Kafka Streams Data Types and Serialization

Every Kafka Streams application must provide SerDes (Serializer/Deserializer) for the data types of record keys and record values (e.g. java.lang.String or Avro objects) to materialize the data when necessary. Operations that require such SerDes information include: stream(), table(), to(), repartition(), groupByKey(), groupBy().

You can provide SerDes by using either of these methods:

• By setting default SerDes via a Properties instance.
• By specifying explicit SerDes when calling the appropriate API methods, thus overriding the defaults.

You can configure Java streams applications to deserialize and ingest data in multiple ways, including Kafka console producers, JDBC source connectors, and Java client producers. For full code examples, see Pipelining with Kafka Connect and Kafka Streams.

Configuring SerDes

SerDes specified in the Streams configuration via the Properties config are used as the default in your Kafka Streams application.

import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.StreamsConfig;

Properties settings = new Properties();
// Default serde for keys of data records (here: built-in serde for String type)
settings.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
// Default serde for values of data records (here: built-in serde for Long type)
settings.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.Long().getClass().getName());

Note

If a Serde is specified via Properties, the Serde class cannot have generic types, i.e., a class MySerde<T extends Number> implements Serde<T> cannot be used. This implies that you cannot use any Serde that is created via Serdes.serdeFrom(Serializer<T>, Deserializer<T>). Only fully typed Serde classes like MySerde implements Serde<MyCustomType> are supported due to Java type erasure.

Overriding default SerDes

You can also specify SerDes explicitly by passing them to the appropriate API methods, which overrides the default serde settings:

import org.apache.kafka.common.serialization.Serde;
import org.apache.kafka.common.serialization.Serdes;

final Serde<String> stringSerde = Serdes.String();
final Serde<Long> longSerde = Serdes.Long();

// The stream userCountByRegion has type `String` for record keys (for region)
// and type `Long` for record values (for user counts).
KStream<String, Long> userCountByRegion = ...;
userCountByRegion.to("RegionCountsTopic", Produced.with(stringSerde, longSerde));

If you want to override serdes selectively, i.e., keep the defaults for some fields, then don’t specify the serde whenever you want to leverage the default settings:

import org.apache.kafka.common.serialization.Serde;
import org.apache.kafka.common.serialization.Serdes;

// Use the default serializer for record keys (here: region as String) by not specifying the key serde,
// but override the default serializer for record values (here: userCount as Long).
final Serde<Long> longSerde = Serdes.Long();
KStream<String, Long> userCountByRegion = ...;
userCountByRegion.to("RegionCountsTopic", Produced.valueSerde(Serdes.Long()));

Note

If some of your incoming records are corrupted or ill-formatted, they will cause the deserializer class to report an error. Since 4.0.0 release we have introduced an org.apache.kafka.streams.errors.DeserializationExceptionHandler interface which allows you to customize how to handle such records. The customized implementation of the interface can be specified via the StreamsConfig. For more details, please feel free to read the Failure and exception handling FAQ

Available SerDes

Primitive and basic types

Apache Kafka® includes several built-in serde implementations for Java primitives and basic types such as byte[] in its kafka-clients Maven artifact:

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>6.1.15-ccs</version>
</dependency>

This artifact provides the following serde implementations under the package org.apache.kafka.common.serialization, which you can leverage when e.g., defining default serializers in your Streams configuration.

Data type	Serde
byte[]	Serdes.ByteArray(), Serdes.Bytes() (see tip below)
ByteBuffer	Serdes.ByteBuffer()
Double	Serdes.Double()
Integer	Serdes.Integer()
Long	Serdes.Long()
String	Serdes.String()
UUID	Serdes.UUID()

Tip

Bytes is a wrapper for Java’s byte[] (byte array) that supports proper equality and ordering semantics. You may want to consider using Bytes instead of byte[] in your applications.

You would use the built-in SerDes as follows, using the example of the String serde:

// When configuring the default SerDes of StreamConfig
Properties streamsConfiguration = new Properties();
streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG,   Serdes.String().getClass().getName());
streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());

// When you want to override SerDes explicitly/selectively
final Serde<String> stringSerde = Serdes.String();
StreamsBuilder builder = new StreamsBuilder();
builder.stream("my-avro-topic", Consumed.with(keyGenericAvroSerde, valueGenericAvroSerde));

Avro

Confluent provides Schema Registry-compatible Avro serdes for data in generic Avro and in specific Avro format:

<dependency>
    <groupId>io.confluent</groupId>
    <artifactId>kafka-streams-avro-serde</artifactId>
    <version>6.1.15</version>
</dependency>

Both the generic and the specific Avro serde require you to configure the endpoint of Confluent Schema Registry via the schema.registry.url setting:

• When you define the generic or specific Avro serde as a default serde via StreamsConfig, then you must also set the Schema Registry endpoint in StreamsConfig.
• When you instantiate the generic or specific Avro serde directly (e.g., new GenericAvroSerde()), you must call Serde#configure() on the serde instance to set the Schema Registry endpoint before using the serde instance. Additionally, you must tell Serde#configure() via a boolean parameter whether the serde instance is used for serializing/deserializing record keys (true) or record values (false).

Usage example for Confluent GenericAvroSerde:

// Generic Avro serde example
import io.confluent.kafka.streams.serdes.avro.GenericAvroSerde;

// When configuring the default serdes of StreamConfig
final Properties streamsConfiguration = new Properties();
streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, GenericAvroSerde.class);
streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, GenericAvroSerde.class);
streamsConfiguration.put("schema.registry.url", "http://my-schema-registry:8081");

// When you want to override serdes explicitly/selectively
final Map<String, String> serdeConfig = Collections.singletonMap("schema.registry.url",
                                                                 "http://my-schema-registry:8081");
final Serde<GenericRecord> keyGenericAvroSerde = new GenericAvroSerde();
keyGenericAvroSerde.configure(serdeConfig, true); // `true` for record keys
final Serde<GenericRecord> valueGenericAvroSerde = new GenericAvroSerde();
valueGenericAvroSerde.configure(serdeConfig, false); // `false` for record values

StreamsBuilder builder = new StreamsBuilder();
KStream<GenericRecord, GenericRecord> textLines =
  builder.stream(keyGenericAvroSerde, valueGenericAvroSerde, "my-avro-topic");

Usage example for Confluent SpecificAvroSerde:

// Specific Avro serde example
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;

// When configuring the default serdes of StreamConfig
final Properties streamsConfiguration = new Properties();
streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, SpecificAvroSerde.class);
streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, SpecificAvroSerde.class);
streamsConfiguration.put("schema.registry.url", "http://my-schema-registry:8081");

// When you want to override serdes explicitly/selectively
final Map<String, String> serdeConfig = Collections.singletonMap("schema.registry.url",
                                                                 "http://my-schema-registry:8081");
// `Foo` and `Bar` are Java classes generated from Avro schemas
final Serde<Foo> keySpecificAvroSerde = new SpecificAvroSerde<>();
keySpecificAvroSerde.configure(serdeConfig, true); // `true` for record keys
final Serde<Bar> valueSpecificAvroSerde = new SpecificAvroSerde<>();
valueSpecificAvroSerde.configure(serdeConfig, false); // `false` for record values

StreamsBuilder builder = new StreamsBuilder();
KStream<Foo, Bar> textLines = builder.stream("my-avro-topic", Consumed.with(keySpecificAvroSerde, valueSpecificAvroSerde));

When you create source streams, you specify input serdes by using the Streams DSL. When you construct the processor topology by using the lower-level Processor API, you can specify the serde class, like the Confluent GenericAvroSerde and SpecificAvroSerde classes.

TopologyBuilder builder = new TopologyBuilder();
builder.addSource("Source", keyGenericAvroSerde.deserializer(), valueGenericAvroSerde.deserializer(), inputTopic);

The following end-to-end demos showcase using the Confluent Avro serdes:

• Java:

  • GenericAvroIntegrationTest
  • SpecificAvroIntegrationTest

• Scala:

  • GenericAvroScalaIntegrationTest
  • SpecificAvroScalaIntegrationTest

Reflection Avro

Starting with version 5.4.0, Confluent Platform also provides a serializer and deserializer for writing and reading data in “reflection Avro” format. This serde’s “generic Avro” counterpart is GenericAvroSerde. This serde reads and writes data according to the wire format defined at Formats, Serializers, and Deserializers. It requires access to a Schema Registry endpoint, which you must define in the GenericAvroDeserializer by using the schema.registry.url parameter.

The following code example configures this serde as a Kafka Streams application’s default serde for both record keys and record values:

Properties streamsConfiguration = new Properties();
streamsConfiguration.put(StreamsConfig.KEY_SERDE_CLASS_CONFIG, ReflectionAvroSerde.class);
streamsConfiguration.put(StreamsConfig.VALUE_SERDE_CLASS_CONFIG, ReflectionAvroSerde.class);
streamsConfiguration.put(
       AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG,
       "http://confluent-schema-registry-server:8081/");

The following code example explicitly overrides the application’s default serdes, regardless of whatever they were configured to, so that only specific operations, like Kafka Streams use this serde:

Serde<MyJavaClassGeneratedFromAvroSchema> reflectionAvroSerde = new ReflectionAvroSerde<>();
   boolean isKeySerde = false;
   reflectionAvroSerde.configure(
       Collections.singletonMap(
           AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG,
           "http://confluent-schema-registry-server:8081/"),
       isKeySerde);
KStream<String, MyJavaClassGeneratedFromAvroSchema> stream = ...;
stream.to(Serdes.String(), reflectionAvroSerde, "my-output-topic");

JSON

The Kafka Streams code examples also include a basic serde implementation for JSON Schema:

• PageViewTypedDemo

As shown in the example file, you can use JSONSerdes inner classes Serdes.serdeFrom(<serializerInstance>, <deserializerInstance>) to construct JSON compatible serializers and deserializers.

JSON Schema

Confluent provides a Schema Registry-compatible JSON Schema Serde for data in JSON format:

<dependency>
    <groupId>io.confluent</groupId>
    <artifactId>kafka-streams-json-schema-serde</artifactId>
    <version>6.1.15</version>
</dependency>

Similar to how the Avro deserializer can return an instance of a specific Avro record type or a GenericRecord, the JSON Schema deserializer can return an instance of a specific Java class, or an instance of JsonNode. For more information, see JSON Schema Serializer and Deserializer.

The following code example shows how to use the KafkaJsonSchemaSerde class to serialize and deserialize a JSON record with a schema. For the full code listing, see KafkaJsonSchemaSerdeTest.java.

private static final String ANY_TOPIC = "any-topic";

private static ObjectMapper objectMapper = new ObjectMapper();

private static final String recordSchemaString = "{\"properties\": {\n"
    + "     \"null\": {\"type\": \"null\"},\n"
    + "     \"boolean\": {\"type\": \"boolean\"},\n"
    + "     \"number\": {\"type\": \"number\"},\n"
    + "     \"string\": {\"type\": \"string\"}\n"
    + "  },\n"
    + "  \"additionalProperties\": false\n"
    + "}";

private static final JsonSchema recordSchema = new JsonSchema(recordSchemaString);

private Object createJsonRecord() throws IOException {
  String json = "{\n"
      + "    \"null\": null,\n"
      + "    \"boolean\": true,\n"
      + "    \"number\": 12,\n"
      + "    \"string\": \"string\"\n"
      + "}";

  return objectMapper.readValue(json, Object.class);
}

private static KafkaJsonSchemaSerde<Object> createConfiguredSerdeForRecordValues() {
  SchemaRegistryClient schemaRegistryClient = new SchemaRegistryClient();
  KafkaJsonSchemaSerde<Object> serde = new KafkaJsonSchemaSerde<>(schemaRegistryClient);
  Map<String, Object> serdeConfig = new HashMap<>();
  serdeConfig.put(AbstractKafkaSchemaSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, "demo");
  serde.configure(serdeConfig, false);
  return serde;
}

public void shouldRoundTripRecords() throws Exception {
  // Given
  KafkaJsonSchemaSerde<Object> serde = createConfiguredSerdeForRecordValues();
  Object record = createJsonRecord();

  // When
  Object roundtrippedRecord = serde.deserializer().deserialize(
      ANY_TOPIC, serde.serializer().serialize(ANY_TOPIC, record));

  // Then
  assertThat(roundtrippedRecord, equalTo(record));

  // Cleanup
  serde.close();
}

Protobuf

Confluent provides a Schema Registry-compatible Protobuf Serde for data in Protobuf format:

<dependency>
  <groupId>io.confluent</groupId>
  <artifactId>kafka-streams-protobuf-serde</artifactId>
  <version>6.1.15</version>
</dependency>

The Protobuf serde provides support for referenced schemas, the ability of a schema to refer to other schemas. Also, a Protobuf schema can register a referenced schema automatically. For more information, see Protobuf Schema Serializer and Deserializer.

The following code example shows how to use the KafkaProtobufSerde class to serialize and deserialize a Protoobuf record with a schema. For the full code listing, see KafkaProtobufSerdeTest.java.

private static final String ANY_TOPIC = "any-topic";

private static final String recordSchemaString = "syntax = \"proto3\";\n"
    + "\n"
    + "option java_package = \"io.confluent.kafka.serializers.protobuf.test\";\n"
    + "option java_outer_classname = \"TestMessageProtos\";\n"
    + "\n"
    + "import \"google/protobuf/descriptor.proto\";\n"
    + "\n"
    + "message TestMessage {\n"
    + "    string test_string = 1 [json_name = \"test_str\"];\n"
    + "    bool test_bool = 2;\n"
    + "    bytes test_bytes = 3;\n"

    ...

    + "}\n";

private static final ProtobufSchema recordSchema = new ProtobufSchema(recordSchemaString);

private DynamicMessage createDynamicMessage() {
  DynamicMessage.Builder builder = recordSchema.newMessageBuilder();
  Descriptors.Descriptor desc = builder.getDescriptorForType();
  Descriptors.FieldDescriptor fd = desc.findFieldByName("test_string");
  builder.setField(fd, "string");
  fd = desc.findFieldByName("test_bool");
  builder.setField(fd, true);
  fd = desc.findFieldByName("test_bytes");
  builder.setField(fd, ByteString.copyFromUtf8("hello"));

  ...

  return builder.build();
}

private static KafkaProtobufSerde<Message> createConfiguredSerdeForRecordValues() {
  SchemaRegistryClient schemaRegistryClient = new SchemaRegistryClient();
  KafkaProtobufSerde<Message> serde = new KafkaProtobufSerde<>(schemaRegistryClient);
  Map<String, Object> serdeConfig = new HashMap<>();
  serdeConfig.put(AbstractKafkaSchemaSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, "demo");
  serde.configure(serdeConfig, false);
  return serde;
}

public void shouldRoundTripRecords() {
  // Given
  KafkaProtobufSerde<Message> serde = createConfiguredSerdeForRecordValues();
  DynamicMessage record = createDynamicMessage();

  // When
  Message roundtrippedRecord = serde.deserializer().deserialize(
      ANY_TOPIC, serde.serializer().serialize(ANY_TOPIC, record));

  // Then
  assertThat(roundtrippedRecord, equalTo(record));

  // Cleanup
  serde.close();
}

Further serdes

The Confluent examples repository demonstrates how to implement templated serdes:

• PriorityQueue<T> serde: PriorityQueueSerde

Implementing custom SerDes

If you need to implement custom SerDes, your best starting point is to take a look at the source code references of existing SerDes (see previous section). Typically, your workflow will be similar to:

1. Write a serializer for your data type T by implementing org.apache.kafka.common.serialization.Serializer.
2. Write a deserializer for T by implementing org.apache.kafka.common.serialization.Deserializer.
3. Write a serde for T by implementing org.apache.kafka.common.serialization.Serde, which you either do manually (see existing SerDes in the previous section) or by leveraging helper functions in Serdes such as Serdes.serdeFrom(Serializer<T>, Deserializer<T>). Note that you will need to implement your own class (that has no generic types) if you want to use your custom serde in the configuration provided to KafkaStreams. If your serde class has generic types or you use Serdes.serdeFrom(Serializer<T>, Deserializer<T>), you can pass your serde only via methods calls (for example builder.stream("topicName", Consumed.with(...)).

Note

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