Skip to content

Contact sales

By filling out this form and clicking submit, you acknowledge our privacy policy.
  • Labs icon Lab
  • A Cloud Guru
Google Cloud Platform icon

Using Schema Registry in a Kafka Application

Confluent Schema Registry gives you the ability to serialize and deserialize complex data objects, as well as manage and enforce contracts between producers and consumers. In this hands-on lab, you will have the opportunity to work with the Confluent Schema Registry by building a full application that uses it. You will create a schema, and then you will build both a producer and a consumer that use the schema to serialize and deserialize data.

Google Cloud Platform icon

Path Info

Clock icon Intermediate
Clock icon 1h 0m
Clock icon Oct 18, 2019

Contact sales

By filling out this form and clicking submit, you acknowledge our privacy policy.

Table of Contents

  1. Challenge

    Clone the Starter Project and Run it to Make Sure Everything Is Working

    1. Clone the starter project into the home directory:
    cd ~/
    git clone
    1. Run the code to ensure it works before modifying it:
    cd content-ccdak-schema-registry-lab/
    ./gradlew runProducer
    ./gradlew runConsumer

    Note: We should see a Hello, world! message in the output for both the producer and the consumer.

  2. Challenge

    Implement the Producer and Consumer Using an Avro Schema.

    1. Create the directory for Avro schemas:
    mkdir -p src/main/avro/com/linuxacademy/ccdak/schemaregistry
    1. Create a schema definition for purchases:
    vi src/main/avro/com/linuxacademy/ccdak/schemaregistry/Purchase.avsc
      "namespace": "com.linuxacademy.ccdak.schemaregistry",
      "type": "record",
      "name": "Purchase",
      "fields": [
        {"name": "id", "type": "int"},
        {"name": "product", "type": "string"},
        {"name": "quantity", "type": "int"}
    1. Implement the producer:
    vi src/main/java/com/linuxacademy/ccdak/schemaregistry/
    package com.linuxacademy.ccdak.schemaregistry;
    import io.confluent.kafka.serializers.AbstractKafkaAvroSerDeConfig;
    import io.confluent.kafka.serializers.KafkaAvroSerializer;
    import java.util.Properties;
    import org.apache.kafka.clients.producer.KafkaProducer;
    import org.apache.kafka.clients.producer.ProducerConfig;
    import org.apache.kafka.clients.producer.ProducerRecord;
    import org.apache.kafka.common.serialization.StringSerializer;
    public class ProducerMain {
        public static void main(String[] args) {
            final Properties props = new Properties();
            props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
            props.put(ProducerConfig.ACKS_CONFIG, "all");
            props.put(ProducerConfig.RETRIES_CONFIG, 0);
            props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
            props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, KafkaAvroSerializer.class);
            props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, "http://localhost:8081");
            KafkaProducer<String, Purchase> producer = new KafkaProducer<String, Purchase>(props);
            Purchase apples = new Purchase(1, "apples", 17);
            producer.send(new ProducerRecord<String, Purchase>("inventory_purchases", apples.getId().toString(), apples));
            Purchase oranges = new Purchase(2, "oranges", 5);
            producer.send(new ProducerRecord<String, Purchase>("inventory_purchases", oranges.getId().toString(), oranges));
    1. Implement the consumer:
    vi src/main/java/com/linuxacademy/ccdak/schemaregistry/
    package com.linuxacademy.ccdak.schemaregistry;
    import io.confluent.kafka.serializers.AbstractKafkaAvroSerDeConfig;
    import io.confluent.kafka.serializers.KafkaAvroDeserializer;
    import io.confluent.kafka.serializers.KafkaAvroDeserializerConfig;
    import java.time.Duration;
    import java.util.Collections;
    import java.util.Properties;
    import org.apache.kafka.clients.consumer.ConsumerConfig;
    import org.apache.kafka.clients.consumer.ConsumerRecord;
    import org.apache.kafka.clients.consumer.ConsumerRecords;
    import org.apache.kafka.clients.consumer.KafkaConsumer;
    import org.apache.kafka.common.serialization.StringDeserializer;
    public class ConsumerMain {
        public static void main(String[] args) {
            final Properties props = new Properties();
            props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
            props.put(ConsumerConfig.GROUP_ID_CONFIG, "group1");
            props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "true");
            props.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, "1000");
            props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
            props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, "http://localhost:8081");
            props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
            props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, KafkaAvroDeserializer.class);
            props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, true);
            KafkaConsumer<String, Purchase> consumer = new KafkaConsumer<>(props);
            try {
                BufferedWriter writer = new BufferedWriter(new FileWriter("/home/cloud_user/output/output.txt", true));
                while (true) {
                    final ConsumerRecords<String, Purchase> records = consumer.poll(Duration.ofMillis(100));
                    for (final ConsumerRecord<String, Purchase> record : records) {
                        final String key = record.key();
                        final Purchase value = record.value();
                        String outputString = "key=" + key + ", value=" + value;
                        writer.write(outputString + "\n");
            } catch (IOException e) {
                throw new RuntimeException(e);
    1. Run the producer:
    ./gradlew runProducer
    1. Run the consumer:
    ./gradlew runConsumer
    1. Verify the data in the output file:
    cat /home/cloud_user/output/output.txt

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

What's a lab?

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

Provided environment for hands-on practice

We will provide the credentials and environment necessary for you to practice right within your browser.

Guided walkthrough

Follow along with the author’s guided walkthrough and build something new in your provided environment!

Did you know?

On average, you retain 75% more of your learning if you get time for practice.

Start learning by doing today

View Plans