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

Transformation Support on the Hive Engine

Transformation Support on the Hive Engine

Some restrictions and guidelines apply to processing transformations on the Hive engine.
The following table describes rules and guidelines for processing transformations on the Hive engine.
Transformation
Rules and Guidelines
Transformations not listed in this table are not supported.
Address Validator
The Address Validator transformation cannot generate a certification report.
Aggregator
Mapping validation fails in the following situations:
  • The transformation contains stateful variable ports.
  • The transformation contains unsupported functions in an expression.
When a mapping contains an Aggregator transformation with an input/output port that is not a group by port, the transformation might not return the last row of each group with the result of the aggregation. Hadoop execution is distributed, and thus it might not be able to determine the actual last row of each group.
Case Converter
Supported without restrictions.
Comparison
Supported without restrictions.
Consolidation
The Consolidation transformation might process data differently in the native environment and in a Hadoop environment.
The transformation might demonstrate the following differences in behavior:
  • The transformation might process records in a different order in each environment.
  • The transformation might identify a different record as the survivor in each environment.
Data Masking
Mapping validation fails in the following situations:
  • The transformation is configured for repeatable expression masking.
  • The transformation is configured for unique repeatable substitution masking.
Data Processor
Mapping validation fails in the following situations:
  • The transformation contains more than one input port.
  • The transformation contains pass-through ports.
Decision
Supported without restrictions.
Expression
Mapping validation fails in the following situations:
  • The transformation contains stateful variable ports.
  • The transformation contains unsupported functions in an expression.
An Expression transformation with a user-defined function returns a null value for rows that have an exception error in the function.
Filter
Supported without restrictions.
Java
To use external .jar files in a Java transformation, perform the following steps:
  1. Copy external .jar files to the Informatica installation directory in the Data Integration Service machine at the following location:
    <Informatic installation directory>/services/shared/jars
    . Then recycle the Data Integration Service.
  2. On the machine that hosts the Developer tool where you develop and run the mapping that contains the Java transformation:
    1. Copy external .jar files to a directory on the local machine.
    2. Edit the Java transformation to include an import statement pointing to the local .jar files.
    3. Update the classpath in the Java transformation.
    4. Compile the transformation.
You can optimize the transformation for faster processing when you enable an input port as a partition key and sort key. The data is partitioned across the reducer tasks and the output is partially sorted.
The following restrictions apply to the Transformation Scope property:
  • The value Transaction for transformation scope is not valid.
  • If transformation scope is set to Row, a Java transformation is run by mapper script.
  • If you enable an input port for partition Key, the transformation scope is set to All Input. When the transformation scope is set to All Input, a Java transformation is run by the reducer script and you must set at least one input field as a group-by field for the reducer key.
You can enable the Stateless advanced property when you run mappings in a Hadoop environment.
The Java code in the transformation cannot write output to standard output when you push transformation logic to Hadoop. The Java code can write output to standard error which appears in the log files.
Joiner
Mapping validation fails in the following situations:
  • The transformation contains an inequality join.
Labeler
Supported without restrictions.
Lookup
Mapping validation fails in the following situations:
  • The cache is configured to be shared, named, persistent, dynamic, or uncached. The cache must be a static cache.
  • The lookup is a relational Hive data source.
Mappings fail in the following situations:
  • The lookup is unconnected.
If you add a data object that uses Sqoop as a Lookup transformation in a mapping, the Data Integration Service does not run the mapping through Sqoop. It runs the mapping through JDBC.
When you a run mapping that contains a Lookup transformation, the Data Integration Service creates lookup cache .jar files. Hive copies the lookup cache .jar files to the following temporary directory:
/tmp/<user_name>/hive_resources
. The Hive parameter
hive.downloaded.resources.dir
determines the location of the temporary directory. You can delete the lookup cache .jar files specified in the LDTM log after the mapping completes to retrieve disk space.
Match
Mapping validation fails in the following situations:
  • The transformation specifies an identity match type.
A Match transformation generates cluster ID values differently in native and Hadoop environments. In a Hadoop environment, the transformation appends a group ID value to the cluster ID.
Merge
Supported without restrictions.
Normalizer
Supported without restrictions.
Parser
Supported without restrictions.
Rank
Mapping validation fails in the following situations:
  • Case sensitivity is disabled.
Router
Supported without restrictions.
Sorter
Mapping validation fails in the following situations:
  • Case sensitivity is disabled.
The Data Integration Service logs a warning and ignores the Sorter transformation in the following situations:
  • There is a type mismatch between the Sorter transformation and the target.
  • The transformation contains sort keys that are not connected to the target.
  • The Write transformation is not configured to maintain row order.
  • The transformation is not directly upstream from the Write transformation.
The Data Integration Service treats null values as high, even if you configure the transformation to treat null values as low.
Standardizer
Supported without restrictions.
Union
Supported without restrictions.
Update Strategy
The Update Strategy transformation is supported only on Hadoop distributions that support Hive ACID.
Mapping validation fails in the following situations:
  • The transformation is connected to more than one target.
  • The transformation is not connected directly to the target.
The mapping fails in the following situations:
  • The target is not ORC bucketed.
Compile validation errors occur and the mapping execution stops in the following situations:
  • The target is not a Hive target on the same cluster.
  • The Hive version is earlier than 0.14.
  • A primary key is not configured.
Hive targets always perform Update as Update operations. Hive targets do not support Update Else Insert or Update as Insert.
When the Update Strategy transformation receives multiple update rows for the same key, the results might differ.
Weighted Average
Supported without restrictions.


Updated November 09, 2018