tJDBCOutput properties for Apache Spark Streaming
These properties are used to configure tJDBCOutput running in the Spark Streaming Job framework.
The Spark Streaming tJDBCOutput component belongs to the Databases family.
This component can be used to write data to a RDS MariaDB, a RDS PostgreSQL or a RDS SQLServer database.
This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric.
Basic settings
Property type |
Either Built-In or Repository. |
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Built-In: No property data stored centrally. |
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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 Centralizing database metadata. |
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Use an existing connection |
Select this check box and in the Component List drop-down list, select the desired connection component to reuse the connection details you already defined. |
JDBC URL |
The JDBC URL of the database to be used. For example, the JDBC URL for the Amazon Redshift database is jdbc:redshift://endpoint:port/database. If you are using Spark V1.3, this URL should contain the authentication information, such as:
jdbc:mysql://XX.XX.XX.XX:3306/Talend?user=ychen&password=talend |
Driver JAR |
Complete this table to load the driver JARs needed. To do this, click the [+] button under the table to add as many rows as needed, each row for a driver JAR, then select the cell and click the [...] button at the right side of the cell to open the Module dialog box from which you can select the driver JAR to be used. For example, the driver jar RedshiftJDBC41-1.1.13.1013.jar for the Redshift database. For more information, see Importing a database driver. |
Class Name |
Enter the class name for the specified driver between double quotation marks. For example, for the RedshiftJDBC41-1.1.13.1013.jar driver, the name to be entered is com.amazon.redshift.jdbc41.Driver. |
Username and Password |
Enter the authentication information to the database you need to connect to. To enter the password, click the [...] button next to the password field, enter the password in double quotes in the pop-up dialog box, and click OK to save the settings. Available only for Spark V1.4. and onwards. |
Table |
Name of the table to be written. Note that only one table can be written at a time. |
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. |
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Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. |
Action on data |
Select an action to be performed on data of the table defined.
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Die on error |
Select the check box to stop the execution of the Job when an error occurs. |
Advanced settings
Additional JDBC parameters |
Specify additional connection properties for the database connection you are creating. The properties are separated by semicolon and each property is a key-value pair, for example, encryption=1;clientname=Talend. This field is not available if the Use an existing connection check box is selected. |
Left protected char and Right protected char |
Enter the symbol reserved by the database you are using, the left part in Left protected char and the right part in Right protected char, so that tJDBCOutput can generate SQL expressions with this reserved symbol properly placed. For example, if you are using Oracle, double quotation marks (") are reserved for object names and so you need to enter the left and the right marks in these fields, respectively. Then at runtime, tJDBCOutput places double quotations marks around object names such as a table name. |
Additional Columns |
This option allows you to call SQL functions to perform actions on columns, provided that these are not insert, update or delete actions, or actions that require pre-processing. This option is not available if you have just created the database table (even if you delete it beforehand). Click the [+] button under the table to add column(s), and set the following parameters for each column. |
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Name: Type in the name of the schema column to be altered or inserted. |
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SQL expression: Type in the SQL statement to be executed in order to alter or insert the data in the corresponding column. |
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Position: Select Before, Replace or After, depending on the action to be performed on the reference column. |
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Reference column: Type in a reference column that the current component can use to locate or replace the new column, or the column to be modified. |
Use field options |
Select the check box for the corresponding column to customize a request, particularly if multiple actions are being carried out on the data.
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Use Batch |
Select this check box to activate the batch mode for data processing. This check box is available only when the Insert, the Update or the Delete option is selected from the Action on data list in the Basic settings view. |
Batch Size |
Specify the number of records to be processed in each batch. This field appears only when the Use batch mode check box is selected. |
Connection pool |
In this area, you configure, for each Spark executor, the connection pool used to control the number of connections that stay open simultaneously. The default values given to the following connection pool parameters are good enough for most use cases.
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Evict connections |
Select this check box to define criteria to destroy connections in the connection pool. The following fields are displayed once you have selected it.
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Usage
Usage rule |
This component is used as an end component and requires an input link. This component should use a tJDBCConfiguration component present in the same Job to connect to a database. You need to drop a tJDBCConfiguration component alongside this component and configure the Basic settings of this component to use tJDBCConfiguration. Although tJDBCOutput is flexible to connect to as various target databases as possible, some databases are not made to directly receive streaming data. Before using tJDBCOutput, ensure that your target system fits streaming operations and validate your architecture design based on this verification. |
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:
This connection is effective on a per-Job basis. |