July 30, 2023

tPartition – Docs for ESB 7.x


Allows you to visually define how an input dataset is partitioned.

The tPartition splits the input dataset
into a given number of partitions.

tPartition properties for Apache Spark Batch

These properties are used to configure tPartition running in the Spark Batch Job framework.

The Spark Batch
tPartition component belongs to the Processing family.

The component in this framework is available in all Talend products with Big Data
and in Talend Data Fabric.

Basic settings

Schema and Edit

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the

Click Edit
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

Click Sync
to retrieve the schema from the previous component connected in the

 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

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


Usage rule

This component is used as an intermediate step.

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
data integration Jobs.

Spark Connection

In the Spark
tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

  • Yarn mode (Yarn client or Yarn cluster):

    • When using Google Dataproc, specify a bucket in the
      Google Storage staging bucket
      field in the Spark configuration

    • When using HDInsight, specify the blob to be used for Job
      deployment in the Windows Azure Storage
      area in the Spark

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
    • When using Qubole, add a
      tS3Configuration to your Job to write
      your actual business data in the S3 system with Qubole. Without
      tS3Configuration, this business data is
      written in the Qubole HDFS system and destroyed once you shut
      down your cluster.
    • When using on-premise
      distributions, use the configuration component corresponding
      to the file system your cluster is using. Typically, this
      system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the
    configuration component corresponding to the file system your cluster is
    using, such as tHDFSConfiguration or

    If you are using Databricks without any configuration component present
    in your Job, your business data is written directly in DBFS (Databricks

This connection is effective on a per-Job basis.

Related scenarios

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

Document get from Talend https://help.talend.com
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