July 30, 2023

tReplace – Docs for ESB 7.x

  2. Talend for ESB Docs
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  4. tReplace – Docs for ESB 7.x

tReplace

Cleanses all files before further processing.

Carries out a Search &
Replace operation in the input columns defined.

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

tReplace Standard properties

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

The Standard
tReplace 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.

Two read-only columns, Value and Match are added to the output
schema automatically.

 

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.

Simple Mode Search / Replace

Click the

tReplace_1.png

button to add as many conditions as needed. The
conditions are performed one after the other for each row.

Input column: Select the column of
the schema the search & replace is to be operated on

Search: Type in the value to search
in the input column

Replace with: Type in the
substitution value.

Whole word: Select this check box
if the searched value is to be considered as whole.

Case sensitive: Select this check
box to care about the case.

Note that you cannot use regular expression in these columns.

Use advanced mode

Select this check box when the operation you want to perform
cannot be carried out through the simple mode. In the text field,
type in the regular expression as required.

Advanced settings

tStatCatcher Statistics

Select this check box to gather the job processing metadata at a 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 is not startable as it requires an input flow. And
it requires an output component.

Cleaning up and filtering a CSV file

This Job searches and replaces various typos and defects in a csv
file then operates a column filtering before producing a new csv file with the final
output.

tReplace_2.png

  • Drop the following components from the Palette onto the design workspace: tFileInputDelimited, tReplace, tFilterColumn
    and tFileOutputDelimited.

  • Connect the components using Main
    Row     connections via a right-click each component.

  • Select the tFileInputDelimited component and set the input flow
    parameters.

tReplace_3.png

  • The File is a simple csv
    file stored locally. The Row Separator is
    a carriage return and the Field Separator
    is a semi-colon. In the Header is the name
    of the column, and no Footer nor
    Limit are to be set.

  • The file contains characters such as: *t,
    .     or Nikson which we want to turn
    into Nixon, and streat, which we want to turn into Street.

tReplace_4.png

  • The schema for this file is built in also and made of four
    columns of various types (string or int).

  • Now select the tReplace
    component to set the search & replace parameters.

tReplace_5.png

  • The schema can be synchronized with the incoming flow.

  • Select the Simple mode
    check box as the search parameters can be easily set without requiring the use
    of regexp.

  • Click the plus sign to add some lines to the parameters table.

  • On the first parameter line, select Amount as InputColumn. Type “.” in the Search
    field, and “,” in the Replace field.

  • On the second parameter line, select Street as InputColumn. Type “streat” in the Search field, and “Street” in the
    Replace field.

  • On the third parameter line, select again Amount as InputColumn. Type “$” in the Search
    field, and “£” in the Replace field.

  • On the fourth paramater line, select Name
    as InputColumn. Type “Nikson” in the Search field,
    and “Nixon” in the Replace field.

  • On the fifth parameter line, select Firstname as InputColumn. Type
    “*t” in the Search field, and replace them with nothing between double
    quotes.

  • The advanced mode isn’t used in this scenario.

  • Select the next component in the Job, tFilterColumn.

tReplace_6.png

  • The tFilterColumn
    component holds a schema editor allowing to build the output schema based on the
    column names of the input schema. In this use case, add one new column named empty_field and change the order of the input schema
    columns to obtain a schema as follows: empty_field,
    Firstname, Name, Street, Amount    .

  • Click OK to validate.

tReplace_7.png

  • Set the tFileOutputDelimited properties manually.

  • The schema is built-in for this scenario, and comes from the
    preceding component in the Job.

  • Save the Job and press F6
    to execute it.

tReplace_8.png

The first column is empty, the rest of the columns have been cleaned up
from the parasitical characters, and Nikson was replaced with Nixon. The street column was moved and the decimal delimiter has
been changed from a dot to a comma, along with the currency sign.

tReplace MapReduce properties (deprecated)

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

The MapReduce
tReplace 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.

Two read-only columns, Value and Match are added to the output
schema automatically.

 

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.

Simple Mode Search / Replace

Click the

tReplace_1.png

button to add as many conditions as needed. The
conditions are performed one after the other for each row.

Input column: Select the column of
the schema the search & replace is to be operated on

Search: Type in the value to search
in the input column

Replace with: Type in the
substitution value.

Whole word: Select this check box
if the searched value is to be considered as whole.

Case sensitive: Select this check
box to care about the case.

Note that you cannot use regular expression in these columns.

Use advanced mode

Select this check box when the operation you want to perform
cannot be carried out through the simple mode. In the text field,
type in the regular expression as required.

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
.

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.

Replacing values and filtering columns using Map/Reduce
components

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

You can use 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.

tReplace_10.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 to be used in this scenario is the same as in the Job
described earlier, reading as follows:

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:

    tReplace_11.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 and the tHDFSOutput component writes data in that
    distribution.

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

    tReplace_12.png

  2. Click the

    tReplace_13.png

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

    tReplace_14.png

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

    tReplace_1.png

    button to manually add these schema columns; 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 the
    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 components

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

    tReplace_16.png

    This component keeps its configuration used by the original Job. It
    searches incoming entries and replaces the ones you have specified in the
    Search column with the values given in
    the Replace with column.
  2. Double-click tFilterColumns to open its
    Component view.

    tReplace_17.png

    The components keeps its schema from the original Job while the order of
    its columns stays no longer as it was rearranged in the scenario earlier and
    has automatically changed back to its original order.
    tReplace_18.png

Configuring tHDFSOutput

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

    tReplace_19.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, tFilterColumns,
    click Sync columns to retrieve the schema
    of tFilterColumns.

    tReplace_20.png

  3. In the Folder field, enter the path, or
    browse to the folder you want to write the unique entries 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.

tReplace_21.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.

tReplace properties for Apache Spark Batch

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

The Spark Batch
tReplace 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.

Simple Mode Search / Replace

Click the

tReplace_1.png

button to add as many conditions as needed. The
conditions are performed one after the other for each row.

Input column: Select the column of
the schema the search & replace is to be operated on

Search: Type in the value to search
in the input column

Replace with: Type in the
substitution value.

Whole word: Select this check box
if the searched value is to be considered as whole.

Case sensitive: Select this check
box to care about the case.

Note that you cannot use regular expression in these columns.

Advanced mode

Select this check box when the operation you want to perform
cannot be carried out through the simple mode. In the text field,
type in the regular expression as required.

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.

tReplace properties for Apache Spark Streaming

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

The Spark Streaming
tReplace 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.

Simple Mode Search / Replace

Click the

tReplace_1.png

button to add as many conditions as needed. The
conditions are performed one after the other for each row.

Input column: Select the column of
the schema the search & replace is to be operated on

Search: Type in the value to search
in the input column

Replace with: Type in the
substitution value.

Whole word: Select this check box
if the searched value is to be considered as whole.

Case sensitive: Select this check
box to care about the case.

Note that you cannot use regular expression in these columns.

Advanced mode

Select this check box when the operation you want to perform
cannot be carried out through the simple mode. In the text field,
type in the regular expression as required.

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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