tRedshiftConfiguration properties for Apache Spark Batch

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

The Spark Batch tRedshiftConfiguration component belongs to the Storage and the Databases families.

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

Basic settings

Property type	Either Built-In or Repository. Built-In: No property data stored centrally. Repository: Select the repository file where the properties are stored.
Host	Enter the endpoint of the database you need to connect to in Redshift.
Port	Enter the port number of the database you need to connect to in Redshift. The related information can be found in the Cluster Database Properties area in the Web console of your Redshift. For further information, see Managing clusters console.
Username and Password	Enter the authentication information to the Redshift database you need to connect to. To enter the password, click the [...] button next to the password field, and then in the pop-up dialog box enter the password between double quotes and click OK to save the settings.
Database	Enter the name of the database you need to connect to in Redshift. The related information can be found in the Cluster Database Properties area in the Web console of your Redshift. For further information, see Managing clusters console. The bucket and the Redshift database to be used must be in the same region on Amazon. This could avoid the S3ServiceException errors known to Amazon. For further information about these errors, see S3ServiceException Errors.
Schema	Enter the name of the database schema to be used in Redshift. The default schema is called PUBLIC. A schema in terms of Redshift is similar to a operating system directory. For further information about a Redshift schema, see Schemas.
Additional JDBC Parameters	Specify additional JDBC properties for the connection you are creating. The properties are separated by ampersand & and each property is a key-value pair. For example, ssl=true & sslfactory=com.amazon.redshift.ssl.NonValidatingFactory, which means the connection will be created using SSL.
S3 configuration	Select the tS3Configuration component from which you want Spark to use the configuration details to connect to S3. You need drop the tS3Configuration component to be used alongside tRedshiftConfiguration in the same Job so that this tS3Configuration is displayed on the S3 configuration list.
S3 temp path	Enter the location in S3 in which the data to be transferred from or to Redshift is temporarily stored. This path is independent of the temporary path you need to set in the Basic settings tab of tS3Configuration.

Usage

Usage rule	This component is used with no need to be connected to other components. You need to drop tRedshiftConfiguration alongside the other Redshift related Subjobs to be run in the same Job so that the configuration is used by the whole Job at runtime. Since Redshift uses S3 to store temporary data, you need to drop a tS3Configuration component alongside tRedshiftConfiguration in the same Job so that the S3 configuration is used by the whole Job at runtime. 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.

Usage rule

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

You need to drop tRedshiftConfiguration alongside the other Redshift related Subjobs to be run in the same Job so that the configuration is used by the whole Job at runtime.

Since Redshift uses S3 to store temporary data, you need to drop a tS3Configuration component alongside tRedshiftConfiguration in the same Job so that the S3 configuration is used by the whole Job at runtime.

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.

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