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 Window Functions

Rules and Guidelines for Window Functions

Certain guidelines apply when you use window functions on the Spark engine.
Consider the following rules and guidelines when you define window functions in a transformation:
  • Specify a constant integer as the offset argument in a window function.
  • Specify a default argument that is the same data type as the input value.
  • You cannot specify a default argument that contains complex data type or a SYSTIMESTAMP argument.
  • To use the LEAD and LAG window functions, you must configure partition and order keys in the windowing properties.
  • To use an aggregate function as a window function in an Expression transformation, you must configure a frame specification in the windowing properties.

Updated October 23, 2019