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(), through(), groupByKey(), groupBy().

You can provide SerDes by using either of these methods:

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

Configuring SerDes

SerDes specified in the Streams configuration via StreamsConfig 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.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.VALUE_SERDE_CLASS_CONFIG, Serdes.Long().getClass().getName());

StreamsConfig config = new StreamsConfig(settings);

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()));

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>1.0.0-cp1</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()

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.KEY_SERDE_CLASS_CONFIG,   Serdes.String().getClass().getName());
streamsConfiguration.put(StreamsConfig.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>4.0.0</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.KEY_SERDE_CLASS_CONFIG,   GenericAvroSerde.class);
streamsConfiguration.put(StreamsConfig.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.KEY_SERDE_CLASS_CONFIG,   SpecificAvroSerde.class);
streamsConfiguration.put(StreamsConfig.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));

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

JSON

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

You can construct a unified JSON serde from the JsonPOJOSerializer and JsonPOJODeserializer via Serdes.serdeFrom(<serializerInstance>, <deserializerInstance>). The PageViewTypedDemo example demonstrates how to use this JSON serde.

Further serdes

The Confluent examples repository demonstrates how to implement templated serdes:

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>).