Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Connections
  4. Mappings in the Hadoop Environment
  5. Mapping Objects in the Hadoop Environment
  6. Processing Hierarchical Data on the Spark Engine
  7. Stateful Computing on the Spark Engine
  8. Monitoring Mappings in the Hadoop Environment
  9. Mappings in the Native Environment
  10. Profiles
  11. Native Environment Optimization
  12. Data Type Reference
  13. Complex File Data Object Properties
  14. Function Reference
  15. Parameter Reference

Hive Sources

Hive Sources

You can include Hive sources in an Informatica mapping that runs in the Hadoop environment.
Consider the following limitations when you configure a Hive source in a mapping that runs in the Hadoop environment:
  • The Data Integration Service can run pre-mapping SQL commands against the source database before it reads from a Hive source. When you create a SQL override on a Hive source, you must enclose keywords or special characters in backtick (`) characters.
  • When you run a mapping with a Hive source in the Hadoop environment, references to a local path in pre-mapping SQL commands are relative to the Data Integration Service node. When you run a mapping with a Hive source in the native environment, references to local path in pre-mapping SQL commands are relative to the Hive server node.
  • A mapping fails to validate when you configure post-mapping SQL commands. The Data Integration Service does not run post-mapping SQL commands against a Hive source.
  • A mapping fails to run when you have Unicode characters in a Hive source definition.
  • The third-party Hive JDBC driver does not return the correct precision and scale values for the Decimal data type. As a result, when you import Hive tables with a Decimal data type into the Developer tool, the Decimal data type precision is set to 38 and the scale is set to 0. Consider the following configuration rules and guidelines based on the version of Hive:
    • Hive 0.11. Accept the default precision and scale for the Decimal data type in the Developer tool.
    • Hive 0.12. Accept the default precision and scale for the Decimal data type in the Developer tool.
    • Hive 0.12 with Cloudera CDH 5.0. You can configure the precision and scale fields for source columns with the Decimal data type in the Developer tool.
    • Hive 0.13 and above. You can configure the precision and scale fields for source columns with the Decimal data type in the Developer tool.
    • Hive 0.14 or above. The precision and scale used for the Decimal data type in the Hive database also appears in the Developer tool.
A mapping that runs on the Spark engine can have partitioned Hive source tables and bucketed sources.


Updated December 13, 2018