July 30, 2023

tWriteXMLFields – Docs for ESB 7.x


Converts records into byte arrays.

tWriteXMLFields generates strings or
byte arrays to be used by the output components, such as tKafkaOutput requiring serialized data while tJMSOutput requiring strings. tWriteXMLFields embeds the incoming data into a single XML column.

tWriteXMLFields properties for Apache Spark Streaming

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

The Spark Streaming
tWriteXMLFields component belongs to the Processing family.

The streaming version of this component is available in Talend Real Time Big Data Platform and in
Talend Data Fabric.

Basic settings

Output type

Select the type of the data to be outputted into the target file. The data is
byte arrays if you select byte.

Schema and Edit

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

The schema of this component is read-only. You can click
Edit schema to view the schema.

When the output type is String, the read-only single column is messageContent. This column is used to provide strings to the output components
such as tJMSOutput.

When the output type is byte, the read-only single column is serializedValue. This column is used to provide byte arrays to the output
components such as tKafkaOutput.

The output schema and its read-only column can be seen by
clicking the Row > Output link to the
component that follows in the same Job. The schema is displayed in the Basic settings tab of the Component view

Row tag

Specify the tag that will wrap data and structure per row.

Custom encoding

You may encounter encoding issues when you process the stored data. In that
situation, select this check box to display the Encoding list.

Select the encoding from the list or select Custom
and define it manually. This field is compulsory for database data handling. The
supported encodings depend on the JVM that you are using. For more information, see

Advanced settings

Root tags

Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.).

Output format

Define the output format.

  • Column: The columns retrieved
    from the input schema.

  • As attribute: select check box
    for the column(s) you want to use as attribute(s) of the parent
    element in the XML output.


If the same column is selected in both the Output format table as an attribute
and in the Use dynamic grouping
setting as the criterion for dynamic grouping, only the dynamic
group setting will take effect for that column.

Use schema column name: By
default, this check box is selected for all columns so that the
column labels from the input schema are used as data wrapping tags.
If you want to use a different tag than from the input schema for
any column, clear this check box for that column and specify a tag
label between quotation marks in the Label field.

Use dynamic grouping

Select this check box if you want to dynamically group the output
columns. Click the plus button to add one ore more grouping criteria
in the Group by table.

Column: Select a column you want
to use as a wrapping element for the grouped output rows.

Attribute label: Enter an
attribute label for the group wrapping element, between quotation


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
integration Jobs.

Spark Connection

In the Spark
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

    • When using HDInsight, specify the blob to be used for Job
      deployment in the Windows Azure Storage
      area in the Spark

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
    • 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

    If you are using Databricks without any configuration component present
    in your Job, your business data is written directly in DBFS (Databricks

This connection is effective on a per-Job basis.

Related scenarios

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

Document get from Talend https://help.talend.com
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