A Router transformation is similar to a Filter transformation because both transformations use a condition to test data. A Filter transformation tests data for one condition and drops the rows of data that do not meet the condition. A Router transformation tests data for one or more conditions and can route rows of data that do not meet any of the conditions to a default output group.

If you need to test the same input data based on multiple conditions, use a Router transformation in a mapping instead of creating multiple Filter transformations to perform the same task. The Router transformation is more efficient. For example, to test data based on three conditions, you can use one Router transformation instead of three Filter transformations. When you use a Router transformation in a mapping, the Data Integration Service processes the incoming data once. When you use multiple Filter transformations in a mapping, the Data Integration Service processes the incoming data for each transformation.

A Router transformation consists of input and output groups, input and output ports, group filter conditions, and advanced properties that you configure in the Developer tool.

When the Spark engine runs a mapping with a Router transformation, the Spark engine processes the upstream mapping pipeline once and stages the data on the HDFS so it can be used by each downstream branch.

The following figure shows a sample Router transformation and its components:

Input group
Input ports
Default output group
User-defined output groups