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

Frame

Frame

The frame determines which rows are included in the calculation for the current input row, based on their relative position to the current row.
If you use an aggregate function instead of LEAD or LAG, you must specify a window frame. LEAD and LAG reference individual row sand ignore the frame specification.
The start offset and end offset describe the number of rows that appear before and after the current input row. An offset of "0" represents the current input row. For example, a start offset of -3 and an end offset of 0 describes a frame including the current input row and the three rows before the current row.
The following image shows a frame with a start offset of -1 and an end offset of 1:
The table has five rows. The middle row is the current input row, and the frame includes one row before the current row and one row after the current row.
For every input row, the function performs an aggregate operation on the rows inside the frame. If you configure an aggregate expression like SUM with the preceding frame, the expression calculates the sum of the values within the frame and returns a value of 6000 for the input row.
You can also specify a frame that does not include the current input row. For example, a start offset of 10 and an end offset of 15 describes a frame that includes six total rows, from the tenth to the fifteenth row after the current row.
The start offset must be less than or equal to the end offset.
Offsets of
All Rows Preceding
and
All Rows Following
represent the first row of the partition and the last row of the partition. For example, if the start offset is All Rows Preceding and the end offset is -1, the frame includes one row before the current row and all rows before that.
The following figure illustrates a frame with a start offset of 0 and an end offset of All Rows Following:
The frame includes the current input row and all rows below the current input row.

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