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

Rules and Guidelines for Windowing Configuration

Rules and Guidelines for Windowing Configuration

Certain guidelines apply when you configure a transformation for windowing.
Consider the following rules and guidelines when you define windowing properties for a window function:
  • When you configure a frame, the start offset must be less than or equal to the end offset. Otherwise, the frame is not valid.
  • Configure a frame specification if you use an aggregate function as a window function. LEAD and LAG operate based on the offset value and ignore the frame specification.
  • You cannot use complex ports as partition or order keys.
  • You cannot preview the data in a transformation configured for windowing.
  • Assign unique port names to partition and order keys to avoid run-time errors.
  • The partition and order keys cannot use both a dynamic port and one or more generated ports of the same dynamic port. You must select either the dynamic port or the generated ports.

Updated October 23, 2019