Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. Cleanse transformation
  8. Data Masking transformation
  9. Data Services transformation
  10. Deduplicate transformation
  11. Expression transformation
  12. Filter transformation
  13. Hierarchy Builder transformation
  14. Hierarchy Parser transformation
  15. Hierarchy Processor transformation
  16. Input transformation
  17. Java transformation
  18. Java transformation API reference
  19. Joiner transformation
  20. Labeler transformation
  21. Lookup transformation
  22. Machine Learning transformation
  23. Mapplet transformation
  24. Normalizer transformation
  25. Output transformation
  26. Parse transformation
  27. Python transformation
  28. Rank transformation
  29. Router transformation
  30. Rule Specification transformation
  31. Sequence Generator transformation
  32. Sorter transformation
  33. SQL transformation
  34. Structure Parser transformation
  35. Transaction Control transformation
  36. Union transformation
  37. Velocity transformation
  38. Verifier transformation
  39. Web Services transformation

Transformations

Transformations

Window functions

Window functions

In advanced mode, you can use a window function to concisely express stateful computations. A window function takes a small subset of a larger data set for processing and analysis.
Window functions operate on a group of rows and calculate a return value for every input row.
Use window functions to perform the following tasks:
  • Retrieve data from upstream or downstream rows.
  • Calculate a cumulative sum based on a group of rows.
  • Calculate a cumulative average based on a group of rows.
Before you define a window function, configure the following window properties on the
Window
tab:
Frame
Defines the rows that are included in the frame for the current input row, based on physical offsets from the position of the current input row.
You configure a frame if you use an aggregate function as a window function. The window functions LEAD and LAG reference individual rows and ignore the frame.
Partition Keys
Separates the input rows into different partitions.
If you do not define partition keys, all rows belong to a single partition.
Order Keys
Defines how rows in a partition are ordered.
The fields you choose determine the position of a row within a partition. The order key can be ascending or descending. If you do not define order keys, the rows have no particular order.
You cannot parameterize an expression that contains a window function. If the expression is parameterized, you cannot specify a window function in the mapping task.

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