July 30, 2023

tUnite – Docs for ESB 7.x

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tUnite

Centralizes data from various and heterogeneous sources.

tUnite merges data from various
sources, based on a common schema.

Note that tUnite cannot exist in a
data flow loop. For instance, if a data flow goes through several tMap components to generate two flows, they cannot be fed to tUnite.

Note: This component is for sequential flow only and does not support parallelization.

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

tUnite Standard properties

These properties are used to configure tUnite running in the Standard Job framework.

The Standard
tUnite component belongs to the Orchestration family.

The component in this framework is available in all Talend
products.

Basic
settings

Schema and Edit
Schema

A schema is a row description, it defines the number of fields to
be processed and passed on to the next component. The schema is
either Built-in or stored remotely
in the Repository.

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.

Click Sync
columns to retrieve the schema from the previous
component in the Job.

This
component offers the advantage of the dynamic schema feature. This allows you to
retrieve unknown columns from source files or to copy batches of columns from a source
without mapping each column individually. For further information about dynamic schemas,
see
Talend Studio

User Guide.

This
dynamic schema feature is designed for the purpose of retrieving unknown columns of a
table and is recommended to be used for this purpose only; it is not recommended for the
use of creating tables.

 

Built-in: The
schema will be created and stored locally for this component only.
Related topic: see
Talend Studio User
Guide.

 

Repository: The
schema already exists and is stored in the Repository, hence can be
reused in various projects and Job designs. Related topic: see

Talend Studio User
Guide.

Advanced
settings

tStatCatcher
Statistics

Select this check box to collect log data at the
component level.

Global Variables

Global Variables

ERROR_MESSAGE: the error message generated by the
component when an error occurs. This is an After variable and it returns a string. This
variable functions only if the Die on error check box is
cleared, if the component has this check box.

NB_LINE: the number of rows processed. This is an After
variable and it returns an integer.

A Flow variable functions during the execution of a component while an After variable
functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl +
Space to access the variable list and choose the variable to use from it.

For further information about variables, see
Talend Studio

User Guide.

Usage

Usage rule

This component is not
startable and requires one or several input components and an output
component.

Connections

Outgoing links (from this component to another):

Row: Main.

Trigger: Run if; On Component Ok;
On Component Error

Incoming links (from one component to this one):

Row: Main; Reject.

For further information regarding connections, see

Talend Studio User
Guide.

Iterating on files and merge the content

The following Job iterates on a list of files then merges their content and displays
the final 2-column content on the console.

tUnite_1.png

Dropping and linking the components

  1. Drop the following components onto the design workspace: tFileList, tFileInputDelimited, tUnite
    and tLogRow.
  2. Connect the tFileList to the tFileInputDelimited using an Iterate connection and connect the other
    component using a row main link.

Configuring the components

  1. In the tFileList
    Basic settings view, browse to the
    directory, where the files to merge are stored.

    tUnite_2.png

    The files are pretty basic and contain a list of countries and their
    respective score.
    tUnite_3.png

  2. In the Case Sensitive field, select
    Yes to consider the letter case.
  3. Select the tFileInputDelimited component,
    and display this component’s Basic settings
    view.

    tUnite_4.png

  4. Fill in the File Name/Stream field by
    using the Ctrl+Space bar combination to
    access the variable completion list, and selecting
    tFileList.CURRENT_FILEPATH from the global variable list to
    process all files from the directory defined in the tFileList.
  5. Click the Edit Schema button and set
    manually the 2-column schema to reflect the input files’ content.

    tUnite_5.png

    For this example, the 2 columns are Country and
    Points. They are both nullable. The Country column is of
    String type and the
    Points column is of Integer type.
  6. Click OK to validate the setting and
    accept to propagate the schema throughout the Job.
  7. Then select the tUnite component and
    display the Component view. Notice that the
    output schema strictly reflects the input schema and is read-only.
  8. In the Basic settings view of tLogRow, select the Table option to display properly the output values.

Saving and executing the Job

  1. Press Ctrl+S to save your Job.
  2. Press F6, or click Run on the Run console to
    execute the Job.

    The console shows the data from the various files, merged into one single
    table.
    tUnite_6.png

tUnite properties for Apache Spark Batch

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

The Spark Batch
tUnite component belongs to the Orchestration family.

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

Basic settings

Schema and Edit
Schema

A schema is a row description, it defines the number of fields to be
processed and passed on to the next component. The schema is either
Built-in or stored remotely in the
Repository.

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.

Click Sync columns to retrieve the
schema from the previous component in the Job.

 

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.

Usage

Usage rule

This component is used as an intermediate step.

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.

Related scenarios

No scenario is available for the Spark Batch version of this component
yet.

tUnite properties for Apache Spark Streaming

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

The Spark Streaming
tUnite component belongs to the Orchestration family.

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

Basic settings

Schema and Edit
Schema

A schema is a row description, it defines the number of fields to be
processed and passed on to the next component. The schema is either
Built-in or stored remotely in the
Repository.

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.

Click Sync columns to retrieve the
schema from the previous component in the Job.

 

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.

Usage

Usage rule

This component is used as an intermediate step.

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.

Related scenarios

No scenario is available for the Spark Streaming version of this component
yet.

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