tKuduConfiguration

Enables the reuse of the connection configuration to Cloudera Kudu in the same
Job.

tKuduConfiguration provides Kudu
connection information for the Kudu components used in the same Spark
Job. The Spark cluster to be used reads this configuration to eventually
connect to Kudu.

tKuduConfiguration properties for Apache Spark Batch

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

The Spark Batch
tKuduConfiguration component belongs to the Storage and the Databases families.

The component in this framework is available in all subscription-based Talend products with Big Data
and Talend Data Fabric.

Basic settings

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.

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.

Usage

Usage rule	This component is used with no need to be connected to other components. Use it only when you need to connect to a Cloudera Kudu cluster.
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.

Usage rule

This component is used with no need to be connected to other
components.

Use it only when you need to connect to a Cloudera Kudu cluster.

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.

Related scenario

For a related scenario, see Writing and reading data from Cloudera Kudu using a Spark Batch Job.

Document get from Talend https://help.talend.com

Thank you for watching.

Docs 7.x

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tKuduConfiguration – Docs for ESB 7.x

tKuduConfiguration