help under Lists. Alternatively, AttributeExposer can be used.

All Attributes: Every attribute from all input features that created an output surface will be added to the list specified in List Name.

Selected Attributes: Only the attributes specified in the Selected Attributes parameter will be added to the list specified in List Name.

The attributes to be added to the list when Add To List is Selected Attributes.