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

Flatten Array

Flatten Array

The Normalizer transformation flattens a one-dimensional array to a primitive data type and an n-dimensional array to an (n-1)-dimensional array. The number of rows that the transformation creates is the same as the size of the array.
For example, if you flatten an array port with 10 string elements, the output returns 10 string ports. If you flatten a 3-dimensional array, the output returns a 2-dimensional array.
A table contains a string port Name and an array port Phones. You want to flatten the array port. The table contains the following values:
Name
Phones
Adams
[205-128-6478, 722-515-2889, 650-213-4020]
Jane
[650-321-4506]
When you flatten the array port, the output is as follows:
Name
Phones
GCID_Phones
Adams
205-128-6478
1
Adams
722-515-2889
2
Adams
650-213-4020
3
Jane
650-321-4506
1
You can edit the Occurs value of a flattened field to extract a specific number of elements in the array. The value must be a positive integer greater than 1. The value determines the number of elements to extract. For example, you can change the value of Occurs to 2 to extract the first two elements of the array. The output is as follows:
Name
Phones
GCID_Phones
Adams
205-128-6478
1
Adams
722-515-2889
2
Jane
650-321-4506
1

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