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 Targets

Hive Targets

A mapping that is running in the Hadoop environment can write to a Hive target.
The Spark engine can write to bucketed Hive targets. Bucketing and partitioning of Hive tables can improve performance by reducing data shuffling and sorting.
Consider the following restrictions when you configure a Hive target in a mapping that runs in the Hadoop environment:
  • The Data Integration Service does not run pre-mapping or post-mapping SQL commands against a Hive target. You cannot validate and run a mapping with PreSQL or PostSQL properties for a Hive target.
  • A mapping fails to run if the Hive target definition differs in the number and order of the columns from the relational table in the Hive database.
  • A mapping fails to run when you use Unicode characters in a Hive target definition.
  • You must truncate the target table to overwrite data to a Hive table with Hive version 0.7. The Data Integration Service ignores write, update override, delete, insert, and update strategy properties when it writes data to a Hive target.
  • When you set up a dynamic target for a partitioned Hive table, the value used for the partition is the final column in the table. If the table has a dynamic partition column, the final column of the table is the dynamic partition column. To use a different column for the partition, move it to the last column of the table. If the table has multiple partition columns, the dynamic partition values are selected from the last columns of the upstream transformation.
  • The Data Integration Service can truncate the partition in the Hive target in which the data is being inserted. You must choose to both truncate the partition in the Hive target and truncate the target table.
In a mapping that runs on the Spark engine or the Blaze engine, you can create a custom DDL query that creates or replaces a Hive table at run time. However, with the Blaze engine, you cannot use a backtick (`) character in the DDL query. The backtick character is required in HiveQL when you include special characters or keywords in a query.
When a mapping creates or replaces a Hive table, the type of table that the mapping creates depends on the run-time engine that you use to run the mapping.
The following table shows the table type for each run-time engine:
Run-Time Engine
Resulting Table Type
Blaze
MANAGED_TABLE
Spark
EXTERNAL_TABLE
Hive
MANAGED_TABLE


Updated November 09, 2018