tRunJob properties for Apache Spark Batch - 7.3

Orchestration (Integration)

Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Real-Time Big Data Platform
Talend Studio
Data Governance > Third-party systems > Orchestration components (Integration)
Data Quality and Preparation > Third-party systems > Orchestration components (Integration)
Design and Development > Third-party systems > Orchestration components (Integration)
Last publication date

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

The Spark Batch tRunJob component belongs to the System family.

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

Basic settings

Use dynamic job

Select this check box to allow multiple Jobs to be called and processed. When this option is enabled, only the latest version of the Jobs can be called and processed. An independent process will be used to run the subJob. The Context and the Use an independent process to run subJob options disappear.

Warning: The Use dynamic job option is not compatible with the Jobserver cache. Therefore, the execution may fail if you run a Job that contains tRunjob with this check box selected in Talend Administration Center.

Context job

This field is visible only when the Use dynamic job option is selected. Enter the name of the Job that you want to call from the list of Jobs selected.


Select the Job to be called in and processed. Make sure you already executed once the Job called, beforehand, in order to ensure a smooth run through tRunJob.

It is not recommended to call a Standard Job from a Spark Batch Job as it causes many issues.


Select the child Job version that you want to use.


If you defined contexts and variables for the Job to be run by the tRunJob, select the applicable context entry on the list.

Die on child error

Clear this check box to execute the parent Job even though there is an error when executing the child Job.

Transmit whole context

Select this check box to get all the context variables from the parent Job. Deselect it to get all the context variables from the child Job.

If this check box is selected when the parent and child Jobs have the same context variables defined:
  • variable values for the parent Job will be used during the child Job execution if no relevant values are defined in the Context Param table.

  • otherwise, values defined in the Context Param table will be used during the child Job execution.

Context Param

You can change the value of selected context parameters. Click the [+] button to add the parameters defined in the Context tab of the child Job. For more information on context parameters, see Talend Studio User Guide.

The values defined here will be used during the child Job execution even if Transmit whole context is selected.

Advanced settings

Print Parameters

Select this check box to display the internal and external parameters in the Console.


Usage rule

This component is used with no need to be connected to other components.

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.