Table of Contents

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  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. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Normalizer Transformation
  33. Merge Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Group By Ports

Group By Ports

You can define groups of rows to aggregate instead of running an aggregation across all the input data. For example, you can calculate the total company sales or you can find the total sales grouped by region.
To define a group for the aggregate expression, select the appropriate input, input/output, output, and variable ports in the Aggregator transformation. You can select multiple group by ports to create a new group for each unique combination. The Data Integration Service then performs the defined aggregation for each group.
When you group values, the Data Integration Service produces one row for each group. If you do not group values, the Data Integration Service returns one row for all input rows. The Data Integration Service returns the last row of each group with the result of the aggregation. You can specify to return a specific row. For example, if you use the FIRST aggregator function, the Data Integration Service returns the first row.
When you select multiple group by ports in the Aggregator transformation, the Data Integration Service uses a port order to determine the order by which it groups. The group order can affect the results. Order the group by ports to ensure the appropriate grouping. You can change the port order after you select the ports in the group/
For example, you might create an output port called Price_Out. The expression for Price_Out is SUM (Qty * Price). You define Store_ID and Item as the group by ports. The transformation returns the total price for each item by store.
The input rows might contain the following data:
Store_ID
Item
Qty
Price
101
battery
3
2.99
101
battery
1
3.19
101
battery
2
2.59
101
AAA
2
2.45
201
battery
1
1.99
201
battery
4
1.59
301
battery
1
2.45
The Data Integration Service performs the aggregate calculation on the following unique groups:
Store_Id
Item
101
battery
101
AAA
201
battery
301
battery
The Data Integration Service returns the Store_ID, Item, Qty, and Price from the last row with the sum of (Price * Qty) for each item by store :
Store_ID
Item
Qty
Price
Price_Out
101
battery
2
2.59
17.34
101
AAA
2
2.45
4.90
201
battery
4
1.59
8.35
301
battery
1
2.45
2.45

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