# tFileOutputXML properties for Apache Spark Batch These properties are used to configure tFileOutputXML running in the Spark Batch Job framework. The Spark Batch tFileOutputXML component belongs to the File and the XML families. The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | Define a storage configuration
component | Select the configuration component to be used to provide the configuration
information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local
system.

The configuration component to be used must be present in the same Job.
For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write
the result in a given HDFS system. | | Property type |

Either Built-In or Repository.
Built-In: No property data stored centrally.
Repository: Select the repository file where the properties are stored.

The properties are stored centrally under the Hadoop Cluster node of the Repository tree.

The fields that come after are pre-filled in using the fetched data.

For more information about the Hadoop Cluster node, see Managing Hadoop connection metadata.

| | ![](https://help.qlik.com/talend/en-US/components/8.0/Resources/images/Save_Icon.png) | Click this icon to open a database connection wizard and store the
database connection parameters you set in the component Basic
settings view.

For more information about setting up and storing database connection
parameters, see [Centralizing database metadata](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/centralizing-database-metadata). | | Row tag | Specify the tag that will wrap data and structure per row. | | 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.

| | Folder | Browse to, or enter the path pointing to the data to be used in the file system.

This path must point to a folder rather than a file.

The button for browsing does not work with the Spark Local mode; if you are using the other Spark Yarn modes that Talend Studio
supports with your distribution, ensure that you have properly configured the connection
in a configuration component in the same Job. Use the configuration component depending
on the filesystem to be used. | | Action | Select an operation for writing data:

Create: Creates a file and write data
in it.

Overwrite: Overwrites the file
existing in the directory specified in the Folder field.

When you want to sort the rows, row order is not guaranteed with this
component. | | Compress the data | Select the Compress the data check box to compress the
output data. | ## Advanced settings | Properties | Description | | --- | --- | | Root tags | Specify one or more root tags to wrap the whole output file structure and data. The
default root tag is *root*. | | 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.

Information noteNote:

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
marks. | | Custom encoding | 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
[https://docs.oracle.com](https://docs.oracle.com/). | | Advanced separator (for numbers) | Select this check box to modify the separators used for
numbers:

Thousands separator: define
separators for thousands.

Decimal separator: define
separators for decimals. | | Use local timezone for date | Select this check box to use the local date of the machine in which your Job is executed. If leaving this check box clear, UTC is automatically used to format the Date-type data. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used as an end component and requires an input link.

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 on-premises 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 Apache Spark Batch or tS3Configuration Apache Spark Batch.

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. |