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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 Schema

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 fields.

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.

Repartition by range

Select this check box to divide the dataset into multiple partitions. For more information, see the official Spark documentation.

This option is only available in Dataset mode.

Information noteNote: tPartition component supports Spark Dataset API only when:
  • The Job uses with SparkV2 and onwards.
  • The Use Dataset API check box from the Spark configuration view of your Job is selected.
  • The Job do not use custom partitioner nor custom comparator.

Use coalesce

Select this check box to reduce the number of partitions that are returned. For more information, see the official Spark documentation.

This option is only available in Dataset mode.

Information noteNote: tPartition component supports Spark Dataset API only when:
  • The Job uses with SparkV2 and onwards.
  • The Use Dataset API check box from the Spark configuration view of your Job is selected.
  • The Job do not use custom partitioner nor custom comparator.

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

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 Talend data integration Jobs.

Spark Connection

In the Spark Configuration 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 tab.

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

    • When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
    • 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-premises 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 Apache Spark Batch or tS3Configuration Apache Spark Batch.

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

This connection is effective on a per-Job basis.

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