Table of Contents

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  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

Lookup Transformation Support on the Spark Engine

Lookup Transformation Support on the Spark Engine

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

Mapping Validation

Mapping validation fails in the following situations:
  • Case sensitivity is disabled.
  • The lookup condition in the Lookup transformation contains binary data type.
  • The cache is configured to be shared, named, persistent, dynamic, or uncached. The cache must be a static cache.
The mapping fails in the following situation:
  • The transformation is unconnected and used with a Joiner or Java transformation.

Multiple Matches

When you choose to return the first, last, or any value on multiple matches, the Lookup transformation returns any value.
If you configure the transformation to report an error on multiple matches, the Spark engine drops the duplicate rows and does not include the rows in the logs.


Updated October 23, 2019