tPartition - 6.1

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This component will be available in the Palette of Talend Studio on the condition that you have subscribed to one of the Talend solutions with Big Data.


This component splits the input dataset into a given number of partitions.


This component allows you to visually define how an input dataset is partitioned.

tPartition Properties in Spark Batch Jobs

Component family



Basic settings

Schema and Edit Schema

A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. The schema is either Built-In or stored remotely in the Repository.

Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

  • View schema: choose this option to view the schema only.

  • Change to built-in property: choose this option to change the schema to Built-in for local changes.

  • Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the [Repository Content] window.

Click Sync columns to retrieve the schema from the previous component connected in the Job.


 Number of partitions

Enter the number of partitions you want to split the input dataset up into.


Partition key

Complete this table to define the key to be used for the partitioning.

In the Partition key table, the schema columns are automatically added into the Column column and in the Partition column column, you need to select the check box(es) corresponding to the column(s) you want to use as the key of the partitioning.

This partitioning proceeds in the hash mode, that is to say, the records meeting the same criteria (the key) are dispatched into the same partition.


Use custom partitioner

Select this check box to use a Spark partitioner you need to import from outside the Studio. For example, a partitioner you have developed by yourself. In this situation, you need to give the following information:

  • Custom partitioner FQCN: enter the fully qualified class name of the partitioner to be imported.

  • Custom partitioner JAR: click the [+] button as many time as needed to add the same number of rows. In each row, click the [...] button to import the jar file containing this partitioner class and its dependent jar files.


Sort within partitions

Select this check box to sort the records within each partition.

This feature is useful when a partition contains several distinct key values.

  • Natural key order: keys are sorted in their natural order, for example, in the alphabetical order.

  • Custom comparator: this allows you to use a custom program to sort the keys.

    You need to enter the fully qualified class name of the comparator to be imported in the Custom comparator FQCN field and add the jar files to be loaded in the Custom comparator JAR table.

Usage in Spark Batch Jobs

In a Talend Spark Batch Job, this component is used as an intermediate step and other components used along with it must be Spark Batch components, too. They generate native Spark Batch code that can be executed directly in a Spark cluster.

This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs.

Spark Connection

You need to use the Spark Configuration tab in the Run view to define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.


If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at

Related scenarios

No scenario is available for the Spark Batch version of this component yet.