Table of Contents

Search

  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Macro Transformation
  30. Match Transformation
  31. Match Transformations in Field Analysis
  32. Match Transformations in Identity Analysis
  33. Normalizer Transformation
  34. Merge Transformation
  35. Parser Transformation
  36. Python Transformation
  37. Rank Transformation
  38. Read Transformation
  39. Relational to Hierarchical Transformation
  40. REST Web Service Consumer Transformation
  41. Router Transformation
  42. Sequence Generator Transformation
  43. Sorter Transformation
  44. SQL Transformation
  45. Standardizer Transformation
  46. Union Transformation
  47. Update Strategy Transformation
  48. Web Service Consumer Transformation
  49. Parsing Web Service SOAP Messages
  50. Generating Web Service SOAP Messages
  51. Weighted Average Transformation
  52. Window Transformation
  53. Write Transformation
  54. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Router Transformation Overview

Router Transformation Overview

The Router transformation is an active transformation that routes data into multiple output groups based on one or more conditions. Route the output groups to different transformations or to different targets in the mapping.
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:
The Router transformation includes an input group, the output group called default, and the France, Japan, and USA output groups. All groups have the following ports: COUNTRY, CUSTOMER_NO, FIRSTNAME, LASTNAME.
  1. Input group
  2. Input ports
  3. Default output group
  4. User-defined output groups

0 COMMENTS

We’d like to hear from you!