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 subscription-based 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. 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.
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: Use scan: select this radio box to scan the whole Kudu table. Use query: select this radio box to display the Query fields table. Then complete this table to build the queries to be used.

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.

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: 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 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 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:

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 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.

Did this page help you?

If you find any issues with this page or its content – a typo, a missing step, or a technical error – please let us know!

Leave your feedback here