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. 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

Sorter Transformation Example

Sorter Transformation Example

You have a database table PRODUCT_ORDERS that contains information about all the orders which were placed by the customer.
ORDER_ID
ITEM_ID
ITEM
QUANTITY
PRICE
43
123456
ItemA
3
3.04
41
456789
ItemB
2
12.02
43
000246
ItemC
6
34.55
45
000468
ItemD
5
0.56
41
123456
ItemA
4
3.04
45
123456
ItemA
5
3.04
45
456789
ItemB
3
12.02
Use the Sorter transformation on PRODUCT_ORDERS and specify the ORDER_ID as the sort key with direction as descending.
After sorting the data, the Data Integration Service passes the following rows out of the Sorter transformation:
ORDER_ID
ITEM_ID
ITEM
QUANTITY
PRICE
45
000468
ItemD
5
0.56
45
123456
ItemA
5
3.04
45
456789
ItemB
3
12.02
43
123456
ItemA
3
3.04
43
000246
ItemC
6
34.55
41
456789
ItemB
2
12.02
41
123456
ItemA
4
3.04
You need to find out the total amount and item quantity for each order. You can use the result of the Sorter transformation as an input to an Aggregator transformation. Use sorted input in Aggregator transformation to improve performance.
When you do not use sorted input, the Data Integration Service performs aggregate calculations as it reads. The Data Integration Service stores data for each group until it reads the entire source to ensure that all aggregate calculations are accurate. If you use sorted input and do not presort data correctly, you receive unexpected results.
The Aggregator transformation has the ORDER_ID group by port, with the sorted input option selected. When you pass the data from the Sorter transformation, the Aggregator transformation groups ORDER_ID to calculate the total amount for each order.
ORDER_ID
SUM
45
54.06
43
216.42
41
36.2

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