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

Dynamic Lookup Cache Example

Dynamic Lookup Cache Example

You can use a dynamic lookup cache to insert and update rows in the target. When you use a dynamic lookup cache, you can insert and update the same rows in the cache that you insert and update in the target.
For example, you need to update a table that contains customer data. The source data contains rows of customer data to insert or update in the target. Create a dynamic cache that represents the target. Configure a Lookup transformation to lookup customers in the dynamic cache.
The following figure shows a mapping that has a Lookup transformation with a dynamic lookup cache:
The mapping contains a source, Lookup transformation, and then two branches that insert records into the target and update records into the target. Each branch contains a Router transformation that selects the rows marked for insert or update.
The Router transformation splits into two branches. The Router transformation passes insert rows into one branch and update rows into the other branch. Each branch contains an Update Strategy transformation that writes rows to the target. Both branches contain the same target.
When the mapping starts, the Integration Service builds the lookup cache from customer target table. When the Integration Service reads a row that is not in the lookup cache, it inserts the row in the cache.
The Lookup transformation returns each row to a Router transformation. Based on whether the row is marked for insert or update, the Router transformation directs the row to one of the Update Strategy transformations. The Router transformation determines whether rows are marked for insert or update based on the NewLookupRow property. The Update Strategy transformations mark each row as insert or update before passing it to the target.
The customer target table changes as the mapping runs. The Integration Service inserts new rows and updates existing rows in the lookup cache. The Integration Service keeps the lookup cache and customer target table synchronized.
To generate keys for the target, use Sequence-ID in the associated port. The Integration Service uses the sequence ID as the primary key for each new row inserted into the target table.
You increase session performance when you use a dynamic lookup cache because you build the cache from the database once.

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