July 30, 2023

tKuduInput – Docs for ESB 7.x

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tKuduInput

Retrieves data from a Cloudera Kudu table and sends them to the component that
follows for transformation.

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 click the relevant 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.

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 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

Document get from Talend https://help.talend.com
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Docs 7.x
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