ParallelRepartition component re-distributes already partitioned data according new rules among a different set of CloverDX Cluster workers.

Component	Same input metadata	Sorted inputs	Inputs	Outputs	Java	CTL	Auto-propagated metadata
ParallelRepartition			1[1]	1[2]	[3]	[3]
[1]  The single input port represents multiple virtual input ports. [2]  The single output port represents multiple virtual output ports. [3]  ParallelRepartition can use either a transformation or two other attributes (Ranges and/or Partition key). A transformation must be defined unless at least one of the attributes is specified.

ParallelRepartition propagates metadata in both directions. The component does not change priorities of metadata.

ParallelRepartition has no metadata templates.

The component does not require any specific metadata fields.

ParallelRepartition has same attributes as ParallelPartition. See ParallelPartition Attributes.

ParallelRepartition component re-distributes already partitioned data according to new rules among different set of CloverDX Cluster workers.

This component is functionally analogous of the ParallelPartition component, it distributes incoming data records among different CloverDX Cluster workers. Unlike ParallelPartition, the incoming data can be already partitioned.

For more details about this component, consider following usage of the repartitioner:

Figure 60.1. Usage example of ParallelRepartition component

The ParallelRepartition component defines a boundary between two incompatible allocations. Data in front of ParallelRepartition is already partitioned on node1 and node2, let's say according to a key A. The component allows changing an allocation (even cardinality), in our case the allocation behind the repartitioner is node1, node2 and node3, according to a new key B. All is done in one step. Let's look at the following image, which shows how the repartitioner works.

Figure 60.2. Example of actual working of ParallelRepartition component in runtime

Three separate graphs are executed, one on each of three nodes - node1, node2 and node3. The ParallelRepartition component is substituted by one Partition component for each source partition and by one SimpleGather component for each target partition. So altogether, five components do the work instead of the ParallelRepartition. Each Partition splits the data from single input partition to all output partitions where the data is gathered by the SimpleGather component.

	Note
	For more information about this component, see Chapter 41, Data Partitioning in Cluster.