Table of Contents

Search

  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. Appendix C: Function Reference

Lookup Transformation in a Streaming Mapping

Lookup Transformation in a Streaming Mapping

Streaming mappings have additional processing rules that do not apply to batch mappings.

Mapping Validation

Mapping validation fails in the following situations:
  • The lookup is a data object.
  • An Aggregator transformation is in the same streaming pipeline as a passive Lookup transformation configured with an inequality lookup condition.
  • A Rank transformation is in the same streaming pipeline as a passive Lookup transformation configured with an inequality lookup condition.
  • A pipeline contains more than one passive Lookup transformation configured with an inequality condition.
The mapping fails in the following situations:
  • The transformation is unconnected.

General Guidelines

Consider the following general guidelines:
  • Using a float data type to look up data can return unexpected results.
  • Use a Lookup transformation to look up data in a flat file, HDFS, Hive, relational, JDBC V2, and HBase data.
  • To avoid cross join of DataFrames, configure the Lookup transformation to ignore null values that match.

HBase Lookups

To use a Lookup transformation on uncached HBase tables, perform the following steps:
  1. Create an HBase data object. When you add an HBase table as the resource for a HBase data object, include the ROW ID column.
  2. Create a HBase read data operation and import it into the streaming mapping.
  3. When you import the data operation to the mapping, select the
    Lookup
    option.
  4. On the Lookup tab, configure the following options:
    • Lookup column. Specify an equality condition on ROW ID
    • Operator. Specify =
  5. Verify that format for any date value in the HBase tables is of a valid Java date format. Specify this format in the
    Date Time Format
    property of the
    Advanced Properties
    tab of the data object read operation.
If an HBase lookup does not result in a match, it generates a row with null values for all columns. You can add a Filter transformation after the Lookup transformation to filter out null rows.
Mapping validation fails in the following situations:
  • The condition does not contain a ROW ID.
  • The transformation contains an inequality condition.
  • The transformation contains multiple conditions.
  • An input column is of a date type.

JDBC V2 Lookups

You can add a JDBC V2 data object read operation as a lookup in a streaming mapping. You can run streaming mappings with JDBC V2 lookup in Azure Databricks service in Microsoft Azure cloud services.


Updated November 10, 2020