Table of Contents


  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

Java Transformation on the Spark Engine

Java Transformation on the Spark Engine

Some processing rules for the Spark engine differ from the processing rules for the Data Integration Service.

General Restrictions

The Java transformation is supported with the following restrictions on the Spark engine:
  • The Java code in the transformation cannot write output to standard output when you push transformation logic to Hadoop. The Java code can write output to standard error which appears in the log files.
  • For date/time values, the Spark engine supports the precision of up to microseconds. If a date/time value contains nanoseconds, the trailing digits are truncated.


The Java transformation has the following restrictions when used with partitioning:
  • The Partitionable property must be enabled in the Java transformation. The transformation cannot run in one partition.
  • The following restrictions apply to the Transformation Scope property:
    • The value Transaction for transformation scope is not valid.
    • If you enable an input port for partition key, the transformation scope must be set to All Input.
    • Stateless must be enabled if the transformation scope is row.

Mapping Validation

Mapping validation fails in the following situations:
  • You reference an unconnected Lookup transformation from an expression within a Java transformation.
  • You select a port of a complex data type as the partition or sort key.
  • You enable nanosecond processing in date/time and the Java transformation contains a port of complex data type with an element of a date/time type. For example, a port of type
    is not valid if you enable nanosecond processing in date/time.
The mapping fails in the following situation:
  • The Java transformation and the mapping use different precision modes when the Java transformation contains a decimal port or a complex port with an element of a decimal data type.
    Even if high precision is enabled in the mapping, the mapping processes data in low-precision mode in some situations, such as when the mapping contains a complex port with an element of a decimal data type, or the mapping is a streaming mapping. If high precision is enabled in both the Java transformation and the mapping, but the mapping processes data in low-precision mode, the mapping fails.

Using External .jar Files

To use external .jar files in a Java transformation, perform the following steps:
  1. Copy external .jar files to the Informatica installation directory in the Data Integration Service machine at the following location:
    <Informatic installation directory>/services/shared/jars
  2. Recycle the Data Integration Service.
  3. On the machine that hosts the Developer tool where you develop and run the mapping that contains the Java transformation:
    1. Copy external .jar files to a directory on the local machine.
    2. Edit the Java transformation to include an import statement pointing to the local .jar files.
    3. Update the classpath in the Java transformation.
    4. Compile the transformation.


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