When

Data Integration

processes a sorted Joiner transformation, it caches rows for one hundred unique keys at a time. If the master group contains many rows with the same key value,

Data Integration

must cache more rows, and performance can be slowed.

The Joiner transformation compares each row of the detail group against the master group. The fewer rows in the master, the fewer iterations of the join comparison occur, which speeds the join process.

Performing a join in a database is faster than performing a join in the mapping. The type of database join you use can affect performance. Normal joins are faster than outer joins and result in fewer rows. In some cases, you cannot perform the join in the database, such as joining tables from two different databases or flat file systems.

To improve mapping performance, configure the Joiner transformation to use sorted input. When you configure the Joiner transformation to use sorted data,

Data Integration

improves performance by minimizing disk input and output. You see the greatest performance improvement when you work with large data sets. For an unsorted Joiner transformation, designate the source with fewer rows as the master group.

In a mapping that has multiple Joiner transformations, join the largest data set in the most downstream transformation.