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

Defining a Join Condition

Defining a Join Condition

The join condition contains ports from both input sources that the Data Integration Service uses to join two rows.
Depending on the type of join selected, the Data Integration Service either adds the row to the result set or discards the row. The Joiner transformation produces result sets based on the join type, condition, and input data sources.
Before you define a join condition, verify that the master and detail sources are configured for optimal performance. During a mapping run, the Data Integration Service compares each row of the master source against the detail source. To improve performance for an unsorted Joiner transformation, use the source with fewer rows as the master source. To improve performance for a sorted Joiner transformation, use the source with fewer duplicate key values as the master.
Use one or more ports from the input sources of a Joiner transformation in the join condition. Additional ports increase the time necessary to join two sources. The order of the ports in the condition can impact the performance of the Joiner transformation. If you use multiple ports in the join condition, the Data Integration Service compares the ports in the order you specify.
If you join Char and Varchar datatypes, the Data Integration Service counts any spaces that pad Char values as part of the string:
Char(40) = "abcd" Varchar(40) = "abcd"
The Char value is “abcd" padded with 36 blank spaces, and the Data Integration Service does not join the two fields because the Char field contains trailing spaces.
The Joiner transformation does not match null values. For example, if both EMP_ID1 and EMP_ID2 contain a row with a null value, the Data Integration Service does not consider them a match and does not join the two rows. To join rows with null values, replace null input with default values, and then join on the default values.
You can define a simple or advanced condition type. You can also define an expression parameter. An expression parameter is a parameter that contains the join expression. You can change the parameter value at run time with a mapping parameter.

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