Table of Contents

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

Transformations

Transformations

Sorted data

Sorted data

To improve job performance, you can configure an Aggregator transformation to use sorted data. To configure the Aggregator transformation to process sorted data, on the
Advanced
tab, select
Sorted Input
.
When you configure an Aggregator transformation to use sorted data, you must sort data earlier in the data flow. If the Aggregator transformation processes data from a relational database, you must also ensure that the sort keys in the source are unique. If the data is not presorted correctly or the sort keys are not unique, you can receive unexpected results or errors when you run the mapping task.
When the
mapping
task performs aggregate calculations on sorted data, the task caches sequential rows of the same group. When the task reads data for different group, it performs aggregate calculations for the cached group, and then continues with the next group.
For example, an Aggregator transformation has the STORE_ID and ITEM group by fields, with the sorted input option selected. When you pass the following data through the Aggregator, the
mapping
task performs an aggregation for the three rows in the 101/battery group as soon as it finds the new group, 201/battery:
STORE_ID
ITEM
QTY
PRICE
101
'battery'
3
2.99
101
'battery'
1
3.19
101
'battery'
2
2.59
201
'battery'
4
1.59
201
'battery'
1
1.99
When you do not use sorted data, the
mapping
task performs aggregate calculations after it reads all data.