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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

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Temporarily Store Data and Simplify Complex Expressions

Temporarily Store Data and Simplify Complex Expressions

Variables increase performance when you enter multiple related expressions in the same transformation. You can define components as variables instead of parsing and validating the same expression components mulitple times in the transformation.
For example, if an Aggregator transformation uses the same filter condition before calculating sums and averages, you can define this condition as a variable, and then reuse the condition in both aggregate calculations.
You can simplify complex expressions. If an Aggregator includes the same calculation in multiple expressions, you can increase performance by creating a variable to store the results of the calculation.
For example, you might create the following expressions to find both the average salary and the total salary with the same data: 
AVG( SALARY, ( ( JOB_STATUS = 'Full-time' ) AND (OFFICE_ID = 1000 ) ) )
SUM( SALARY, ( ( JOB_STATUS = 'Full-time' ) AND (OFFICE_ID = 1000 ) ) )
Instead of entering the same arguments for both calculations, you might create a variable port for each condition in this calculation, and then change the expression to use the variables.
The following table shows how to use variables to simplify complex expressions and temporarily store data:
Port
Value
V_CONDITION1
JOB_STATUS = ‘Full-time’
V_CONDITION2
OFFICE_ID = 1000
AVG_SALARY
AVG(SALARY, (V_CONDITION1 AND V_CONDITION2) )
SUM_SALARY
SUM(SALARY, (V_CONDITION1 AND V_CONDITION2) )
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