Defining Databricks connection parameters with Spark Universal - Cloud - 8.0

Talend Data Fabric Studio User Guide

Version
Cloud
8.0
Language
English (United States)
EnrichDitaval
Data Fabric
Product
Talend Data Fabric
Module
Talend Studio
Content
Design and Development

About this task

Talend Studio connects to an interactive Databricks cluster to run the Job from this cluster.

Complete the Spark Universal connection configuration with Databricks mode on either Spark 3.1.x or 3.2.x in the Spark configuration tab of the Run view of your Spark Job. This configuration is effective on a per-Job basis.

The information in this section is only for users who have subscribed to Talend Data Fabric or to any Talend product with Big Data but it is not applicable to Talend Open Studio for Big Data users.

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.
    Tip: 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, Spark 3.1.x or Spark 3.2.x from the Version drop-down list and Local from the Runtime mode/environment drop-down list.
  4. Enter the basic configuration information:
    Use local timezone Select this check box to let Spark use the local timezone provided by the system.
    Note:
    • If you clear this check box, Spark use UTC timezone.
    • Some components also have the Use local timezone for date check box. If you clear the check box from the component, it inherits timezone 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 ensure the backwards compatibility.
    Important: If your Job contains tDeltaLakeInput and tDeltaLakeOutput components, you must select this check box.
    Note: Newly created Jobs in 7.3 or later use DS and imported Jobs from 7.3 or earlier use RDD by default. However, not all the components are migrated from RDD to DS so it is recommended to clear the check box to avoid any errors by default.
    Use timestamp for dataset components Select this check box to use java.sql.Timestamp for dates.
    Note: If you leave this check box clear, java.sql.Timestamp or java.sql.Date can be used depending on the pattern.
  5. Complete the Databricks configuration parameters:
    Cloud provider Select the cloud provider to be used between AWS, Azure and GCP.
    Run mode Select the mode you want to use to run your Job on Databricks cluster when you execute your Job in Talend Studio. With Create and run now, a new Job is created and run immediately on Databricks and with Runs submit, a one-time run is submitted without creating a Job on Databricks.
    Use pool You can select this check box to leverage a Databricks pool. If you do, you must indicate the Pool ID instead of the Cluster ID. You must also select the Use transient cluster check box.
    Endpoint Enter the URL address of your workspace.
    Cluster ID Enter the ID of the Databricks cluster to be used. This ID is the value of the spark.databricks.clusterUsageTags.clusterId property of your Spark cluster. You can find this property on the properties list in the Environment tab in the Spark UI view of your cluster.
    Token Enter the authentication token generated for your Databricks user account.
    DBFS dependencies folder Enter the directory that is used to store your Job related dependencies on Databricks Filesystem at runtime, putting a slash (/) at the end of this directory. For example, enter /jars/ to store the dependencies in a folder named jars. This folder is created on the fly if it does not exist then.
    Project ID Enter the ID of your Google Platform project where the Databricks project is located.

    This field is only available when you select GCP from the Cloud provider drop-down list.

    Bucket Enter the name of the bucket you use for Databricks from Google Platform.

    This field is only available when you select GCP from the Cloud provider drop-down list.

    Workspace ID Enter the ID of your Google Platform workspace respecting the following format: databricks-workspaceid.

    This field is only available when you select GCP from the Cloud provider drop-down list.

    Google credentials Enter the directory in which the JSON file containing your service account key is stored in the Jobserver machine.

    This field is only available when you select GCP from the Cloud provider drop-down list.

    Poll interval when retrieving Job status (in ms) Enter the time interval (in milliseconds) at the end of which you want the Studio to ask Spark for the status of your Job.
    Use transient cluster Select this check box to leverage the transient Databricks clusters.
    Do not restart the cluster when submitting Select this check box to prevent the Studio restarting the cluster when the Studio is submitting your Jobs. However, if you make changes in your Jobs, clear this check box so that the Studio restarts your cluster to take these changes intot account.
  6. In the Spark "scratch" directory field, enter the directory in which the Studio stores in the local system the temporary files such as the jar files to be transferred. If you launch the Job on Windows, the default disk is C:. So if you leave /tmp in this field, this directory is C:/tmp.
  7. If you need the Job to be resilient to failure, select the Activate checkpointing check box to enable the Spark checkpointing operation. In the Checkpoint directory field, enter the directory in which Spark stores, in the file system of the cluster, the context data of the computations such as the metadata and the generated RDDs of this computation.
  8. In the Advanced properties table, add any Spark properties you need to use to override their default counterparts used by the Studio.

Results

The connection details to the Databricks cluster are complete, you are ready to schedule executions of your Job or to run it immediately from this cluster.