tKuduInput properties for Apache Spark Batch
These properties are used to configure tKuduInput running in the Spark Batch Job framework.
The Spark Batch tKuduInput component belongs to the Databases family.
The component in this framework is available in all Talend products with Big Data and Talend Data Fabric.
Basic settings
Use an existing configuration |
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. |
Server connection |
Click the [+] button to add as many rows as the Kudu masters you need to use, each row for a master. Then enter the locations and the listening ports of the master nodes of the Kudu service to be used. This component supports only the Apache Kudu service installed on Cloudera. For compatibility information between Apache Kudu and Cloudera, see the related Cloudera documentation:Compatibility Matrix for Apache Kudu. |
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.
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Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:
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Kudu table |
Specify the name of the table from which you need to read data. |
Query mode |
Select the mode you want to use to read data from the table:
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Advanced settings
Limit |
Enter, without double quotation marks, the number of rows you want to display after the scan or the query of your Kudu table. This number does not change the number of rows to be actually scanned or queried. |
Usage
Usage rule |
This component is used as a start component and requires an output link. |
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. |