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Serializer and Formatter

In this document, we describe how to use Avro with the Kafka Java client and console tools.

Assuming that you have the Schema Registry source code checked out at /tmp/schema-registry, the following is how you can obtain all needed jars.

mvn package

The jars can be found in



You can plug KafkaAvroSerializer into KafkaProducer to send messages of Avro type to Kafka. Currently, we support primitive types of null, Boolean, Integer, Long, Float, Double, String, byte[], and complex type of IndexedRecord. Sending data of other types to KafkaAvroSerializer will cause a SerializationException. Typically, IndexedRecord will be used for the value of the Kafka message. If used, the key of the Kafka message is often of one of the primitive types. When sending a message to a topic t, the Avro schema for the key and the value will be automatically registered in the Schema Registry under the subject t-key and t-value, respectively, if the compatibility test passes. The only exception is that the null type is never registered in the Schema Registry.

In the following example, we send a message with key of type string and value of type Avro record to Kafka. A SerializationException may occur during the send call, if the data is not well formed.

import org.apache.avro.Schema;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.util.Properties;

Properties props = new Properties();
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
props.put("schema.registry.url", "http://localhost:8081");
KafkaProducer producer = new KafkaProducer(props);

String key = "key1";
String userSchema = "{\"type\":\"record\"," +
                    "\"name\":\"myrecord\"," +
Schema.Parser parser = new Schema.Parser();
Schema schema = parser.parse(userSchema);
GenericRecord avroRecord = new GenericData.Record(schema);
avroRecord.put("f1", "value1");

ProducerRecord<Object, Object> record = new ProducerRecord<>("topic1", key, avroRecord);
try {
} catch(SerializationException e) {
  // may need to do something with it

You can plug in KafkaAvroDecoder to KafkaConsumer to receive messages of any Avro type from Kafka. In the following example, we receive messages with key of type string and value of type Avro record from Kafka. When getting the message key or value, a SerializationException may occur if the data is not well formed.

import org.apache.avro.generic.IndexedRecord;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
import io.confluent.kafka.serializers.KafkaAvroDecoder;
import kafka.message.MessageAndMetadata;
import kafka.utils.VerifiableProperties;
import org.apache.kafka.common.errors.SerializationException;
import java.util.*;

Properties props = new Properties();
props.put("zookeeper.connect", "localhost:2181");
props.put("", "group1");
props.put("schema.registry.url", "http://localhost:8081");

String topic = "topic1";
Map<String, Integer> topicCountMap = new HashMap<>();
topicCountMap.put(topic, new Integer(1));

VerifiableProperties vProps = new VerifiableProperties(props);
KafkaAvroDecoder keyDecoder = new KafkaAvroDecoder(vProps);
KafkaAvroDecoder valueDecoder = new KafkaAvroDecoder(vProps);

ConsumerConnector consumer = kafka.consumer.Consumer.createJavaConsumerConnector(new ConsumerConfig(props));

Map<String, List<KafkaStream<Object, Object>>> consumerMap = consumer.createMessageStreams(
    topicCountMap, keyDecoder, valueDecoder);
KafkaStream stream = consumerMap.get(topic).get(0);
ConsumerIterator it = stream.iterator();
while (it.hasNext()) {
  MessageAndMetadata messageAndMetadata =;
  try {
    String key = (String) messageAndMetadata.key();
    IndexedRecord value = (IndexedRecord) messageAndMetadata.message();

  } catch(SerializationException e) {
    // may need to do something with it

We recommend users use the new producer in org.apache.kafka.clients.producer.KafkaProducer. If you are using a version of Kafka older than, you can plug KafkaAvroEncoder into the old producer in kafka.javaapi.producer. However, there will be some limitations. You can only use KafkaAvroEncoder for serializing the value of the message and only send value of type Avro record. The Avro schema for the value will be registered under the subject recordName-value, where recordName is the name of the Avro record. Because of this, the same Avro record type shouldn’t be used in more than one topic.


You can use kafka-avro-console-producer and kafka-avro-console-consumer respectively to send and receive Avro data in JSON format from the console. Under the hood, they use AvroMessageReader and AvroMessageFormatter to convert between Avro and JSON.

To run the Kafka console tools, first make sure that Zookeeper, Kafka and the Schema Registry server are all started. In the following examples, we use the default value of the Schema Registry URL. You can configure that by supplying

--property schema.registry.url=address of your schema registry

in the commandline arguments of kafka-avro-console-producer and kafka-avro-console-consumer.

In the following example, we send Avro records in JSON as the message value (make sure there is no space in the schema string).

bin/kafka-avro-console-producer --broker-list localhost:9092 --topic t1 \
  --property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}'

In the shell, type in the following.
  {"f1": "value1"}

In the following example, we read the value of the messages in JSON.

bin/kafka-avro-console-consumer --topic t1 \
  --zookeeper localhost:2181

You should see following in the console.
  {"f1": "value1"}

In the following example, we send strings and Avro records in JSON as the key and the value of the message, respectively.

bin/kafka-avro-console-producer --broker-list localhost:9092 --topic t2 \
  --property parse.key=true \
  --property key.schema='{"type":"string"}' \
  --property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"f1","type":"string"}]}'

In the shell, type in the following.
  "key1" \t {"f1": "value1"}

In the following example, we read both the key and the value of the messages in JSON,

bin/kafka-avro-console-consumer --topic t2 \
  --zookeeper localhost:2181 \
  --property print.key=true

You should see following in the console.
   "key1" \t {"f1": "value1"}

Wire Format

Most users can use the serializers and formatter directly and never worry about the details of how Avro messages are mapped to bytes. However, if you’re working with a language that Confluent has not developed serializers for, or simply want a deeper understanding of how the Confluent Platform works, you may need more detail on how data is mapped to low-level bytes.

The wire format currently has only a couple of components:

Bytes Area Description
0 Magic Byte Confluent serialization format version number; currently always 0.
1-4 Schema ID 4-byte schema ID as returned by the Schema Registry
5-… Data Avro serialized data in Avro’s binary encoding. The only exception is raw bytes, which will be written directly without any special Avro encoding.

Note that all components are encoded with big-endian ordering, i.e. standard network byte order.

Compatibility Guarantees

The serialization format used by Confluent Platform serializers is guaranteed to be stable over major releases without any changes without advanced warning. This is critical because the serialization format affects how keys are mapped across partitions. Because many applications depend on keys with the same logical format being routed to the same physical partition, it is usually important that the physical byte format of serialized data does not change unexpectedly for an application. Even the smallest modification can result in records with the same logical key being routed to different partitions because messages are routed to partitions based on the hash of the key.

In order to ensure there is no variation even as the serializers are updated with new formats, the serializers are very conservative when updating output formats. To ensure stability for clients, Confluent Platform and its serializers ensure the following:

  • The format (including magic byte) will not change without significant warning over multiple Confluent Platform major releases. Although the default may eventually be changed infrequently to allow adoption of new features by default, this will be done very conservatively and with at least one major release between changes, during which the relevant changes will result in user-facing warnings so no users will be caught off guard by the need for transition. Very significant, compatibility-affecting changes will guarantee at least 1 major release of warning and 2 major releases before an incompatible change will be made.
  • Within the version specified by the magic byte, the format will never change in any backwards-incompatible way. Any changes made will be fully backward compatible with documentation in release notes and at least one version of warning will be provided if it introduces a new serialization feature which requires additional downstream support.
  • Deserialization will be supported over multiple major releases. This does not guarantee indefinite support, but support for deserializing any earlier formats will be supported indefinitely as long as there is no notified reason for incompatibility.

For more information about compatibility or support, reach out to the community mailing list.