This is the seventh post in this series where we go through the basics of using Kafka. We saw in the previous post how to build a simple Kafka Streams application. We will see here how to use a custom SerDe (Serializer / Deserializer) and how to use Avro and the Schema Registry.

The concept of SerDe

In Kafka tutorial #3 - JSON SerDes, I introduced the name SerDe but we had 2 separate classes for the serializer and the deserializer. Kafka Streams keeps the serializer and the deserializer together, and uses the org.apache.kafka.common.serialization.Serde interface for that. Here is the Java code of this interface:

public interface Serde<T> extends Closeable {

    void configure(Map<String, ?> configs, boolean isKey);

    void close();

    Serializer<T> serializer();

    Deserializer<T> deserializer();
}

We will see how to use this interface.

Custom deserializer

The goal here is to avoid having to deserialize JSON strings into Person objects by hand in our Kafka Streams topology, as we did in part 6:

val personJsonStream: KStream<String, String> = streamsBuilder
        .stream<String, String>(personsTopic, Consumed.with(Serdes.String(), Serdes.String()))

val personStream: KStream<String, Person> = personJsonStream.mapValues { v ->
    jsonMapper.readValue(v, Person::class.java)
}

This is where we want to use an implementation of Serde<Person>. To write one, we first need implementations of Serializer<Person> and Deserializer<Person>. We already wrote these classes in part 3. We can therefore simply write the SerDe as follows:

class PersonSerde : Serde<Person> {
    override fun configure(configs: MutableMap<String, *>?, isKey: Boolean) {}
    override fun close() {}
    override fun deserializer(): Deserializer<Person> = PersonDeserializer()
    override fun serializer(): Serializer<Person> = PersonSerializer()
}

class PersonSerializer : Serializer<Person> {
    ...
}

class PersonDeserializer : Deserializer<Person> {
    ...
}

We can now use this SerDe to build a KStream that directly deserializes the values of the messages as Person objects:

val personStream: KStream<String, Person> = streamsBuilder
        .stream(personsTopic, Consumed.with(Serdes.String(), PersonSerde()))

Another option, instead of creating our own PersonSerde class, would have been to use Serdes.serdeFrom() to dynamically wrap our serializer and deserializer into a Serde:

val personSerde = Serdes.serdeFrom(PersonSerializer(), PersonDeserializer())

The rest of the code remains the same as in part 6!

Avro and the Schema Registry

Now, let’s assume we have produced our messages in Avro format, as we did in part 4. In part 5, we had been able to consume this data by configuring the URL to the Schema Registry and by using a KafkaAvroDeserializer. Here, we need to use an instance of a Serde, so let’s add a dependency to get one:

repositories {
    mavenCentral()
    maven { url 'https://packages.confluent.io/maven/' }
}

dependencies {
    ...
    compile 'io.confluent:kafka-streams-avro-serde:5.0.0'
}

This dependency contains GenericAvroSerde and SpecificAvroSerde, two implementations of Serde that allow you to work with Avro records. We will use the former, and we need to configure it with the URL of the Schema Registry:

val avroSerde = GenericAvroSerde().apply {
    configure(mapOf(Pair("schema.registry.url", "http://localhost:8081")), false)
}

We can now create a KStream with this Serde, to get a KStream that contains GenericRecord objects:

val personAvroStream: KStream<String, GenericRecord> = streamsBuilder
        .stream(personsAvroTopic, Consumed.with(Serdes.String(), avroSerde))

We can finally “rehydrate” our model objects:

val personStream: KStream<String, Person> = personAvroStream.mapValues { personAvro ->
    val person = Person(
            firstName = personAvro["firstName"].toString(),
            lastName = personAvro["lastName"].toString(),
            birthDate = Date(personAvro["birthDate"] as Long)
    )
    logger.debug("Person: $person")
    person
}

And, again, the rest of the code remains the same as in part 6!

We could make our code cleaner by creating our own Serde that would include the “rehydration” code, so that we would directly deserialize Avro objects into Person objects. To do so, we would have to extend the GenericAvroDeserializer. We will leave this exercise to the reader!

Conclusion

We have seen how we can improve our Kafka Streams application to deserialize data in JSON or Avro format. The serialization part - when writing to a topic - would be very similar since we are using SerDes that are capable both of deserializing and serializing data.

Notice that if you are working in Scala, the Kafka Streams Circe library offers SerDes that handle JSON data through the Circe library (equivalent of Jackson in the Scala world).

The code of this tutorial can be found here.

Kafka tutorial #7 - Kafka Streams SerDes and Avro

The concept of SerDe

Custom deserializer

Avro and the Schema Registry

Conclusion