In the read and write operations for a complex file data object, you cannot edit the precision and scale of elements within a field that is of a complex data type.

For example, if Field1 is of type array with string elements, you cannot edit the precision and scale of the string elements.

When you configure Sqoop and OraOop, and export data to an Oracle target that contains mixed case characters in the table name, the mapping fails.

Workaround: Use the generic Oracle JDBC driver to export data.

Workaround: Use the

hdp-connector-for-teradata-1.4.1.2.3.2.0-2950-distro.tar.gz

JAR.

Workaround: Before you run the mapping, create a table in the target database with columns corresponding to the connected ports.

Microsoft Azure Blob Storage incorrectly appears as a source option for the Lookup transformation on the Spark and Databricks Spark engines, but this option is not supported. Mapping validation fails with the following error when you select Microsoft Azure Blob Storage as a lookup source on the Spark or Databricks Spark engine:

[Spark Validation] The Integration Service cannot 
use the Spark engine to push the transformation 
[test_input_txt_Read1] of type [LookupTx] to 
the Hadoop cluster. [ID: TRANSFORMATION_NOT_SUPPORTED] 
test_input_txt_Read1

Workaround: Perform any of the following steps:

Workaround: If possible, increase partitioning on the source.

The number of nodes is incorrectly displayed in the session log for the mappings that run on nodes that are labeled in a cluster that runs on Blaze and Spark engines.

Workaround: Edit the Spark.JdbcNumPartition setting in the mapping Runtime Properties to reduce the number of partitions to 1. This causes all rows to be processed as a single partition.

When the Spark engine processes an input value of zero in a decimal port that is configured with equivalent precision and scale, the engine treats the value as data overflow and the return value is NULL.

The Spark engine might take up to 30 minutes to run a mapping that contains a Python transformation if you pass a large number of ports to the Python transformation.

Workaround: Edit the filter condition to remove the table name prefixed to the column name and run the mapping again. (422627)

Workaround: After you import the workflow, manually assign a Databricks or Hadoop connection to the Create Cluster task.

If you use the numberOfErrorRows system-defined mapping output in a mapping that runs on the Spark engine, the engine returns an incorrect value for the mapping output.

Workaround: Run the mapping on the Blaze engine.

When memory usage reaches the maximum container size, YARN kills the container.

Memory usage on the OOP Container Manager reaches the maximum container size if the following conditions are true:

Concurrent jobs run for longer than two days.
The Blaze engine has not reached the idle timeout limit or the sunset time.

On Oracle and DB2, when a Lookup transformation contains a Text data type column and you import the mapping into the Developer tool, the Text datatype is mapped to the Clob datatype and the mapping fails with an error. Similarly, the Binary data type gets imported into the Model repository as a Blob data type and the mapping fails.

Workaround: Edit the column type in the Lookup transformation to run the mapping.