# tKinesisInput properties for Apache Spark Structured Streaming [Preview](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/Introduction/feature-preview-program-overview.htm) Use these properties to configure tKinesisInput running in the Spark Structured Streaming Job framework. The Spark Structured Streaming tKinesisInput component belongs to the Messaging 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.

| | Configuration component | Select the tKinesisConfiguration component to use for
the Kinesis connection settings and default options. | | Record format | Select how to read the Kinesis payload:

String: reads the payload as plain text.
Avro: reads and deserializes the payload as Avro data.

| | Include metadata | Select this check box to include Kinesis metadata columns in the output
schema, such as stream name, partition key, sequence number, and arrival
timestamp. | | Stream name | Enter the name of the Kinesis stream you want tKinesisInput to pull data from. | | Starting Position | Select where to start reading when no previous checkpoint is available:

Latest: starts after the most recent records and reads only new incoming data.
Earliest: starts from the oldest available records in the stream.
At Timestamp: starts from records at or after the specified timestamp. Enter the timestamp value in the field that appears.

| | 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 | | --- | --- | | Advanced options | Add Kinesis reader options as key-value pairs in the
Option and Value table.
Use these options to override the default options inherited from the selected
tKinesisConfiguration component for this
tKinesisInput component only.

For example, set kinesis.maxFetchRecordsPerShard to control
how many records are fetched from each shard per trigger. | ## Usage | Usage guidance | Description | | --- | --- | | Usage rule | This component is used as a start component and requires an output link.

At runtime, this component keeps listening to the stream and reads new messages once they
are buffered in this stream.

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. |