# tFileInputXML properties for Apache Spark Batch These properties are used to configure tFileInputXML running in the Spark Batch Job framework. The Spark Batch tFileInputXML component belongs to the File family. The component in this framework is available in all Talend products with Big Data and Talend Data Fabric. ## Basic settings | 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 further information about the Hadoop
Cluster node, see the Getting Started Guide. | | 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:

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

If the path you set points to a folder, this component will
read all of the files stored in that folder, for example,
/user/talend/in; if sub-folders exist, the sub-folders are automatically
ignored unless you define the property
spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive to be
true in the Advanced properties table in the
Spark configuration tab.



If you want to specify more than one files or directories in this
field, separate each path using a comma (,).

If the file to be read is a compressed one, enter the file name
with its extension; then ttFileInputXML automatically decompresses it at
runtime. The supported compression formats and their corresponding
extensions are:



The button for browsing does not work with the Spark Local mode; if you are using the other Spark Yarn modes that the Studio supports with your distribution, ensure that
you have correctly configured the connection in a configuration component in the same
Job, such as tHDFSConfiguration. Use the
configuration component depending on the filesystem to be used. | | Element to extract | Enter the element from which you need to read the contents and the
child elements of the input XML data.

The element defined in this field is used at the root node of any
XPath specified within this component. This element helps define the
atomic units of the XML data to be used so that however big the
original document is or wherever the input is split, the rows within
this element can be correctly distributed to the mapper
tasks.

Note that any content outside this element is ignored and the
child elements of this element cannot contain this element
itself. | | Loop XPath query | Node of the tree, which the loop is based on.

Note its root is the element you have defined in the Element to extract field. | | Mapping | Column: Columns to map. They
reflect the schema as defined in 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. These nodes are important when the output
flow from this component needs to use the XML structure, for
example, the Document data
type.

For further information about the Document type, see
*Talend Studio User
Guide*. | | Die on error | Select the check box to stop the execution of the Job when an error
occurs. | ## Advanced settings | Set minimum partitions | Select this check box to control the number of partitions to be created from the input
data over the default partitioning behavior of Spark.

In the displayed field, enter, without quotation marks, the minimum number of partitions
you want to obtain.

When you want to control the partition number, you can generally set at least as many partitions as
the number of executors for parallelism, while bearing in mind the available memory and the
data transfer pressure on your network. | | --- | --- | | 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
[https://docs.oracle.com](https://docs.oracle.com/). | ## Usage | **Usage rule** | This component is used as a start component and requires an
output 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:



This connection is effective on a per-Job basis. |