tGSConfiguration properties for Apache Spark Streaming

These properties are used to configure tGSConfiguration running in the Spark Streaming Job framework.

The Spark Streaming tGSConfiguration component belongs to the Storage family.

This component is available in Talend Real Time Big Data Platform and Talend Data Fabric.

Basic settings

When you use this component with Google Dataproc:

Google Storage bucket	Enter the name of the bucket to be used by the whole Job. Then the File components such as tFileInputDelimited or tFileOutputDelimited use the directories in this bucket. For example, if you enter `my_bucket` in this field and enter `/user/ychen` in the Folder field of tFileInputDelimited, tFileInputDelimited reads data from gs://my_bucket/user/ychen.

Google Storage bucket

Enter the name of the bucket to be used by the whole Job. Then the File components such as tFileInputDelimited or tFileOutputDelimited use the directories in this bucket.

For example, if you enter my_bucket in this field and enter /user/ychen in the Folder field of tFileInputDelimited, tFileInputDelimited reads data from gs://my_bucket/user/ychen.

When you use this component with other distributions:

Project identifier	Enter the ID of your Google Cloud Platform project. If you are not certain about your project ID, check it in the Manage Resources page of your Google Cloud Platform services.
Google Storage bucket	Enter the name of the bucket to be used by the whole Job. Then the File components such as tFileInputDelimited or tFileOutputDelimited use the directories in this bucket. For example, if you enter `my_bucket` in this field and enter `/user/ychen` in the Folder field of tFileInputDelimited, tFileInputDelimited reads data from gs://my_bucket/user/ychen.
Use P12 credentials file format	When the Google credentials file to be used is in P12 format, select this check box and then in the Service account Id field that is displayed, enter the ID of the service account for which this P12 credentials file has been created.
Path to Google Credentials file	Enter the path to the credentials file associated to the user account to be used. This file must be stored in the machine in which your Talend Job is actually launched and executed. If you use Talend Jobserver to run your Job, store the credentials file not only in the machine of the Jobserver, in which the Job is launched, but also in the worker machines of the Spark cluster, in which the Job is executed; if you do not use the Jobserver, store the credentials file in your local machine from which you launch the Job and in the worker machines of the Spark cluster.

Global Variables

Global Variables	ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box. A Flow variable functions during the execution of a component while an After variable functions after the execution of the component. To fill up a field or expression with a variable, press Ctrl + Space to access the variable list and choose the variable to use from it. For further information about variables, see Talend Studio User Guide.

ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box.

A Flow variable functions during the execution of a component while an After variable functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl + Space to access the variable list and choose the variable to use from it.

For further information about variables, see Talend Studio User Guide.

Usage

Usage rule	This component is used standalone in a subJob to provide connection configuration to Google Storage for the whole Job.
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.

Usage rule

This component is used standalone in a subJob to provide connection configuration to Google Storage for the whole Job.

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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