July 30, 2023

tExtractXMLField – Docs for ESB 7.x

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tExtractXMLField

Reads the XML structured data from an XML field and sends the data as defined in
the schema to the following component.

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

tExtractXMLField Standard properties

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

The Standard
tExtractXMLField component belongs to the Processing and the XML families.

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

Basic settings

Property type

Either Built-In or 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.

 

Built-In: No property data stored centrally.

 

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

When this file is selected, the fields that
follow are pre-filled in using fetched data.

Schema type and Edit
Schema

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

 

Built-In: You create and store the schema locally for this component
only.

 

Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

XML field

Name of the XML field to be processed.

Related topic: see
Talend Studio User
Guide.

Loop XPath query

Node of the XML tree, which the loop is based on.

Mapping

Column: reflects the schema as
defined by the Schema type field.

XPath Query: Enter the fields to be
extracted from the structured input.

Get nodes: Select this check box to
recuperate the XML content of all current nodes specified in the
Xpath query list or select the
check box next to specific XML nodes to recuperate only the content
of the selected nodes.

Limit

Maximum number of rows to be processed. If Limit is 0, no rows are
read or processed.

Die on error

Select the check box to stop the execution of the Job when an error
occurs.

Clear the check box to skip any rows on error and complete the process for
error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link.

Advanced settings

Ignore the namespaces

Select this check box to ignore namespaces when reading and
extracting the XML data.

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 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 an intermediate component. It needs an input and
an output components.

Extracting XML data from a field in a database table

This three-component scenario allows to read the XML structure included in the fields
of a database table and then extracts the data.

Procedure

  1. Drop the following components from the Palette onto the design workspace: tMysqlInput, tExtractXMLField,
    and tFileOutputDelimited.

    Connect the three components using Main
    links.
    tExtractXMLField_1.png

  2. Double-click tMysqlInput to display its
    Basic settings view and define its
    properties.

    tExtractXMLField_2.png

  3. If you have already stored the input schema in the Repository tree view, select Repository first from the Property
    Type     list and then from the Schema list to display the Repository
    Content     dialog box where you can select the relevant metadata.

    For more information about storing schema metadata in the Repository tree view, see
    Talend Studio User
    Guide    .
    If you have not stored the input schema locally, select Built-in in the Property Type
    and Schema fields and enter the database
    connection and the data structure information manually. For more information
    about tMysqlInput properties, see tMysqlInput.
  4. In the Table Name field, enter the name of
    the table holding the XML data, customerdetails in this
    example.

    Click Guess Query to display the query
    corresponding to your schema.
  5. Double-click tExtractXMLField to display its
    Basic settings view and define its
    properties.

    tExtractXMLField_3.png

  6. Click Sync columns to retrieve the schema
    from the preceding component. You can click the three-dot button next to
    Edit schema to view/modify the
    schema.

    The Column field in the Mapping table will be automatically populated with the defined
    schema.
  7. In the Xml field list, select the column from
    which you want to extract the XML data. In this example, the filed holding the
    XML data is called CustomerDetails.

    In the Loop XPath query field, enter the node
    of the XML tree on which to loop to retrieve data.
    In the Xpath query column, enter between
    inverted commas the node of the XML field holding the data you want to extract,
    CustomerName in this example.
  8. Double-click tFileOutputDelimited to display
    its Basic settings view and define its
    properties.

    tExtractXMLField_4.png

  9. In the File Name field, define or browse to
    the path of the output file you want to write the extracted data in.

    Click Sync columns to retrieve the schema
    from the preceding component. If needed, click the three-dot button next to
    Edit schema to view the schema.
  10. Save your Job and click F6 to execute
    it.
tExtractXMLField_5.png

tExtractXMLField read and extracted the clients names
under the node CustomerName of the CustomerDetails
field of the defined database table.

Extracting correct and erroneous data from an XML field in a delimited
file

This scenario describes a four-component Job that reads an XML structure from a
delimited file, outputs the main data and rejects the erroneous data.

Procedure

  1. Drop the following components from the Palette to the design workspace: tFileInputDelimited, tExtractXMLField, tFileOutputDelimited and tLogRow.

    Connect the first three components using Row
    Main     links.
    Connect tExtractXMLField to tLogRow using a Row
    Reject     link.
    tExtractXMLField_6.png

  2. Double-click tFileInputDelimited to open its
    Basic settings view and define the
    component properties.

    tExtractXMLField_7.png

  3. Select Built-in in the Schema list and fill in the file metadata manually in the
    corresponding fields.

    Click the three-dot button next to Edit
    schema     to display a dialog box where you can define the structure
    of your data.
    Click the plus button to add as many columns as needed to your data structure.
    In this example, we have one column in the schema:
    xmlStr.
    Click OK to validate your changes and close
    the dialog box.
    Note:

    If you have already stored the schema in the Metadata folder under File
    delimited    , select Repository
    from the Schema list and click the
    three-dot button next to the field to display the Repository Content dialog box where you can select the
    relevant schema from the list. Click Ok to
    close the dialog box and have the fields automatically filled in with the
    schema metadata.

    For more information about storing schema metadata in the Repository tree
    view, see
    Talend Studio User Guide    .

  4. In the File Name field, click the three-dot
    button and browse to the input delimited file you want to process,
    CustomerDetails_Error     in this example.

    This delimited file holds a number of simple XML lines separated by double
    carriage return.
    Set the row and field separators used in the input file in the corresponding
    fields, double carriage return for the first and nothing for the second in this
    example.
    If needed, set Header, Footer and Limit. None is used
    in this example.
  5. In the design workspace, double-click tExtractXMLField to display its Basic
    settings     view and define the component properties.

    tExtractXMLField_8.png

  6. Click Sync columns to retrieve the schema
    from the preceding component. You can click the three-dot button next to
    Edit schema to view/modify the
    schema.

    The Column field in the Mapping table will be automatically populated with the defined
    schema.
  7. In the Xml field list, select the column from
    which you want to extract the XML data. In this example, the filed holding the
    XML data is called xmlStr.

    In the Loop XPath query field, enter the node
    of the XML tree on which to loop to retrieve data.
  8. In the design workspace, double-click tFileOutputDelimited to open its Basic
    settings     view and display the component properties.

    tExtractXMLField_9.png

  9. In the File Name field, define or browse to
    the output file you want to write the correct data in,
    CustomerNames_right.csv in this example.

    Click Sync columns to retrieve the schema of
    the preceding component. You can click the three-dot button next to Edit schema to view/modify the schema.
  10. In the design workspace, double-click tLogRow
    to display its Basic settings view and define
    the component properties.

    Click Sync Columns to retrieve the schema of
    the preceding component. For more information on this component, see tLogRow.
  11. Save your Job and press F6 to execute it.
tExtractXMLField_10.png

tExtractXMLField reads and extracts in the output
delimited file, CustomerNames_right, the client information for
which the XML structure is correct, and displays as well erroneous data on the console
of the Run view.

tExtractXMLField MapReduce properties (deprecated)

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

The MapReduce
tExtractXMLField component belongs to the XML 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

Property type

Either Built-In or 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.

 

Built-In: No property data stored centrally.

 

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

When this file is selected, the fields that
follow are pre-filled in using fetched data.

Schema type and Edit
Schema

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

 

Built-In: You create and store the schema locally for this component
only.

 

Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

XML field

Name of the XML field to be processed.

Related topic: see
Talend Studio User
Guide.

Loop XPath query

Node of the XML tree, which the loop is based on.

Mapping

Column: reflects the schema as
defined by the Schema type field.

XPath Query: Enter the fields to be
extracted from the structured input.

Get nodes: Select this check box to
recuperate the XML content of all current nodes specified in the
Xpath query list or select the
check box next to specific XML nodes to recuperate only the content
of the selected nodes.

Die on error

Select the check box to stop the execution of the Job when an error
occurs.

Clear the check box to skip any rows on error and complete the process for
error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link.

Advanced settings

Ignore the namespaces

Select this check box to ignore namespaces when reading and
extracting the XML data.

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.

Related scenarios

No scenario is available for the Map/Reduce version of this component yet.

tExtractXMLField properties for Apache Spark Batch

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

The Spark Batch
tExtractXMLField component belongs to the XML family.

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.

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: No property data stored centrally.

 

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

When this file is selected, the fields that
follow are pre-filled in using fetched data.

Schema type and Edit
Schema

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

 

Built-In: You create and store the schema locally for this component
only.

 

Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

XML field

Name of the XML field to be processed.

Related topic: see
Talend Studio User
Guide.

Loop XPath query

Node of the XML tree, which the loop is based on.

Mapping

Column: reflects the schema as
defined by the Schema type field.

XPath Query: Enter the fields to be
extracted from the structured input.

Get nodes: Select this check box to
recuperate the XML content of all current nodes specified in the
Xpath query list or select the
check box next to specific XML nodes to recuperate only the content
of the selected nodes.

Die on error

Select the check box to stop the execution of the Job when an error
occurs.

Advanced settings

Ignore the namespaces

Select this check box to ignore namespaces when reading and
extracting the XML data.

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.

tExtractXMLField properties for Apache Spark Streaming

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

The Spark Streaming
tExtractXMLField component belongs to the XML family.

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

Basic settings

Property type

Either Built-In or 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.

 

Built-In: No property data stored centrally.

 

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

When this file is selected, the fields that
follow are pre-filled in using fetched data.

Schema type and Edit
Schema

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

 

Built-In: You create and store the schema locally for this component
only.

 

Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

XML field

Name of the XML field to be processed.

Related topic: see
Talend Studio User
Guide.

Loop XPath query

Node of the XML tree, which the loop is based on.

Mapping

Column: reflects the schema as
defined by the Schema type field.

XPath Query: Enter the fields to be
extracted from the structured input.

Get nodes: Select this check box to
recuperate the XML content of all current nodes specified in the
Xpath query list or select the
check box next to specific XML nodes to recuperate only the content
of the selected nodes.

Die on error

Select the check box to stop the execution of the Job when an error
occurs.

Clear the check box to skip any rows on error and complete the process for
error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link.

Advanced settings

Ignore the namespaces

Select this check box to ignore namespaces when reading and
extracting the XML data.

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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