Consider the types of transformations in the mapping and the order in which transformations appear so that you do not get unexpected results. You can partition a mapping if the

mapping

task can maintain data consistency when it processes the partitioned data.

For flat file partitioning, session performance is optimal with large source files. The load may be unbalanced if the amount of input data is small.

When a Sequence Generator transformation is in a mapping with partitioning enabled, ensure that you set up caching in the Sequence Generator transformation. Otherwise, the sequence numbers the task generates for each partition are not consecutive.

Sequence numbers generated by Normalizer and Sequence Generator transformations might not be sequential for a partitioned source, but they are unique.

When a Sorter transformation is in a mapping with partitioning enabled, the task sorts data in each partition separately.

A Sorter transformation must be placed before any Joiner transformation or Aggregator transformation that is configured to use sorted data.

You cannot use in-out parameters for key range values.

If your mapping has more than eight partitions,

mapping

task performance might degrade. You can configure the Buffer Block Size and DTM Buffer Size advanced properties in the

mapping

task to improve performance.

On Linux, if a target table name includes a unicode character, you need to set the default locale to UTF-8 to support multibyte data. To set the default locale to UTF-8, see the following examples:

For bash and related UNIX shells:

export LC_ALL=en_US.UTF-8

For csh and related UNIX shells:

setenv LC_ALL en_US.UTF-8

If you use key range partitioning on a source in a mapping in advanced mode, the mapping fails if all of the following conditions are true:

The Data Integration Server processes the source.
An
advanced cluster
processes a midstream transformation.
The
advanced cluster
runs in a Google Cloud or Microsoft Azure environment.