# tFileStreamInputPositional (deprecated) Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated) This content is deprecated. Listens on a given directory for new files, reads data from them row by row and extracts fields based on a specific pattern. Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead. ## tFileStreamInputPositional properties for Apache Spark Streaming These properties are used to configure tFileStreamInputPositional running in the Spark Streaming Job framework. The Spark Streaming tFileStreamInputPositional component belongs to the File family. The streaming version of this component is available in Talend Real-Time Big Data Platform and in 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.

| | 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 tFileInputPositional 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. | | Customize | Select this check box to customize the data format of the
positional file and define the table columns:

Column: Select the column you
want to customize.

Size: Enter the column
size.

Padding char: Enter, between double quotation
marks, the padding character you need to remove from the field. A space
by default.

Alignment: Select the appropriate
alignment parameter. | | Pattern | Enter between double quotes the length values separated by commas, interpreted as a
string. Make sure the values entered in this field are consistent
with the schema defined. | | Header | Enter the number of rows to be skipped in the beginning of file.

For example, enter *0* to ignore
no rows for the data without header and set *1* for the data with header at the first row. | | Skip empty rows | Select this checkbox to skip the empty rows. | ## Advanced settings | Properties | Description | | --- | --- | | 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. | | Advanced separator (for number) | 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 (*.*). | | Trim columns | Select this check box to remove the leading and trailing whitespaces from all
columns. When this check box is cleared, the Check column to
trim table is displayed, which lets you select particular columns to
trim. | ## 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 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 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. |