Defining Cloudera connection parameters with Spark Universal

About this task

Talend Studio connects to a Cloudera cluster to run the Job from this cluster.

Procedure

Click the Run view beneath the design workspace, then click the Spark configuration view.
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.
Select Universal from the Distribution drop-down list, the Spark version from the Version drop-down list, and Cloudera from the Runtime mode/environment drop-down list.
Cloudera mode supports only Spark 3.x version.
Specify the path to the Hadoop configuration JAR file that provides the connection parameters of the Cloudera cluster you want to use. The JAR file contains all the necessary information to establish the connection with all the *-site.xml files of the cluster.

Spark Configuration view opened with the path to custom Hadoop

configuration JAR file highlighted.
The JAR file must include the following XML files:
- hdfs-site.xml
- core-site.xml
- yarn-site.xml
- mapred-site.xml
If you use Hive or HBase components, the JAR file must include in addition the following XML files accordingly:
- hive-site.xml
- hbase-site.xml
If you need to launch your Spark Job from Windows, specify where the winutils.exe program to be used is stored:
- If you know where to find your winutils.exe file and you want to use it, select the Define the Hadoop home directory check box and enter the directory where your winutils.exe is stored.
- Otherwise, leave the Define the Hadoop home directory check box clear, Talend Studio generates one by itself and automatically uses it for this Job.

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.
Use custom classpath	Select this check box to specify a custom classpath, allowing your Job to use additional or alternative Java libraries during execution or compilation.
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 within 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.

If you are accessing a Hadoop distribution running with Kerberos security, select the Use kerberos authentication check box.
To use a keytab file to log in, enter the principal to be used in the Principal field and in the Keytab field, browse to the keytab file to be used.
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).
Select the Enable spark event logging check box to enable the Spark application logs of the Job to be persistent in the file system.
The parameters relevant to Spark logs are displayed:
- Compress Spark event logs: select this check box to compress the logs.
- Spark event logs directory: enter the directory in which Spark events are logged.
- Spark history server address: enter the location of the history server.
Since the administrator of your cluster can have defined these properties in the cluster configuration files, it is recommended to contact the administrator for the exact values.
In the Spark "scratch" directory field, enter the directory in which Talend 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.
Select the Wait for the Job to complete check box to make Talend Studio or, if you use Talend JobServer, your Job JVM keep monitoring the Job until the execution of the Job is over. By selecting this check box, you actually set the spark.yarn.submit.waitAppCompletion property to be true. While it is generally useful to select this check box when running a Spark Batch Job, it makes more sense to keep this check box clear when running a Spark Streaming Job.
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.
In the Advanced properties table, add any Spark properties you need to use to override their default counterparts used by Talend Studio.

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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