# tFixedFlowInput properties for Apache Spark Streaming (deprecated) Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated) This content is deprecated. These properties are used to configure tFixedFlowInput running in the Spark Streaming Job framework. Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead. The Spark Streaming tFixedFlowInput component belongs to the Misc family. This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | Schema and Edit
Schema | A schema is a row description, it defines the number of fields that
will be processed and passed on to the next component. The schema is
either built-in or remote in the Repository.

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: The schema will be created and
stored locally for this component only. For more information about a component
schema in its Basic settings tab, see [Basic settings tab](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/basic-settings-tab).

Repository: You have already created the
schema and stored it in the Repository, hence can be reused in various projects
and Job designs. For more information about a component schema in its
Basic settings tab, see [Basic settings tab](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/basic-settings-tab). | | Mode | From the three options, select the mode that you want to use.

Use Single Table : Enter the data that
you want to generate in the relevant value field.

Use Inline Table : Add the row(s) that
you want to generate.

Use Inline Content : Enter the data
that you want to generate, separated by the separators that you have
already defined in the Row and
Field Separator fields. | | Number of rows | Enter the number of lines to be generated. | | Input repetition interval | Enter the time interval in millisecond at the end of which the input
data is sent to the following component another time.

This allows you to generate a stream of data flow. | | Values | Between inverted commas, enter the values corresponding to the
columns you defined in the schema dialog box using the Edit schema button. | ## 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. |