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tKafkaInput properties for Apache Spark Streaming

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

The Spark Streaming tKafkaInput component belongs to the Messaging family.

This component is available in Talend Real Time Big Data Platform and Talend Data Fabric.

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. When you create a Spark Job, avoid the reserved word line when naming the fields.

Note that the schema of this component is read-only. It stores the message body sent from the message producer.

Output type

Select the type of the data to be sent to the next component.

Typically, using String is recommended, because tKafkaInput can automatically translate the Kafka byte[] messages into strings to be processed by the Job. However, in case that the format of Kafka messages is not known to tKafkaInput, such as Protobuf, you can select byte and then use a Custom code component such as tJavaRow to deserialize the messages into strings so that the other components of the same Job can process these messages.

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 (,).

Starting offset

Select the starting point from which the messages of a topic are consumed.

In Kafka, the sequential ID number of a message is called offset. From this list, you can select From beginning to start consumption from the oldest message of the entire topic, or select From latest to start from the latest message that has been consumed by the same consumer group and of which the offset is tracked by Spark within Spark checkpoints.

Note that in order to enable the component to remember the position of a consumed message, you need to activate the Spark Streaming checkpointing in the Spark Configuration tab in the Run view of the Job.

Each consumer group has its own counter to remember the position of a message it has consumed. For this reason, once a consumer group starts to consume messages of a given topic, a consumer group recognizes the latest message only with regard to the position where this group stops the consumption, rather than to the entire topic. Based on this principle, the following behaviors can be expected:

  • A topic has for example 100 messages. If a consumer group has stopped the consumption at the message of the offset 50, then when you select From latest, the same consumer group restarts from the offset 51.

  • If you create a new consumer group or reset an existing consumer group, which, in either case, means this group has not consumed any message of this topic, then when you start it from latest, this new group starts and waits for the offset 101.

Topic name

Enter the name of the topic from which tKafkaInput receives the feed of messages.

Group ID

Enter the name of the consumer group to which you want the current consumer (the tKafkaInput component) to belong.

This consumer group will be created at runtime if it does not exist at that moment.

This property is available only when you are using Spark 2.0 or the Hadoop distribution to be used is running Spark 2.0. If you do not know the Spark version you are using, ask the administrator of your cluster for details.

Set number of records per second to read from each Kafka partition

Enter this number within double quotation marks to limit the size of each batch to be sent for processing.

For example, if you put 100 and the batch value you define in the Spark configuration tab is 2 seconds, the size from a partition for each batch is 200 messages.

If you leave this check box clear, the component tries to read all the available messages in one second into one single batch before sending it, potentially resulting in Job that stops responding in case of a huge quantity of messages.


Select this check box 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.

This property is available only when you are using Spark 2.0 or the Hadoop distribution to be used is running Spark 2.0. If you do not know the Spark version you are using, ask the administrator of your cluster for details.

The TrustStore file and any used KeyStore file must be stored locally on every single Spark node that is hosting a Spark executor.

Use Kerberos authentication

If the Kafka cluster to be used is secured with Kerberos, select this check box to display the related parameters to be defined:

  • JAAS configuration path: enter the path, or browse to the JAAS configuration file to be used by the Job to authenticate as a client to Kafka.

    This JAAS file describes how the clients, the Kafka-related Jobs in terms of Talend , can connect to the Kafka broker nodes, using either the kinit mode or the keytab mode. It must be stored in the machine where these Jobs are executed.

    Talend , Kerberos or Kafka does not provide this JAAS file. You need to create it by following the explanation in Configuring Kafka client depending on the security strategy of your organization.

  • Kafka brokers principal name: enter the primary part of the Kerberos principal you defined for the brokers when you were creating the broker cluster. For example, in this principal kafka/, the primary part to be used to fill in this field is kafka.

  • Set kinit command path: Kerberos uses a default path to its kinit executable. If you have changed this path, select this check box and enter the custom access path.

    If you leave this check box clear, the default path is used.

  • Set Kerberos configuration path: Kerberos uses a default path to its configuration file, the krb5.conf file (or krb5.ini in Windows) for Kerberos 5 for example. If you have changed this path, select this check box and enter the custom access path to the Kerberos configuration file.

    If you leave this check box clear, a given strategy is applied by Kerberos to attempt to find the configuration information it requires. For details about this strategy, see the Locating the krb5.conf Configuration File section in Kerberos requirements.

For further information about how a Kafka cluster is secured with Kerberos, see Authenticating using SASL.

This check box is available since Kafka

Advanced settings

Kafka properties

Add the Kafka consumer properties you need to customize to this table. For example, you can set a specific value to avoid ZkTimeoutException.

For further information about the consumer properties you can define in this table, see the section describing the consumer configuration in Kafka's documentation in


Select the encoding from the list or select Custom and define it manually.

This encoding is used by tKafkaInput to decode the input messages.


Usage rule

This component is used as a start component and requires an output link.

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.

In the implementation of the current component in Spark, the Kafka offsets are automatically managed by Spark itself, that is to say, instead of being committed to Zookeeper or Kafka, the offsets are tracked within Spark checkpoints. For further information about this implementation, see the Direct approach section in the Spark documentation:

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.

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