Table of Contents

Search

  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. B2B transformation
  7. Aggregator transformation
  8. Cleanse transformation
  9. Data Masking transformation
  10. Data Services transformation
  11. Deduplicate transformation
  12. Expression transformation
  13. Filter transformation
  14. Hierarchy Builder transformation
  15. Hierarchy Parser transformation
  16. Hierarchy Processor transformation
  17. Input transformation
  18. Java transformation
  19. Java transformation API reference
  20. Joiner transformation
  21. Labeler transformation
  22. Lookup transformation
  23. Machine Learning transformation
  24. Mapplet transformation
  25. Normalizer transformation
  26. Output transformation
  27. Parse transformation
  28. Python transformation
  29. Rank transformation
  30. Router transformation
  31. Rule Specification transformation
  32. Sequence Generator transformation
  33. Sorter transformation
  34. SQL transformation
  35. Structure Parser transformation
  36. Transaction Control transformation
  37. Union transformation
  38. Velocity transformation
  39. Verifier transformation
  40. Web Services transformation

Transformations

Transformations

Defining rank groups

Defining rank groups

You can configure the Rank transformation to define groups for ranked rows. For example, to select the 10 most expensive items by manufacturer, define a rank group for the manufacturer. Define rank groups on the
Group By
tab.
To define rank groups, select one or more incoming fields as
Group By Fields
. For each unique value in a rank group, the transformation creates a group of rows that fall within the rank definition (top or bottom, and number in each rank).
Define rank groups to improve performance in a mapping in advanced mode that processes a large volume of data. When you define rank groups, processing is distributed across multiple worker nodes. If you do not define rank groups, the data is processed on one worker node. Depending on the volume of data, performance is impacted and the mapping might fail due to a lack of storage space on the EBS volume that is attached to the worker node.
For example, you create a Rank transformation that ranks the top five salespersons grouped by quarter. The rank index numbers the salespeople from 1 to 5 for each quarter as follows:
RANKINDEX
SALES_PERSON
SALES
QUARTER
1
Alexandra B.
10000
1
2
Boris M.
9000
1
3
Chanchal R.
8000
1
4
Dong T.
7000
1
5
Elias M.
6000
1
1
Elias M.
11000
2
2
Boris M.
10000
2
3
Alexandra B.
9050
2
4
Dong T.
7500
2
5
Frances Z.
6900
2
If you define multiple rank groups, the Rank transformation groups the ranked rows in the order in which the fields are selected in the
Group By Fields
list.

0 COMMENTS

We’d like to hear from you!