# tFileInputFullRow properties for Apache Spark Batch These properties are used to configure tFileInputFullRow running in the Spark Batch Job framework. The Spark Batch tFileInputFullRow component belongs to the File family. 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.

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

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

| | 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 you make changes, the schema automatically becomes built-in.
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.

Depending on the filesystem to be used, properly configure the corresponding configuration component placed in your Job, for example, a tHDFSConfiguration component for HDFS, a tS3Configuration component for S3 and a tAzureFSConfiguration for Azure Storage and Azure Data Lake Storage.

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 tHDFSFullRow automatically decompresses it at
runtime. The supported compression formats and their corresponding
extensions are:

DEFLATE: *.deflate
gzip: *.gz
bzip2: *.bz2
LZO: *.lzo

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. | | Die on error | Select the checkbox to stop the execution of the Job when an error
occurs. | | Row separator | The separator used to identify the end of a row. | | Header | Enter the number of rows to be skipped in the beginning of file. | | Skip empty rows | Select this checkbox to skip the empty rows. | ## Advanced settings | Properties | Description | | --- | --- | | 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.

Then select the encoding to be used from the list or select
Custom and define it
manually. | ## Usage | Usage guidance | Description | | --- | --- | | **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:

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. |