Table of Contents

Search

  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Connections
  4. Mappings in the Hadoop Environment
  5. Mapping Objects in the Hadoop Environment
  6. Processing Hierarchical Data on the Spark Engine
  7. Stateful Computing on the Spark Engine
  8. Monitoring Mappings in the Hadoop Environment
  9. Mappings in the Native Environment
  10. Profiles
  11. Native Environment Optimization
  12. Data Type Reference
  13. Complex File Data Object Properties
  14. Function Reference
  15. Parameter Reference

Partition and Order Keys

Partition and Order Keys

Configure partition and order keys to form groups of rows and define the order or sequence of rows within each partition.
Use the following keys to specify how to group and order the rows in a window:
Partition keys
Configure partition keys to define partition boundaries, rather than performing the calculation across all inputs. The window function operates across the rows that fall into the same partition as the current row.
You can specify the partition keys by value or parameter. Select
Value
to use port names. Choose
Parameter
to use a sort key list parameter. A sort key list parameter contains a list of ports to sort by. If you do not specify partition keys, all the data is included in the same partition.
Order keys
Use order keys to determine how rows in a partition are ordered. Order keys define the position of a particular row in a partition.
You can specify the order keys by value or parameter. Select
Value
to use port names. Choose
Parameter
to use a sort key list parameter. A sort key list parameter contains a list of ports to sort by. You must also choose to arrange the data in ascending or descending order. If you do not specify order keys, the rows in a partition are not arranged in any particular order.

Example

You are the owner of a coffee and tea shop. You want to calculate the best-selling and second best-selling coffee and tea products.
The following table lists the products, the corresponding product categories, and the revenue from each product:
Product
Category
Revenue
Espresso
Coffee
600
Black
Tea
550
Cappuccino
Coffee
500
Americano
Coffee
600
Oolong
Tea
250
Macchiato
Coffee
300
Green
Tea
450
White
Tea
650
You partition the data by category and order the data by descending revenue.
The following image shows the properties you configure on the Windowing tab:
The order key is ascending revenue. The partition key is category.
The following table shows the data grouped into two partitions according to category. Within each partition, the revenue is organized in descending order:
Product
Category
Revenue
Espresso
Coffee
600
Americano
Coffee
600
Cappuccino
Coffee
500
Macchiato
Coffee
300
White
Tea
650
Black
Tea
550
Green
Tea
450
Oolong
Tea
250
Based on the partitioning and ordering specifications, you determine that the two best-selling coffees are espresso and Americano, and the two best-selling teas are white and black.


Updated November 09, 2018