July 30, 2023

tTeradataConfiguration – Docs for ESB 7.x

tTeradataConfiguration

Defines a connection to Teradata and enables the reuse of the connection
configuration in the same Job.

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

Depending on the Talend
product you are using, this component can be used in one, some or all of the following
Job frameworks:

tTeradataConfiguration properties for Apache Spark Batch

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

The Spark Batch
tTeradataConfiguration 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

Property type

Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the
properties are stored.

Host

Enter the IP address of the database server.

Database

Enter the name of the database to be used.

Username and
Password

Enter the database user authentication data.

To enter the password, click the […] button next to the
password field, and then in the pop-up dialog box enter the password between double quotes
and click OK to save the settings.

Additional JDBC parameters

Specify additional connection properties for the database
connection you are creating.

For example, you can enter CHARSET=KANJISJIS_OS to get support of Japanese characters.

Note:

You can set the encoding parameters through this field.

Advanced settings

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.

  • Max total number of connections: enter the maximum number
    of connections (idle or active) that are allowed to stay open simultaneously.

    The default number is 8. If you enter -1, you allow unlimited number of open connections at the same
    time.

  • Max waiting time (ms): enter the maximum amount of time
    at the end of which the response to a demand for using a connection should be returned by
    the connection pool. By default, it is -1, that is to say, infinite.

  • Min number of idle connections: enter the minimum number
    of idle connections (connections not used) maintained in the connection pool.

  • Max number of idle connections: enter the maximum number
    of idle connections (connections not used) maintained in the connection pool.

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.

  • Time between two eviction runs: enter the time interval
    (in milliseconds) at the end of which the component checks the status of the connections and
    destroys the idle ones.

  • Min idle time for a connection to be eligible to
    eviction
    : enter the time interval (in milliseconds) at the end of which the idle
    connections are destroyed.

  • Soft min idle time for a connection to be eligible to
    eviction
    : this parameter works the same way as Min idle
    time for a connection to be eligible to eviction
    but it keeps the minimum number
    of idle connections, the number you define in the Min number of idle
    connections
    field.

Usage

Usage rule

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

You need to drop tTeradataConfiguration along with the
Teradata-related Subjob to be run in the same Job so that the configuration is used by the
whole Job at runtime.

This component, along with the Spark Batch component Palette it belongs to,
appears only when you are creating a Spark Batch 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:

  • 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.

Limitation

Due to license incompatibility, one or more JARs required to use
this component are not provided. You can install the missing JARs for this particular
component by clicking the Install button
on the Component tab view. You can also
find out and add all missing JARs easily on the Modules tab in the
Integration
perspective of your studio. You can find more details about how to install external modules in
Talend Help Center (https://help.talend.com)
.

Related scenarios

For a scenario about how to use the same type of component in a Spark Batch Job, see Writing and reading data from MongoDB using a Spark Batch Job.

tTeradataConfiguration properties for Apache Spark Streaming

These properties are used to configure tTeradataConfiguration running in the Spark Streaming Job framework.

The Spark Streaming
tTeradataConfiguration component belongs to the Storage and the Databases families.

This component is available in Talend Real Time Big Data Platform and 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.

Host

Enter the IP address of the database server.

Database

Enter the name of the database to be used.

Username and
Password

Enter the database user authentication data.

To enter the password, click the […] button next to the
password field, and then in the pop-up dialog box enter the password between double quotes
and click OK to save the settings.

Additional JDBC parameters

Specify additional connection properties for the database
connection you are creating.

For example, you can enter CHARSET=KANJISJIS_OS to get support of Japanese characters.

Note:

You can set the encoding parameters through this field.

Advanced settings

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.

  • Max total number of connections: enter the maximum number
    of connections (idle or active) that are allowed to stay open simultaneously.

    The default number is 8. If you enter -1, you allow unlimited number of open connections at the same
    time.

  • Max waiting time (ms): enter the maximum amount of time
    at the end of which the response to a demand for using a connection should be returned by
    the connection pool. By default, it is -1, that is to say, infinite.

  • Min number of idle connections: enter the minimum number
    of idle connections (connections not used) maintained in the connection pool.

  • Max number of idle connections: enter the maximum number
    of idle connections (connections not used) maintained in the connection pool.

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.

  • Time between two eviction runs: enter the time interval
    (in milliseconds) at the end of which the component checks the status of the connections and
    destroys the idle ones.

  • Min idle time for a connection to be eligible to
    eviction
    : enter the time interval (in milliseconds) at the end of which the idle
    connections are destroyed.

  • Soft min idle time for a connection to be eligible to
    eviction
    : this parameter works the same way as Min idle
    time for a connection to be eligible to eviction
    but it keeps the minimum number
    of idle connections, the number you define in the Min number of idle
    connections
    field.

Usage

Usage rule

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

You need to drop tTeradataConfiguration along with the
Teradata-related Subjob to be run in the same Job so that the configuration is used by the
whole Job at runtime.

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:

  • 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.

Limitation

Due to license incompatibility, one or more JARs required to use
this component are not provided. You can install the missing JARs for this particular
component by clicking the Install button
on the Component tab view. You can also
find out and add all missing JARs easily on the Modules tab in the
Integration
perspective of your studio. You can find more details about how to install external modules in
Talend Help Center (https://help.talend.com)
.

Related scenarios

For a scenario about how to use the same type of component in a Spark Streaming Job, see
Reading and writing data in MongoDB using a Spark Streaming Job.


Document get from Talend https://help.talend.com
Thank you for watching.
Subscribe
Notify of
guest
0 Comments
Inline Feedbacks
View all comments
0
Would love your thoughts, please comment.x
()
x