July 30, 2023

tNormalize – Docs for ESB 7.x

  2. Talend for ESB Docs
  3. Docs 7.x
  4. tNormalize – Docs for ESB 7.x

tNormalize

Normalizes the input flow following SQL standard to help improve data quality and
thus eases the data update.

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

tNormalize Standard properties

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

The Standard
tNormalize component belongs to the Processing 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
(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.

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.

 

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.

Column to normalize

Select the column from the input flow which the normalization is
based on.

Item separator

Enter the separator which will delimit data in the input
flow.

Note:

The item separator is based on regular expressions, so the
character “.” (a special
character for regular expression) should be avoided or used
carefully here.

Advanced settings

Get rid of duplicated rows from output

Select this check box to deduplicate rows in the data of the
output flow.

Use CSV parameters

Select this check box to include CSV specific parameters such as
escape mode and enclosure character.

Discard the trailing empty strings

Select this check box to discard the trailing empty
strings.
Trim resulting values

Select this check box to trim leading and trailing whitespace from
the resulting data.

Note:

When both Discard the trailing empty
string and Trim resulting
values check boxes are selected, the former works
first.

tStatCatcher Statistics

Select this check box to gather the Job processing metadata at the Job level as well as
at each 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 read by an input component or
transferred to an output component. 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 can be used as intermediate step in a data
flow.

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).

Normalizing data

This simple scenario illustrates a Job that normalizes a list of tags for Web forum
topics, and displays the result in a table on the Run
console.

This list is not well organized and it contains trailing empty strings, leading and
trailing whitespace, and repeated tags, as shown below.

Setting up the Job

  1. Drop the following components from the Palette to the design workspace: tFileInputDelimited, tNormalize, tLogRow.
  2. Connect the components using Row >
    Main connections.

    tNormalize_1.png

Configuring the components

  1. Double-click the tFileInputDelimited
    component to open its Basic settings
    view.

    tNormalize_2.png

  2. In the File name field, specify the path
    to the input file to be normalized.
  3. Click the […] button next to Edit schema to open the Schema dialog box, and set up the input schema by adding
    one column named Tags. When done, click OK to validate your schema setup and close the
    dialog box, leaving the rest of the settings as they are.

    tNormalize_3.png

  4. Double-click the tNormalize component to
    open Basic settings view.

    tNormalize_4.png

  5. Check the schema, and if necessary, click Sync
    columns     to get the schema synchronized with the input
    component.
  6. Define the column the normalization operation is based on.

    In this use case, the input schema has only one column,
    Tags, so just accept the default setting.
  7. In the Advanced settings view, select the
    Get rid of duplicate rows from output,
    Discard the trailing empty strings, and
    Trim resulting values check
    boxes.

    tNormalize_5.png

  8. In the tLogRow component, select the
    Print values in the cells of table
    radio button.

Saving and executing the Job

  1. Press Ctrl+S to save your Job.
  2. Click Run on the Run tab or press F6 to
    execute the Job.

    tNormalize_6.png

    The list is tidied up, with duplicate tags, leading and trailing
    whitespace and trailing empty strings removed, and the result is displayed
    in a table cell on the console.

tNormalize MapReduce properties (deprecated)

These properties are used to configure tNormalize running in the MapReduce Job framework.

The MapReduce
tNormalize component belongs to the Processing family.

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

The MapReduce framework is deprecated from Talend 7.3 onwards. Use Talend Jobs for Apache Spark to accomplish your integration tasks.

Basic settings

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.

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.

 

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.

Column to normalize

Select the column from the input flow which the normalization is
based on.

Item separator

Enter the separator which will delimit data in the input
flow.

Note:

The item separator is based on regular expressions, so the
character “.” (a special
character for regular expression) should be avoided or used
carefully here.

Advanced settings

Use CSV parameters

Select this check box to include CSV specific parameters such as
escape mode and enclosure character.

Discard the trailing empty strings

Select this check box to discard the trailing empty
strings.
Trim resulting values

Select this check box to trim leading and trailing whitespace from
the resulting data.

Note:

When both Discard the trailing empty
string and Trim resulting
values check boxes are selected, the former works
first.

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.

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

In a
Talend
 Map/Reduce Job, this component is used as an intermediate
step and other components used along with it must be Map/Reduce components, too. They
generate native Map/Reduce code that can be executed directly in Hadoop.

For further information about a
Talend
 Map/Reduce Job, see the sections
describing how to create, convert and configure a
Talend
 Map/Reduce Job of the

Talend Open Studio for Big Data Getting Started Guide
.

For scenario demonstrating a Map/Reduce Job using this component,
see Normalizing data using Map/Reduce components.

Note that in this documentation, unless otherwise
explicitly stated, a scenario presents only Standard Jobs,
that is to say traditional
Talend
 data integration Jobs, and non Map/Reduce Jobs.

Normalizing data using Map/Reduce components

This scenario applies only to subscription-based Talend products with Big
Data.

You can produce the Map/Reduce version of the Job described earlier using
Map/Reduce components. This
Talend
 Map/Reduce Job generates Map/Reduce code and is run natively in Hadoop.

tNormalize_7.png

Note that the
Talend
 Map/Reduce components are available to subscription-based Big Data users only
and this scenario can be replicated only with Map/Reduce components.

The sample data used in this scenario is the same as in the scenario
explained earlier.

Since
Talend Studio
 allows you to convert a Job between its Map/Reduce and Standard (Non
Map/Reduce) versions, you can convert the scenario explained earlier to create this
Map/Reduce Job. This way, many components used can keep their original settings so as to
reduce your workload in designing this Job.

Before starting to replicate this scenario, ensure that you have appropriate
rights and permissions to access the Hadoop distribution to be used. Then proceed as
follows:

Converting the Job to a Big Data Batch Job

  1. In the Repository tree view, right-click the Job you have created in
    the earlier scenario to open its contextual menu and select Edit properties.

    Then the Edit properties dialog box is displayed. Note that the Job must
    be closed before you are able to make any changes in this dialog box.
    This dialog box looks like the image below:

    tNormalize_8.png

    Note that you can change the Job name as well as the other
    descriptive information about the Job from this dialog box.
  2. From the Job Type list, select
    Big Data Batch. Then a Map/Reduce Job
    using the same name appears under the Big Data
    Batch     sub-node of the Job
    Design     node.

Rearranging the components

  1. Double-click this new Map/Reduce Job to open it in the workspace. The
    Map/Reduce components’ Palette is opened
    accordingly and in the workspace, the crossed-out components, if any,
    indicate that those components do not have the Map/Reduce version.
  2. Right-click each of those components in question and select Delete to remove them from the workspace.
  3. Drop a tHDFSInput component and a
    tHDFSOutput component in the workspace.
    The tHDFSInput component reads data from
    the Hadoop distribution to be used, the tHDFSOutput component, replacing tLogRow, writes data in that distribution.

    If from scratch, you have to drop a tNormalize component, too.
  4. Connect tHDFSInput to tNormalize using the Row >
    Main     link and accept to get the schema of tNormalize.
  5. Connect as well tNormalize to tHDFSOutput using Row >
    Main     link.

Setting up Hadoop connection

  1. Click Run to open its view and then click the
    Hadoop Configuration tab to display its
    view for configuring the Hadoop connection for this Job.
  2. From the Property type list,
    select Built-in. If you have created the
    connection to be used in Repository, then
    select Repository and thus the Studio will
    reuse that set of connection information for this Job.
  3. In the Version area, select the
    Hadoop distribution to be used and its version.

    • If you use Google Cloud Dataproc, see Google Cloud Dataproc.

    • If you cannot
      find the Cloudera version to be used from this drop-down list, you can add your distribution
      via some dynamic distribution settings in the Studio.

    • If you cannot find from the list the distribution corresponding to
      yours, select Custom so as to connect to a
      Hadoop distribution not officially supported in the Studio. For a
      step-by-step example about how to use this
      Custom option, see Connecting to a custom Hadoop distribution.

  4. In the Name node field, enter the location of
    the master node, the NameNode, of the distribution to be used. For example,
    hdfs://tal-qa113.talend.lan:8020.

    • If you are using a MapR distribution, you can simply leave maprfs:/// as it is in this field; then the MapR
      client will take care of the rest on the fly for creating the connection. The
      MapR client must be properly installed. For further information about how to set
      up a MapR client, see the following link in MapR’s documentation: http://doc.mapr.com/display/MapR/Setting+Up+the+Client

    • If you are using WebHDFS, the location should be
      webhdfs://masternode:portnumber; WebHDFS with SSL is not
      supported yet.

  5. In the Resource Manager field,
    enter the location of the ResourceManager of your distribution. For example,
    tal-qa114.talend.lan:8050.

    • Then you can continue to set the following parameters depending on the
      configuration of the Hadoop cluster to be used (if you leave the check
      box of a parameter clear, then at runtime, the configuration about this
      parameter in the Hadoop cluster to be used will be ignored):

      • Select the Set resourcemanager
        scheduler address         check box and enter the Scheduler address in
        the field that appears.

      • Select the Set jobhistory
        address         check box and enter the location of the JobHistory
        server of the Hadoop cluster to be used. This allows the metrics information of
        the current Job to be stored in that JobHistory server.

      • Select the Set staging
        directory         check box and enter this directory defined in your
        Hadoop cluster for temporary files created by running programs. Typically, this
        directory can be found under the yarn.app.mapreduce.am.staging-dir property in the configuration files
        such as yarn-site.xml or mapred-site.xml of your distribution.

      • Select the Use datanode hostname check box to allow the
        Job to access datanodes via their hostnames. This actually sets the dfs.client.use.datanode.hostname
        property to true. When connecting to a
        S3N filesystem, you must select this check box.


  6. If you are accessing the Hadoop cluster running
    with Kerberos security, select this check box, then, enter the Kerberos
    principal name for the NameNode in the field displayed. This enables you to use
    your user name to authenticate against the credentials stored in Kerberos.

    • If this cluster is a MapR cluster of the version 5.0.0 or later, you can set the
      MapR ticket authentication configuration in addition or as an alternative by following
      the explanation in Connecting to a security-enabled MapR.

      Keep in mind that this configuration generates a new MapR security ticket for the username
      defined in the Job in each execution. If you need to reuse an existing ticket issued for the
      same username, leave both the Force MapR ticket
      authentication       check box and the Use Kerberos
      authentication       check box clear, and then MapR should be able to automatically
      find that ticket on the fly.

    In addition, since this component performs Map/Reduce computations, you
    also need to authenticate the related services such as the Job history server and
    the Resource manager or Jobtracker depending on your distribution in the
    corresponding field. These principals can be found in the configuration files of
    your distribution. For example, in a CDH4 distribution, the Resource manager
    principal is set in the yarn-site.xml file and the Job history
    principal in the mapred-site.xml file.

    If you need to use a Kerberos keytab file to log in, select Use a keytab to authenticate. A keytab file contains
    pairs of Kerberos principals and encrypted keys. You need to enter the principal to
    be used in the Principal field and the access
    path to the keytab file itself in the Keytab
    field. This keytab file must be stored in the machine in which your Job actually
    runs, for example, on a Talend
    Jobserver.

    Note that the user that executes a keytab-enabled Job is not necessarily
    the one a principal designates but must have the right to read the keytab file being
    used. For example, the user name you are using to execute a Job is user1 and the principal to be used is guest; in this
    situation, ensure that user1 has the right to read the keytab
    file to be used.

  7. In the User name field, enter the login user
    name for your distribution. If you leave it empty, the user name of the machine
    hosting the Studio will be used.
  8. In the Temp folder field, enter the path in
    HDFS to the folder where you store the temporary files generated during
    Map/Reduce computations.

  9. Leave the default value of the Path separator in
    server     as it is, unless you have changed the separator used by your
    Hadoop distribution’s host machine for its PATH variable or in other words, that
    separator is not a colon (:). In that situation, you must change this value to the
    one you are using in that host.
  10. Leave the Clear temporary folder check box
    selected, unless you want to keep those temporary files.
  11. Leave the Compress intermediate map output to reduce
    network traffic     check box selected, so as to spend shorter time
    to transfer the mapper task partitions to the multiple reducers.

    However, if the data transfer in the Job is negligible, it is recommended to
    clear this check box to deactivate the compression step, because this
    compression consumes extra CPU resources.
  12. If you need to use custom Hadoop properties, complete the Hadoop properties table with the property or
    properties to be customized. Then at runtime, these changes will override the
    corresponding default properties used by the Studio for its Hadoop
    engine.

    For further information about the properties required by Hadoop, see Apache’s
    Hadoop documentation on http://hadoop.apache.org, or
    the documentation of the Hadoop distribution you need to use.

  13. If the HDFS transparent encryption has been enabled in your cluster, select
    the Setup HDFS encryption configurations check
    box and in the HDFS encryption key provider field
    that is displayed, enter the location of the KMS proxy.

    For further information about the HDFS transparent encryption and its KMS proxy, see Transparent Encryption in HDFS.

  14. You can tune the map and reduce computations by
    selecting the Set memory check box to set proper memory allocations
    for the computations to be performed by the Hadoop system.

    The memory parameters to be set are Map (in Mb),
    Reduce (in Mb) and ApplicationMaster (in Mb). These fields allow you to dynamically allocate
    memory to the map and the reduce computations and the ApplicationMaster of YARN.

    For further information about the Resource Manager, its scheduler and the
    ApplicationMaster, see YARN’s documentation such as http://hortonworks.com/blog/apache-hadoop-yarn-concepts-and-applications/.

    For further information about how to determine YARN and MapReduce memory configuration
    settings, see the documentation of the distribution you are using, such as the following
    link provided by Hortonworks: http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.6.0/bk_installing_manually_book/content/rpm-chap1-11.html.


  15. If you are using Cloudera V5.5+, you can select the Use Cloudera Navigator check box to enable the Cloudera Navigator
    of your distribution to trace your Job lineage to the component level, including the
    schema changes between components.

    With this option activated, you need to set the following parameters:

    • Username and Password: this is the credentials you use to connect to your Cloudera
      Navigator.

    • Cloudera Navigator URL : enter the location of the
      Cloudera Navigator to be connected to.

    • Cloudera Navigator Metadata URL: enter the location
      of the Navigator Metadata.

    • Activate the autocommit option: select this check box
      to make Cloudera Navigator generate the lineage of the current Job at the end of the
      execution of this Job.

      Since this option actually forces Cloudera Navigator to generate lineages of
      all its available entities such as HDFS files and directories, Hive queries or Pig
      scripts, it is not recommended for the production environment because it will slow the
      Job.

    • Kill the job if Cloudera Navigator fails: select this check
      box to stop the execution of the Job when the connection to your Cloudera Navigator fails.

      Otherwise, leave it clear to allow your Job to continue to run.

    • Disable SSL validation: select this check box to
      make your Job to connect to Cloudera Navigator without the SSL validation
      process.

      This feature is meant to facilitate the test of your Job but is not
      recommended to be used in a production cluster.


  16. If you are using Hortonworks Data Platform V2.4.0 onwards and you have
    installed Atlas in your cluster, you can select the Use
    Atlas     check box to enable Job lineage to the component level, including the
    schema changes between components.

    With this option activated, you need to set the following parameters:

    • Atlas URL: enter the location of the Atlas to be
      connected to. It is often http://name_of_your_atlas_node:port

    • Die on error: select this check box to stop the Job
      execution when Atlas-related issues occur, such as connection issues to Atlas.

      Otherwise, leave it clear to allow your Job to continue to run.

    In the Username and Password fields, enter the authentication information for access to
    Atlas.

Configuring input and output components

Configuring tHDFSInput

  1. Double-click tHDFSInput to open its
    Component view.

    tNormalize_9.png

  2. Click the

    tNormalize_10.png

    button next to Edit
    schema     to verify that the schema received in the earlier
    steps is properly defined.

    tNormalize_11.png

    Note that if you are creating this Job from scratch, you need to click the

    tNormalize_12.png

    button to manually define the schema; otherwise, if the
    schema has been defined in Repository, you
    can select the Repository option from the
    Schema list in the Basic settings view to reuse it. For further
    information about how to define a schema in Repository, see the chapter describing metadata management
    in the
    Talend Studio User Guide     or the chapter describing the
    Hadoop cluster node in Repository of
    Talend Open Studio for Big Data Getting Started Guide
    .

  3. If you make changes in the schema, click OK to validate these changes and accept the propagation
    prompted by the pop-up dialog box.
  4. In the Folder/File field, enter the path,
    or browse to the source file you need the Job to read.

    If this file is not in the HDFS system to be used, you have to place it in
    that HDFS, for example, using tFileInputDelimited and tHDFSOutput in a Standard
    Job.

Reviewing the transformation component

Double-click tNormalize to open its
Component view.

tNormalize_13.png

This component keeps its both Basic
settings and Advanced
settings used by the original Job. It normalizes the
Tags column of the input flow.
tNormalize_14.png

Configuring tHDFSOutput

  1. Double-click tHDFSOutput to open its
    Component view.

    tNormalize_15.png

  2. As explained earlier for verifying the schema of tHDFSInput, do the same to verify the schema of tHDFSOutput. If it is not consistent with that of
    its preceding component, tNormalize, click
    Sync column to retrieve the schema of
    tNormalize.

    tNormalize_16.png

  3. In the Folder field, enter the path, or
    browse to the folder you want to write data in.
  4. From the Action list, select the
    operation you need to perform on the folder in question. If the folder
    already exists, select Overwrite;
    otherwise, select Create.

Executing the Job

Then you can press F6 to run this Job.

Once done, view the execution results in the web console of HDFS.

tNormalize_17.png

If you need to obtain more details about the Job, it is recommended to use the web
console of the Jobtracker provided by the Hadoop distribution you are using.

tNormalize properties for Apache Spark Batch

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

The Spark Batch
tNormalize component belongs to the Processing 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
(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.

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.

 

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.

Column to normalize

Select the column from the input flow which the normalization is
based on.

Item separator

Enter the separator which will delimit data in the input
flow.

Note:

The item separator is based on regular expressions, so the
character “.” (a special
character for regular expression) should be avoided or used
carefully here.

Advanced settings

Use CSV parameters

Select this check box to include CSV specific parameters such as
escape mode and enclosure character.

Discard the trailing empty strings

Select this check box to discard the trailing empty
strings.
Trim resulting values

Select this check box to trim leading and trailing whitespace from
the resulting data.

Note:

When both Discard the trailing empty
string and Trim resulting
values check boxes are selected, the former works
first.

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.

tNormalize properties for Apache Spark Streaming

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

The Spark Streaming
tNormalize component belongs to the Processing 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
(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.

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.

 

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.

Column to normalize

Select the column from the input flow which the normalization is
based on.

Item separator

Enter the separator which will delimit data in the input
flow.

Note:

The item separator is based on regular expressions, so the
character “.” (a special
character for regular expression) should be avoided or used
carefully here.

Advanced settings

Use CSV parameters

Select this check box to include CSV specific parameters such as
escape mode and enclosure character.

Discard the trailing empty strings

Select this check box to discard the trailing empty
strings.
Trim resulting values

Select this check box to trim leading and trailing whitespace from
the resulting data.

Note:

When both Discard the trailing empty
string and Trim resulting
values check boxes are selected, the former works
first.

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
Thank you for watching.
Docs 7.x
Subscribe
Notify of
guest
0 Comments
Inline Feedbacks
View all comments
0
Would love your thoughts, please comment.x
()
x