NetworkCostCalculator

Network lines.

Input Line features must be a topologically noded network with features connecting at line ends only. That is, all features must be split at junctions.

There can only be one Source input for each group.

All lines that are connected to the Source input port are output through the Connected port.

The lines that are not connected to the Source input port will be output through the Disconnected port. If Output Optimal Cost As parameter is set to Z-Values, the dimension of the disconnected lines is set to 2D. Otherwise, disconnected lines are untouched.

There can only be one Source input for each group. All other inputs and non-linear features are output through the <Rejected> port.

Choose the attributes to group by.

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.

If Weight Type is set to By Length (Forward Only) or By One Attribute, then the weight of each input line is set to the length of the line or the attribute value specified in the Forward Weight Attribute. In this case, the algorithm will only consider the original orientation of the lines when computing the cost of the shortest path.

If Weight Type is set to By Length or By Two Attributes, then the shortest path algorithm will consider both directions of the input lines. If Weight Type is set to By Two Attributes, the original orientation of the input line has the weight specified in the Forward Weight Attribute and the reversed orientation of the input line has the weight specified in the Reverse Weight Attribute. If Weight Type is set to By Length, the weight of both the original orientation and the reversed orientation of the input line is set to the length of the line.

Only linear features with non-negative weight attribute values are allowed if the Weight Type is set to By One Attribute or By Two Attributes. If a feature does not have the attribute specified in the Forward Weight Attribute or the Reverse Weight Attribute, a zero weight is used for the line.

If this parameter is set to Z-Values, then the optimal cost for each connected node is set as the Z-value of the node. Otherwise, the optimal cost is set as the measure value of the node with the measure name specified in Measure Name.

If you leave the Measure Name empty, the default measure name will be used.

If this parameter is set to Yes, the transformer snaps the source points to the closest end points of the network lines.

The source points are only snapped to the network lines if they are within the tolerance specified in Snapping Tolerance.