Sorter Transformation in Informatica IICS – IDMC

Sorter Transformation is Passive and Connected Transformation in Informatica cloud IICS – IDMC.

Usage:
Sorter Transformation is used to sort the data in Ascending or descending order based on one or more specified columns. We can Sort Numerical, String and also Date columns. Sorter Transformation is also used to eliminate duplicates from source data.

When you create a Sorter transformation, specify fields as sort conditions and configure each sort field to sort in ascending or descending order as shown in the below.

When you specify multiple sort conditions, the mapping task sorts each condition sequentially. The mapping task treats each successive sort condition as a secondary sort of the previous sort condition. You can configure the order of sort conditions as shown in below.

In the above screenshot, First OrderId column will sort in descending order, then for each repetitive OrderID , ItemID will sort in descending order.

Properties of Sorter Transformation:

The following image shows the advanced properties for the Sorter transformation. When you check distinct option, Sorter transformation will remove the duplicates from the source data.

Sorter Cache:

• ▹ Sorter Transformation is having only cache called Sorter Cache.
• ▹ The mapping task passes all incoming data into the Sorter transformation before it performs the sort operation.
• ▹ The mapping task uses cache memory to process Sorter transformations.
• ▹ If the mapping task cannot allocate enough memory, the mapping fails.

Note:
▹ To improve the performance of Sorter, use filter before sorter to avoid un-necessary sorting.
▹ You can use the Sorter transformation to increase performance with other transformations. For example, you can sort data that passes through a Lookup or an Aggregator transformation configured to use sorted incoming fields.

Example of Sorter Mapping:

The below mapping task reads the orders data from the source, sorts the orders, totals the prices, and then writes the data to the target. The following image shows the mapping of the data flow: