tKafkaConfiguration properties for Apache Spark Structured Streaming | Talend Components for Jobs Help
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tKafkaConfiguration properties for Apache Spark Structured Streaming

Last updated: 9/30/2026

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

The Spark Structured Streaming tKafkaConfiguration 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
Broker list

Enter the addresses of the broker nodes of the Kafka cluster to be used.

The form of this address should be hostname:port. This information is the name and the port of the hosting node in this Kafka cluster.

If you need to specify several addresses, separate them using a comma (,).

Use SSL/TLS

Select this checkbox to enable the SSL or TLS encrypted connection.

Then you need to use the tSetKeystore component in the same Job to specify the encryption information.

Information noteRestriction: In the Preview version of Spark Structured Streaming, SSL/TLS is not supported because the tSetKeystore component is not implemented, even though the Use SSL/TLS check box is displayed.
Use Schema Registry Select this check box to use Confluent Schema Registry and configure the following parameters:
  • URL: enter the Schema Registry instance URL.
  • Basic authentication: select this check box and enter your credentials in the Username and Password fields.
  • Use the keystore of Kafka broker: select this check box to enable SSL or TLS encryption using the same tSetKeystore component as the Kafka broker. This check box is available when you select Use SSL/TLS and clear Set schema registry keystore.
  • Set schema registry keystore: select this check box to enable SSL or TLS encryption, then use the tSetKeystore component in the same Job to specify the encryption information.

For more information about Schema Registry, see the Confluent documentation.

Advanced settings

Properties Description
Connection pool

In this area, you configure, for each Spark executor, the connection pool used to control the number of connections that stay open simultaneously. The default values given to the following connection pool parameters are good enough for most use cases.

  • Max total number of connections: enter the maximum number of connections (idle or active) that are allowed to stay open simultaneously.

    The default number is 8. If you enter -1, you allow unlimited number of open connections at the same time.

  • Max waiting time (ms): enter the maximum amount of time at the end of which the response to a demand for using a connection should be returned by the connection pool. By default, it is -1, that is to say, infinite.

  • Min number of idle connections: enter the minimum number of idle connections (connections not used) maintained in the connection pool.

  • Max number of idle connections: enter the maximum number of idle connections (connections not used) maintained in the connection pool.

Information noteNote: You can configure the connection pool only if the Job contains one or more tKafkaOutput components.
Evict connections

Select this check box to define criteria to destroy connections in the connection pool. The following fields are displayed once you have selected it.

  • Time between two eviction runs: enter the time interval (in milliseconds) at the end of which the component checks the status of the connections and destroys the idle ones.

  • Min idle time for a connection to be eligible to eviction: enter the time interval (in milliseconds) at the end of which the idle connections are destroyed.

  • Soft min idle time for a connection to be eligible to eviction: this parameter works the same way as Min idle time for a connection to be eligible to eviction but it keeps the minimum number of idle connections, the number you define in the Min number of idle connections field.

Usage

Usage guidance Description
Usage rule

This component works standalone to create the Kafka connection that other Kafka components in the Job can reuse.

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.

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