tJDBCLookupInput properties for Apache Spark Streaming
These properties are used to configure tJDBCLookupInput running in the Spark Streaming Job framework.
The Spark Streaming tJDBCLookupInput component belongs to the Databases family.
The component in this framework is available in Talend Real-Time Big Data Platform and in 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. |
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. |
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:
|
|
Table Name |
Type in the name of the table from which you need to read data. |
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. The result of the query must contain only records that match join key you need to use in tMap. In other words, you must use the schema of the main flow to tMap to construct the SQL statement here in order to load only the matched records into the lookup flow. This approach ensures that no redundant records are loaded into memory and outputted to the component that follows. |
Guess Query |
Click the Guess Query button to generate the query which corresponds to your table schema in the Query field. |
Guess schema |
Click the Guess schema button to retrieve the table schema. |
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. |
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.
|
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.
|
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 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. This component, along with the Spark Streaming component Palette it belongs to, appears only when you are creating a Spark Streaming 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:
This connection is effective on a per-Job basis. |