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

Aggregator Transformation Support on the Hive Engine

Aggregator Transformation Support on the Hive Engine

Some processing rules for the Hive engine differ from the processing rules for the Data Integration Service.

Mapping Validation

Mapping validation fails in the following situations:
  • The transformation contains stateful variable ports.
  • The transformation contains unsupported functions in an expression.

Aggregate Functions

If you use a port in an expression in the Aggregator transformation but you do not use the port within an aggregate function, the Hive engine might use any row in the port to process the expression.
The row that the Hive engine uses might not be the last row in the port. Hadoop execution is distributed, and thus the Hive engine might not be able to determine the last row in the port.

Data Cache Optimization

You cannot optimize the data cache for the transformation to store data using variable length.