# tFixedFlowInput properties for Apache Spark Batch These properties are used to configure tFixedFlowInput running in the Spark Batch Job framework. The Spark Batch tFixedFlowInput component belongs to the Misc family. The component in this framework is available in all Talend products with Big Data and Talend Data Fabric. ## Basic settings | 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. Related topic: see
*Talend Studio User Guide*. | | | Repository: You have already created
the schema and stored it in the Repository, hence can be reused in
various projects and Job designs. Related topic: see *Talend Studio User Guide*. | | 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. | | Values | Between inverted commas, enter the values corresponding to the columns
you defined in the schema dialog box via the Edit
schema button. | ## Advanced settings | Set the number of partitions | Select this check box and then enter the number of partitions into
which you want to dispatch the input rows.

If you leave this check box clear, each input row forms a partition.
For example, with *5* in the Number of rows field, each row is handled as
one partition and thus they make 5 partitions in total. | | --- | --- | ## 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:

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 Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
- 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. |