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Defining Spark-submit scripts connection parameters with Spark Universal

The Spark-submit scripts mode allows you to leverage a HPE Ezmeral Data Fabric v9.1.x cluster to run your Spark Batch Jobs.

For more information about HPE Data Fabric, see the documentation.

You can also use this mode with other clusters than HPE Data Fabric. This is because a Spark-submit script is designed to work with all of Spark’s supported cluster managers, as documented in cluster managers from the Spark documentation.

Procedure

  1. Click the Run view beneath the design workspace, then click the Spark configuration view.
  2. Select Built-in from the Property type drop-down list.
    If you have already set up the connection parameters in the Repository as explained in Centralizing a Hadoop connection, you can easily reuse it. To do this, select Repository from the Property type drop-down list, then click […] button to open the Repository Content dialog box and select the Hadoop connection to be used.
    Information noteTip: Setting up the connection in the Repository allows you to avoid configuring that connection each time you need it in the Spark configuration view of your Jobs. The fields are automatically filled.
  3. Select Universal from the Distribution drop-down list, the Spark version from the Version drop-down list, and Spark-submit scripts from the Runtime mode/environment drop-down list.
  4. Specify the path to the directory on the cluster where spark-submit script is stored, for example, /opt/mapr/spark/spark-3.3.2.
  5. If you run your Spark Job on Windows, specify the location of the winutils.exe program:
    • If you want to use your own winutils.exe file, select the Define the Hadoop home directory check box and enter its folder path.
    • Otherwise, leave the Define the Hadoop home directory check box clear. Talend Studio will generate and use a directory automatically for this Job.
  6. Enter the basic configuration information:
    Parameter Usage
    Use local timezone Select this check box to let Spark use the local time zone provided by the system.
    Information noteNote:
    • If you clear this check box, Spark use UTC time zone.
    • Some components also have the Use local timezone for date check box. If you clear the check box from the component, it inherits time zone from the Spark configuration.
    Use dataset API in migrated components Select this check box to let the components use Dataset (DS) API instead of Resilient Distributed Dataset (RDD) API:
    • If you select the check box, the components inside the Job run with DS which improves performance.
    • If you clear the check box, the components inside the Job run with RDD which means the Job remains unchanged. This ensures the backwards compatibility.

    This check box is selected by default, but if you import a Job from 7.3 backwards, the check box will be cleared as those Jobs run with RDD.

    Information noteImportant: If your Job contains tDeltaLakeInput and tDeltaLakeOutput components, you must select this check box.
    Use timestamp for dataset components Select this check box to use java.sql.Timestamp for dates.
    Information noteNote: If you leave this check box clear, java.sql.Timestamp or java.sql.Date can be used depending on the pattern.
    Batch size (ms) Enter the time interval at the end of which the Spark Streaming Job reviews the source data to identify changes and processes the new micro batches.
    Define a streaming timeout (ms) Select this check box and in the field that is displayed, enter the time frame at the end of which the Spark Streaming Job automatically stops running.
    Information noteNote: If you are using Windows 10, it is recommended to set up a reasonable timeout to avoid Windows Service Wrapper to have issue when sending signal termination from Java applications. If you are facing such issue, you can also manually cancel the Job from your Azure Synapse workspace.
    Parallelize output files writing Select this checkbox to enable the Spark Batch Job to run multiple threads in parallel when writing output files. This option improves the performance of the execution time.

    When you leave this checkbox cleared, the output files are written sequentially in one thread.

    On subJobs level, each subJob is treated sequentially. Only the output file inside the subJob is parallelized.

    This option is only available for Spark Batch Jobs containing the following output components:
    • tAvroOutput
    • tFileOutputDelimited (only when the Use dataset API in migrated components checkbox is selected)
    • tFileOutputParquet
    Information noteImportant: To avoid memory problems during the execution of the Job, you need to take into account the size of the files being written and the execution environment capacity before using this parameter.
  7. Enter the authentication information by specifying your username. You can also use Kerberos to authenticate by selecting the Use Kerberos authentication checkbox.
  8. Optional: Select the Use MapR Ticket authentication checkbox to authenticate using MapR Ticket, and enter the following information:
    Parameter Usage
    Password Enter your password to authenticate to MapR Ticket.
    Cluster name Enter the name of the cluster you want to use.
    Ticket duration Enter the duration of the ticket in seconds. For example: 86400L, where L corresponds to a long integer.
    Set the MapR home directory Select this checkbox, and then enter the MapR home directory path.
    Specify the Hadoop login configuration Select this checkbox, and then enter the Hadoop login.
  9. Select the Set tuning properties check box to define the tuning parameters, by following the process explained in Tuning Spark for Apache Spark Batch Jobs.
    Information noteImportant: You must define the tuning parameters otherwise you can get an error (400 - Bad request).
  10. In the Spark "scratch" directory field, enter the local path where Talend Studio stores temporary files, like JARs to transfer.
    If you run the Job on Windows, the default disk is C:. Leaving /tmp in this field will use C:/tmp as the directory.
  11. To make your Job resilient to failure, select Activate checkpointing to enable Spark’s checkpointing operation.
    In the Checkpoint directory field, enter the cluster file system path where Spark saves context data, such as metadata and generated RDDs.
  12. In the Advanced properties table, add any Spark properties you want to override the defaults set by Talend Studio.
  13. Select the Use Atlas check box to trace data lineage, view Spark Job components, and track schema changes between components.
    This option is only available for Spark Universal 3.3.x.

    With this option activated, you need to set the following parameters:

    • Atlas URL: Enter the address of your Atlas instance, such as http://name_of_your_atlas_node:port.

    • In the Username and Password fields, enter the authentication information for access to Atlas.

    • Set Atlas configuration folder: Select this check box if your Atlas cluster uses custom properties like SSL or read timeout. In the field that appears, enter the path to a local directory containing your atlas-application.properties file. Your Job will then use these custom properties.

      Ask the administrator of your cluster for this configuration file. For more information, see the Client Configs section in Atlas configuration.

    • Die on error: Select this check box to stop Job execution if Atlas-related issues occur, such as connection errors. Clear it to let your Job continue running.

Results

The connection details are complete, you are ready to schedule executions of your Spark Job or to run it immediately.

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