tFixedFlowStreamInput | Talend Components for Jobs Help
Skip to main content Skip to complementary content
Qlik Resources
Loading SearchUnify's search If you need assistance with your product, please contact Qlik Support.
Qlik Customer Portal
Loading SearchUnify's search If you need assistance with your product, please contact Qlik Support.
Qlik Customer Portal

tFixedFlowStreamInput

Last updated: 9/30/2026
Feature in preview
Generates a stream of fixed rows.

tFixedFlowStreamInput properties for Apache Spark Structured Streaming

These properties are used to configure tFixedFlowStreamInput running in the Spark Structured Streaming Job framework.

The Spark Structured Streaming tFixedFlowStreamInput component belongs to the Misc 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
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.

  • 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.

Click Edit schema to make changes to the schema. If you make changes, the schema automatically becomes built-in.

  • 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.
Number of rows Enter the number of times to repeat each row in the dataset. The default value is 1.
Mode Select the mode to use for providing the input data:
  • Use Single Table: Enter the data in the built-in table editor, then define the data in the Values table.
  • Use Inline table: Reference a table defined in another component.
  • Use Inline Content (delimited file): Enter the data as a delimited string. Each line represents a row, and column values are separated by the field separator.
  • Use context variable (String): Use a context variable that contains the data as a string. In Variable name, enter the context variable. For example, context.VariableName.
Row separator Use this separator to split the input into rows. If you leave this field empty, the entire input is treated as one row.
Field separator For a single-column schema, the field separator is not used for splitting, and each row is returned as one field. For a multi-column schema, specify a field separator. If you set it to an empty value directly in the component, an error indicator is displayed and the Job cannot start. If an empty value is provided by a context variable, the Job fails at runtime with the following error: IllegalArgumentException: Field separator must not be empty for a multi-column schema.
Enable watermarking Select this check box to activate watermarking and select the time mode to be used:
  • Event time: Select to use a timestamp column representing when each event occurred, then select the column in the Watermark column (timestamp) drop-down list.
  • Processing time: Select to use the time when Spark reads each record.

In the Watermark delay field, specify how long Spark waits for late data before finalizing each window.

Advanced settings

Properties Description
Set the number of partitions Select this check box, then enter the number of partitions into which to dispatch the input rows. If left clear, each input row forms a partition.

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 Structured Streaming component Palette it belongs to, appears only when you are creating a Spark Structured Streaming Job.

Spark Connection

You need to use the Structured Streaming Configuration tab in the Run view to define the connection to a Spark cluster for the whole Job.

This connection is effective on a per-Job basis.

Did this page help you?

If you find any issues with this page or its content – a typo, a missing step, or a technical error – please let us know!

Leave your feedback here