tKinesisInput - 6.1

Talend Components Reference Guide

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The streaming version of this component is available in the Palette of the studio on the condition that you have subscribed to Talend Real-time Big Data Platform or Talend Data Fabric.


tKinesisInput consumes data from a given Amazon Kinesis stream (an ordered sequence of data records), constructs an RDD out of this data and sends the RDD to its following components.


The tKinesisInput component acts as consumer of an Amazon Kinesis stream to pull messages from this Kinesis stream.

tKinesisInput properties in Spark Streaming Jobs


The streaming version of this component is available in the Palette of the studio on the condition that you have subscribed to Talend Real-time Big Data Platform or Talend Data Fabric.

Component Family

Messaging / Kinesis


Basic settings

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. The schema is either Built-In or stored remotely in the Repository.

The schema of this component is read-only. You can click Edit schema to view the schema.

This read-only payload column is used to carry the body of the Kinesis message to be processed.

The input message body can use very different data formats. For example, if its format is JSON, you need to use tExtractJSONField following tKinesisInput to extract the data to be processed from this body.


Access key

Enter the Access Key ID that uniquely identifies an AWS Account. For further information about how to get your Access Key and Secret Key, see Getting Your AWS Access Keys.


Secret key

Enter the Secret Access Key, constituting the security credentials in combination with the access Key.

To enter the password, click the [...] button next to the password field, and then in the pop-up dialog box enter the password between double quotes and click OK to save the settings.


Stream name

Enter the name of the Kinesis stream you want tKinesisInput to pull data from.


Endpoint URL

Enter the endpoint of the Kinesis service to be used. For example, More valid Kinesis endpoint URLs can be found at


Explicitly set authentication parameters

Select this check box to use the explicit authentication mechanism to connect to Kinesis. Note that this mechanism is supported by Spark V1.4+ only.

Since this security mechanism requires the AWS Region parameter to be explicitly set, you need to enter the region value to be used in the Region field that is displayed. For example, us-west-2.

It is recommended to use the explicit authentication to gain better security when the Spark version you are using supports this mechanism. With this check box selected, the access credentials are provided directly to Kinesis.

While if you leave this check box clear, an older authentication mechanism is used. This way, the access credentials are used by Spark as context variables for Kinesis connection.

Advanced settings

Checkpoint interval

Enter the time interval (in millisecond) at the end of which tKinesisInput saves the position of its read in the Kinesis stream.

Data records in a Kinesis stream are grouped into partitions (shards in terms of Kinesis) and indexed with sequence numbers. A sequence number uniquely identifies the position of a record. For further information about the terms used by Amazon in Kinesis, see


Initial position stream

Select the starting position to read data from the stream in the absence of the Kinesis checkpoint information.

  • Start with the oldest data: starts from the beginning of the stream within the limit of 24 hours.

  • Start after the most recent data: starts at the position after the latest data of the stream.


Storage level

Select how you want the received data to be cached. For further information about the different levels, see

Usage in Spark Streaming Jobs

In a Talend Spark Streaming Job, it is used as a start component and requires an output link. The other components used along with it must be Spark Streaming components, too. They generate native Spark code that can be executed directly in a Spark cluster.

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 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.


If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at

Spark Connection

You need to use the Spark Configuration tab in the Run view to define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.


Due to license incompatibility, one or more JARs required to use this component are not provided. You can install the missing JARs for this particular component by clicking the Install button on the Component tab view. You can also find out and add all missing JARs easily on the Modules tab in the Integration perspective of your studio. For details, see or the section describing how to configure the Studio in the Talend Installation Guide.

Related scenarios

No scenario is available for the Spark Streaming version of this component yet.