Table of Contents

Search

  1. Preface
  2. Working with Transformations
  3. Aggregator Transformation
  4. Custom Transformation
  5. Custom Transformation Functions
  6. Data Masking Transformation
  7. Data Masking Examples
  8. Expression Transformation
  9. External Procedure Transformation
  10. Filter Transformation
  11. HTTP Transformation
  12. Identity Resolution Transformation
  13. Java Transformation
  14. Java Transformation API Reference
  15. Java Expressions
  16. Java Transformation Example
  17. Joiner Transformation
  18. Lookup Transformation
  19. Lookup Caches
  20. Dynamic Lookup Cache
  21. Normalizer Transformation
  22. Rank Transformation
  23. Router Transformation
  24. Sequence Generator Transformation
  25. Sorter Transformation
  26. Source Qualifier Transformation
  27. SQL Transformation
  28. Using the SQL Transformation in a Mapping
  29. Stored Procedure Transformation
  30. Transaction Control Transformation
  31. Union Transformation
  32. Unstructured Data Transformation
  33. Update Strategy Transformation
  34. XML Transformations

Transformation Guide

Transformation Guide

Joining Two Branches of the Same Pipeline

Joining Two Branches of the Same Pipeline

When you join data from the same source, you can create two branches of the pipeline. When you branch a pipeline, you must add a transformation between the source qualifier and the Joiner transformation in at least one branch of the pipeline. You must join sorted data and configure the Joiner transformation for sorted input.
For example, you have a source with the following ports:
  • Employee
  • Department
  • Total Sales
In the target, you want to view the employees who generated sales that were greater than the average sales for their departments. To do this, you create a mapping with the following transformations:
  • Sorter transformation
    . Sorts the data.
  • Sorted Aggregator transformation
    . Averages the sales data and group by department. When you perform this aggregation, you lose the data for individual employees. To maintain employee data, you must pass a branch of the pipeline to the Aggregator transformation and pass a branch with the same data to the Joiner transformation to maintain the original data. When you join both branches of the pipeline, you join the aggregated data with the original data.
  • Sorted Joiner transformation
    . Uses a sorted Joiner transformation to join the sorted aggregated data with the original data.
  • Filter transformation
    . Compares the average sales data against sales data for each employee and filter out employees with less than above average sales.
The following figure shows a mapping that joins two branches of the same pipeline:
""
You can also join data from output groups of the same transformation, such as the Custom transformation or XML Source Qualifier transformation. Place a Sorter transformation between each output group and the Joiner transformation and configure the Joiner transformation to receive sorted input.
Joining two branches might impact performance if the Joiner transformation receives data from one branch much later than the other branch. The Joiner transformation caches all the data from the first branch, and writes the cache to disk if the cache fills. The Joiner transformation must then read the data from disk when it receives the data from the second branch. This can slow processing.

0 COMMENTS

We’d like to hear from you!