Table of Contents


  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings in the Hadoop Environment
  4. Mapping Sources in the Hadoop Environment
  5. Mapping Targets in the Hadoop Environment
  6. Mapping Transformations in the Hadoop Environment
  7. Processing Hierarchical Data on the Spark Engine
  8. Configuring Transformations to Process Hierarchical Data
  9. Processing Unstructured and Semi-structured Data with an Intelligent Structure Model
  10. Stateful Computing on the Spark Engine
  11. Monitoring Mappings in the Hadoop Environment
  12. Mappings in the Native Environment
  13. Profiles
  14. Native Environment Optimization
  15. Cluster Workflows
  16. Connections
  17. Data Type Reference
  18. Function Reference
  19. Parameter Reference

Aggregate Offsets

Aggregate Offsets

An aggregate function performs a calculation on a set of values inside a partition. If the frame offsets are outside the partition, the aggregate function ignores the frame.
If the offsets of a frame are not within the partition or table, the aggregate function calculates within the partition. The function does not return NULL or a default value.
For example, you partition a table by seller ID and you order by quantity. You set the start offset to -3 and the end offset to 4.
The following image shows the partition and frame for the current input row:
The partition includes the current input row plus one row before the current row and two rows after the current row. The frame extends to three rows before the current row and four rows after the current row.
The frame includes eight total rows, but the calculation remains within the partition. If you define an AVG function with this frame, the function calculates the average of the quantities inside the partition and returns 18.75.

Updated October 23, 2019