# tKafkaInputAvro (deprecated) Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated) This content is deprecated. Transmits Avro-formatted messages you need to process to its following component in the Job you are designing. Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead. tKafkaInputAvro is a generic message broker that transmits messages in the AVRO format to the Job that runs transformations over these messages.This component cannot handle AVRO messages created by the avro-tools libraries. This component is not shipped with your Talend Studio by default. You need to install it using the Feature Manager. For more information, see [Installing features using the Feature Manager](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/install-features-to-talend-studio). ## tKafkaInputAvro properties for Apache Spark Streaming These properties are used to configure tKafkaInputAvro running in the Spark Streaming Job framework. The Spark Streaming tKafkaInputAvro component belongs to the Messaging family. This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric. Information noteImportant: For Spark Streaming Jobs, Talend Studio does not support a specific version of Kafka but relies on a Kafka broker version compatibility provided by Spark. The Kafka broker version supported depends on the Spark version you use. For each Spark version, Talend Studio supports the targeted Kafka broker version provided by Spark. As of today Talend Studio relies on Spark compatibility statement, and therefore supports Kafka broker version 0.10.0 onwards. For more information, see [Spark Streaming + Kafka Integration Guide](https://spark.apache.org/docs/latest/streaming-kafka-integration.html) on the official Spark documentation. ## 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. | | 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:

| | 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 checkbox 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. | | 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.

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
checkbox to display the related parameters to be defined:



For further information about how a Kafka cluster is secured with
Kerberos, see [Authenticating using
SASL](http://kafka.apache.org/documentation.html#security_sasl).

This check box is available since Kafka 0.9.0.1. | | Use Schema Registry | Select this check box to use Confluent Schema Registry and to display the
related parameters to be defined:



For more information about Schema Registry, see the [Confluent documentation](https://docs.confluent.io/platform/current/schema-registry/index.html).

This option is available when you have installed the 8.0.1-R2022-12 Talend Studio Monthly update or a later one delivered by Talend. For more information, check with your administrator. | ## Advanced settings | Properties | Description | | --- | --- | | Kafka properties | Add the Kafka consumer properties you need to customize to this table. For example, you
can set a specific *zookeeper.connection.timeout.ms* 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 http://kafka.apache.org/documentation.html#consumerconfigs. | | Use hierarchical mode | Select this check box to map the binary (including hierarchical) Avro schema to the
flat schema defined in the schema editor of the current component. If the Avro
message to be processed is flat, leave this check box clear.

Once selecting it, you need set the following parameter(s):

| ## 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 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 more information about this
implementation, see the [Direct approach](http://spark.apache.org/docs/latest/streaming-kafka-integration.html) section in the Spark documentation. | **Related concepts** * [Retrieving information from schema registry](https://help.qlik.com/talend/en-US/components/8.0/kafka/retrieving-information-from-schema-registry "This scenario explains how to retrieve flight information from schema registry using tKafkaInputAvro in your Spark Streaming Jobs.")