tRedshiftInput properties for Apache Spark Batch - 7.1

Amazon Redshift

author
Talend Documentation Team
EnrichVersion
7.1
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Open Studio for Big Data
Talend Open Studio for Data Integration
Talend Open Studio for ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Amazon services (Integration) > Amazon Redshift components
Data Quality and Preparation > Third-party systems > Amazon services (Integration) > Amazon Redshift components
Design and Development > Third-party systems > Amazon services (Integration) > Amazon Redshift components
EnrichPlatform
Talend Studio

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

The Spark Batch tRedshiftInput component belongs to the Databases family.

The component in this framework is available in all subscription-based 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.

Click this icon to open a database connection wizard and store the database connection parameters you set in the component Basic settings view.

For more information about setting up and storing database connection parameters, see Talend Studio User Guide.

Use an existing connection

Select this check box and in the Component List click the relevant connection component to reuse the connection details you already defined.

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.

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.

Schema and Edit Schema

A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields.

 

Built-In: You create and store the schema locally for this component only.

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

 

Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

  • View schema: choose this option to view the schema only.

  • Change to built-in property: choose this option to change the schema to Built-in for local changes.

  • Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.

Table name

Enter the name of the table from which the data will be read.

Read from

Select the type of the source of the data to be read.
  • Table: tRedshiftInput reads the data of the table you specify in the Table name field.

  • Query: tRedshiftInput reads the result of the query you write in the Query field.

Query Type and Query

Specify the database query statement paying particularly attention to the properly sequence of the fields which must correspond to the schema definition.

Guess Query

Click the Guess Query button to generate the query which corresponds to your table schema in the Query field.

Advanced settings

Trim all the String/Char columns

Select this check box to remove leading whitespace and trailing whitespace from all String/Char columns.

Trim column

This table is filled automatically with the schema being used. Select the check box(es) corresponding to the column(s) to be trimmed.

Usage

Usage rule

This component is used as a start component and requires an output link..

This component should use a tRedshiftConfiguration component present in the same Job to connect to Redshift. You need to drop a tRedshiftConfiguration component alongside this component and configure the Basic settings of this component to use tRedshiftConfiguration.

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-premise 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 or tS3Configuration.

    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.