If you use PowerCenter, you can increase the number of partitions in a pipeline to improve session performance. Increasing the number of partitions allows the Informatica Server to create multiple connections to sources and process partitions of source data concurrently.

When you create a session, the Workflow Manager validates each pipeline in the mapping for partitioning. You can specify multiple partitions in a pipeline if the Informatica Server can maintain data consistency when it processes the partitioned data.

When you configure the partitioning information for a pipeline, you must specify a partition type at each partition point in the pipeline.

The partition type determines how the Informatica Server redistributes data across partition points.

The Workflow Manager allows you to specify the following partition types:

Round-robin partitioning. The Informatica Server distributes data evenly among all partitions. Use round-robin partitioning where you want each partition to process approximately the same number of rows.

For more information, see Round-Robin Partitioning.

Hash partitioning. The Informatica Server applies a hash function to a partition key to group data among partitions. If you select hash auto-keys, the Informatica Server uses all grouped or sorted ports as the partition key. If you select hash user keys, you specify a number of ports to form the partition key. Use hash partitioning where you want to ensure that the Informatica Server processes groups of rows

with the same partition key in the same partition. For more

information, see Hash Partitioning.

Key range partitioning. You specify one or more ports to form a compound partition key. The Informatica Server passes data to each partition depending on the ranges you specify for each port. Use key range partitioning where the sources or targets in the pipeline are partitioned by key range. For more information, see Key Range Partitioning.

Pass-through partitioning. The Informatica Server passes all rows at one partition point to the next partition point without redistributing them. Choose pass-through partitioning where you want to create an additional pipeline stage to improve performance, but do not want to change the distribution of data across partitions.