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.

The transformation might demonstrate the following differences in behavior:

Mapping validation fails in the following situations:

Mapping validation fails in the following situations:

The transformation contains more than one input port.
The transformation contains pass-through ports.

An Expression transformation with a user-defined function returns a null value for rows that have an exception error in the function.

You must copy external .jar files that a Java transformation requires to the Informatica installation directory in the Hadoop cluster nodes at the following location:

[$HADOOP_NODE_INFA_HOME]/services/shared/jars

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.

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.

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.

Mapping validation fails in the following situations:

Case sensitivity is disabled.

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.

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.
The target is ORC bucketed on all columns.

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.

The Hive engine performs Update as Update even if the transformation is configured to Update as Insert or Update else Insert.

When the Update Strategy transformation receives multiple update rows for the same key, the results might differ.