Assembles the outputs of the parallel execution processes so that tRecollector can capture those outputs.
Note that Talend Studio also enables the automatic implementation of parallelization
across a Job without use of the parallelization components and we recommend using that
approach. For further information, see the section describing how to enable
parallelization of data flows of the Talend Studio User Guide. However, if you
need to understand how to use these specific parallelization components, bear in mind
that the parallelization components work closely with each other to accomplish parallel
execution on given processes: the tPartitioner
component dispatches the input records into a specific number of threads; the tCollector component sends these threads to its following
components for parallel execution; the tDepartitioner
component regroups the outputs of the processed parallel threads; the tRecollector component captures the output of a given
tDepartitioner component and sends the captured
data to the next component.
This component regroups the outputs of the parallel processes
executed by its preceding components.
tDepartitioner Standard properties
These properties are used to configure tDepartitioner running in the Standard Job framework.
tDepartitioner component belongs to the Orchestration family.
Schema and Edit
A schema is a row description, it defines the number of fields to be processed and
Enter the number of rows to be processed before the memory is freed.
Select this check box to collect the log data at the component
ERROR_MESSAGE: the error message generated by the
NB_LINE: the number of rows processed. This is an After
A Flow variable functions during the execution of a component while an After variable
To fill up a field or expression with a variable, press Ctrl +
For further information about variables, see
This component is the end component of the parallel execution
This component requires tRecollector to be able to output the execution
Outgoing links (from this component to another):
Incoming links (from one component to this one):
For further information regarding connections, see
For a related scenario, see Sorting the customer data of large size in parallel.