Apache Avro is a data serialization system. It uses JSON for defining data types/protocols and serializes data in a compact binary format. In the following tutorial, we will configure, build and run an example in which we will send/receive an Avro message to/from Apache Kafka using Apache Avro, Spring Kafka, Spring Boot and Maven.

If you want to learn more about Spring Kafka – head on over to the Spring Kafka tutorials page.

General Project Setup

Tools used:

Apache Avro 1.8
Spring Kafka 1.2
Spring Boot 1.5
Maven 3.5

Avro relies on schemas composed of primitive types which are defined using JSON. For this example, we will use the ‘User’ schema from the Apache Avro getting started guide as shown below. This schema is stored in the user.avsc file located under src/main/resources/avro.

{"namespace": "example.avro",
  "type": "record",
  "name": "User",
  "fields": [
    {"name": "name", "type": "string"},
    {"name": "favorite_number",  "type": ["int", "null"]},
    {"name": "favorite_color", "type": ["string", "null"]}
  ]
}

Avro ships with code generation which allows us to automatically create Java classes based on the above defined ‘User’ schema. Once we have generated the relevant classes, there is no need to use the schema directly in our program. The classes can be generated using the avro-tools.jar or via the Avro Maven plugin, we will use the latter in this example.

We start from a previous Spring Boot Kafka example and add the avro dependency to the Maven POM file. In addition we configure the avro-maven-plugin to run the ‘schema’ goal on all schema’s that are found in the /src/main/resources/avro/ location as shown below.

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.codenotfound</groupId>
  <artifactId>spring-kafka-avro</artifactId>
  <version>0.0.1-SNAPSHOT</version>

  <name>spring-kafka-avro</name>
  <description>Spring Kafka - Apache Avro Serializer Deserializer Example</description>
  <url>https://www.codenotfound.com/spring-kafka-apache-avro-serializer-deserializer-example.html</url>

  <parent>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-parent</artifactId>
    <version>1.5.4.RELEASE</version>
  </parent>

  <properties>
    <java.version>1.8</java.version>

    <spring-kafka.version>1.2.2.RELEASE</spring-kafka.version>
    <avro.version>1.8.2</avro.version>
  </properties>

  <dependencies>
    <!-- spring-boot -->
    <dependency>
      <groupId>org.springframework.boot</groupId>
      <artifactId>spring-boot-starter</artifactId>
    </dependency>
    <dependency>
      <groupId>org.springframework.boot</groupId>
      <artifactId>spring-boot-starter-test</artifactId>
      <scope>test</scope>
    </dependency>
    <!-- spring-kafka -->
    <dependency>
      <groupId>org.springframework.kafka</groupId>
      <artifactId>spring-kafka</artifactId>
      <version>${spring-kafka.version}</version>
    </dependency>
    <dependency>
      <groupId>org.springframework.kafka</groupId>
      <artifactId>spring-kafka-test</artifactId>
      <version>${spring-kafka.version}</version>
      <scope>test</scope>
    </dependency>
    <!-- avro -->
    <dependency>
      <groupId>org.apache.avro</groupId>
      <artifactId>avro</artifactId>
      <version>${avro.version}</version>
    </dependency>
  </dependencies>

  <build>
    <plugins>
      <!-- spring-boot-maven-plugin -->
      <plugin>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-maven-plugin</artifactId>
      </plugin>
      <!-- avro-maven-plugin -->
      <plugin>
        <groupId>org.apache.avro</groupId>
        <artifactId>avro-maven-plugin</artifactId>
        <version>${avro.version}</version>
        <executions>
          <execution>
            <phase>generate-sources</phase>
            <goals>
              <goal>schema</goal>
            </goals>
            <configuration>
              <sourceDirectory>${project.basedir}/src/main/resources/avro/</sourceDirectory>
              <outputDirectory>${project.build.directory}/generated/avro</outputDirectory>
            </configuration>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>
</project>

In order to trigger the code generation via Maven, executed following command:

mvn generate-sources

This results in the generation of a User class which contains the schema and a number of Builder methods to construct a User object.

Producing Avro Messages to a Kafka Topic

Kafka stores and transports Byte arrays in its topics. But as we are working with Avro objects we need to transform to/from these Byte arrays. Before version 0.9.0.0, the Kafka Java API used implementations of Encoder/Decoder interfaces to handle transformations but these have been replaced by Serializer/Deserializer interface implementations in the new API.

Kafka ships with a number of built in (de)serializers but an Avro one is not included.

To tackle this we will create an AvroSerializer class that implements the Serializer interface specifically for Avro objects. We then implement the serialize() method which takes as input a topic name and a data object which in our case is an Avro object that extends SpecificRecordBase. The method serializes the Avro object to a byte array and returns the result.

package com.codenotfound.kafka.serializer;

import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.util.Map;

import javax.xml.bind.DatatypeConverter;

import org.apache.avro.generic.GenericDatumWriter;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.io.BinaryEncoder;
import org.apache.avro.io.DatumWriter;
import org.apache.avro.io.EncoderFactory;
import org.apache.avro.specific.SpecificRecordBase;
import org.apache.kafka.common.errors.SerializationException;
import org.apache.kafka.common.serialization.Serializer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class AvroSerializer<T extends SpecificRecordBase> implements Serializer<T> {

  private static final Logger LOGGER = LoggerFactory.getLogger(AvroSerializer.class);

  @Override
  public void close() {
    // No-op
  }

  @Override
  public void configure(Map<String, ?> arg0, boolean arg1) {
    // No-op
  }

  @Override
  public byte[] serialize(String topic, T data) {
    try {
      byte[] result = null;

      if (data != null) {
        LOGGER.debug("data='{}'", data);

        ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream();
        BinaryEncoder binaryEncoder =
            EncoderFactory.get().binaryEncoder(byteArrayOutputStream, null);

        DatumWriter<GenericRecord> datumWriter = new GenericDatumWriter<>(data.getSchema());
        datumWriter.write(data, binaryEncoder);

        binaryEncoder.flush();
        byteArrayOutputStream.close();

        result = byteArrayOutputStream.toByteArray();
        LOGGER.debug("serialized data='{}'", DatatypeConverter.printHexBinary(result));
      }
      return result;
    } catch (IOException ex) {
      throw new SerializationException(
          "Can't serialize data='" + data + "' for topic='" + topic + "'", ex);
    }
  }
}

Now we need to change the SenderConfig to start using our custom Serializer implementation. This is done by setting the ‘VALUE_SERIALIZER_CLASS_CONFIG’ property to the AvroSerializer class. In addition, we change the ProducerFactory and KafkaTemplate generic type so that it specifies User instead of String.

package com.codenotfound.kafka.producer;

import java.util.HashMap;
import java.util.Map;

import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.common.serialization.StringSerializer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.core.DefaultKafkaProducerFactory;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.kafka.core.ProducerFactory;

import com.codenotfound.kafka.serializer.AvroSerializer;

import example.avro.User;

@Configuration
public class SenderConfig {

  @Value("${kafka.bootstrap-servers}")
  private String bootstrapServers;

  @Bean
  public Map<String, Object> producerConfigs() {
    Map<String, Object> props = new HashMap<>();
    props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
    props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
    props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, AvroSerializer.class);

    return props;
  }

  @Bean
  public ProducerFactory<String, User> producerFactory() {
    return new DefaultKafkaProducerFactory<>(producerConfigs());
  }

  @Bean
  public KafkaTemplate<String, User> kafkaTemplate() {
    return new KafkaTemplate<>(producerFactory());
  }

  @Bean
  public Sender sender() {
    return new Sender();
  }
}

The only thing left to do is to update the Sender class so that it’s send() method accepts an Avro User object as input. Note that we also update the KafkaTemplate generic type.

package com.codenotfound.kafka.producer;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.kafka.core.KafkaTemplate;

import example.avro.User;

public class Sender {

  private static final Logger LOGGER = LoggerFactory.getLogger(Sender.class);

  @Value("${kafka.topic.avro}")
  private String avroTopic;

  @Autowired
  private KafkaTemplate<String, User> kafkaTemplate;

  public void send(User user) {
    LOGGER.info("sending user='{}'", user.toString());
    kafkaTemplate.send(avroTopic, user);
  }
}

Consuming Avro Messages from a Kafka Topic

Received messages need to be deserialized back to the Avro format. To achieve this we create an AvroDeserializer class that implements the Deserializer interface. The deserialize() method takes as input a topic name and a Byte array which is decoded back into an Avro object. The schema that needs to be used for the decoding is retrieved from the targetType class parameter that needs to be passed as an argument to the AvroDeserializer constructor.

package com.codenotfound.kafka.serializer;

import java.util.Arrays;
import java.util.Map;

import javax.xml.bind.DatatypeConverter;

import org.apache.avro.generic.GenericRecord;
import org.apache.avro.io.DatumReader;
import org.apache.avro.io.Decoder;
import org.apache.avro.io.DecoderFactory;
import org.apache.avro.specific.SpecificDatumReader;
import org.apache.avro.specific.SpecificRecordBase;
import org.apache.kafka.common.errors.SerializationException;
import org.apache.kafka.common.serialization.Deserializer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class AvroDeserializer<T extends SpecificRecordBase> implements Deserializer<T> {

  private static final Logger LOGGER = LoggerFactory.getLogger(AvroDeserializer.class);

  protected final Class<T> targetType;

  public AvroDeserializer(Class<T> targetType) {
    this.targetType = targetType;
  }

  @Override
  public void close() {
    // No-op
  }

  @Override
  public void configure(Map<String, ?> arg0, boolean arg1) {
    // No-op
  }

  @SuppressWarnings("unchecked")
  @Override
  public T deserialize(String topic, byte[] data) {
    try {
      T result = null;

      if (data != null) {
        LOGGER.debug("data='{}'", DatatypeConverter.printHexBinary(data));

        DatumReader<GenericRecord> datumReader =
            new SpecificDatumReader<>(targetType.newInstance().getSchema());
        Decoder decoder = DecoderFactory.get().binaryDecoder(data, null);

        result = (T) datumReader.read(null, decoder);
        LOGGER.debug("deserialized data='{}'", result);
      }
      return result;
    } catch (Exception ex) {
      throw new SerializationException(
          "Can't deserialize data '" + Arrays.toString(data) + "' from topic '" + topic + "'", ex);
    }
  }
}

The ReceiverConfig needs to be updated so that the AvroDeserializer is used as value for the ‘VALUE_DESERIALIZER_CLASS_CONFIG’ property. We also change the ConsumerFactory and ConcurrentKafkaListenerContainerFactory generic type so that it specifies User instead of String. The DefaultKafkaConsumerFactory is created by passing a new AvroDeserializer that takes ‘User.class’ as constructor argument.

The Class<?> targetType of the AvroDeserializer is needed to allow the deserialization of a consumed byte[] to the proper target object (in this example the User class).

package com.codenotfound.kafka.consumer;

import java.util.HashMap;
import java.util.Map;

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafka;
import org.springframework.kafka.config.ConcurrentKafkaListenerContainerFactory;
import org.springframework.kafka.core.ConsumerFactory;
import org.springframework.kafka.core.DefaultKafkaConsumerFactory;

import com.codenotfound.kafka.serializer.AvroDeserializer;

import example.avro.User;

@Configuration
@EnableKafka
public class ReceiverConfig {

  @Value("${kafka.bootstrap-servers}")
  private String bootstrapServers;

  @Bean
  public Map<String, Object> consumerConfigs() {
    Map<String, Object> props = new HashMap<>();
    props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
    props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, AvroDeserializer.class);
    props.put(ConsumerConfig.GROUP_ID_CONFIG, "avro");

    return props;
  }

  @Bean
  public ConsumerFactory<String, User> consumerFactory() {
    return new DefaultKafkaConsumerFactory<>(consumerConfigs(), new StringDeserializer(),
        new AvroDeserializer<>(User.class));
  }

  @Bean
  public ConcurrentKafkaListenerContainerFactory<String, User> kafkaListenerContainerFactory() {
    ConcurrentKafkaListenerContainerFactory<String, User> factory =
        new ConcurrentKafkaListenerContainerFactory<>();
    factory.setConsumerFactory(consumerFactory());

    return factory;
  }

  @Bean
  public Receiver receiver() {
    return new Receiver();
  }
}

Just like with the Sender class, the argument of the receive() method of the Receiver class needs to be changed to the Avro User class.

package com.codenotfound.kafka.consumer;

import java.util.concurrent.CountDownLatch;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.kafka.annotation.KafkaListener;

import example.avro.User;

public class Receiver {

  private static final Logger LOGGER = LoggerFactory.getLogger(Receiver.class);

  private CountDownLatch latch = new CountDownLatch(1);

  public CountDownLatch getLatch() {
    return latch;
  }

  @KafkaListener(topics = "${kafka.topic.avro}")
  public void receive(User user) {
    LOGGER.info("received user='{}'", user.toString());
    latch.countDown();
  }
}

Test Sending and Receiving Avro Messages on Kafka

The SpringKafkaApplicationTest test case demonstrates the above sample code. An embedded Kafka and ZooKeeper server are automatically started using a JUnit ClassRule. Using @Before we wait until all the partitions are assigned to our Receiver by looping over the available ConcurrentMessageListenerContainer (if we don’t do this the message will already be sent before the listeners are assigned to the topic).

In the testReceiver() test case an Avro User object is created using the Builder methods. This user is then sent to ‘avro.t’ topic. Finally, the CountDownLatch from the Receiver is used to verify that a message was successfully received.

package com.codenotfound.kafka;

import static org.assertj.core.api.Assertions.assertThat;

import java.util.concurrent.TimeUnit;

import org.junit.Before;
import org.junit.ClassRule;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.kafka.config.KafkaListenerEndpointRegistry;
import org.springframework.kafka.listener.MessageListenerContainer;
import org.springframework.kafka.test.rule.KafkaEmbedded;
import org.springframework.kafka.test.utils.ContainerTestUtils;
import org.springframework.test.context.junit4.SpringRunner;

import com.codenotfound.kafka.consumer.Receiver;
import com.codenotfound.kafka.producer.Sender;

import example.avro.User;

@RunWith(SpringRunner.class)
@SpringBootTest
public class SpringKafkaApplicationTest {

  @Autowired
  private Sender sender;

  @Autowired
  private Receiver receiver;

  @Autowired
  private KafkaListenerEndpointRegistry kafkaListenerEndpointRegistry;

  @ClassRule
  public static KafkaEmbedded embeddedKafka = new KafkaEmbedded(1, true, "avro.t");

  @Before
  public void setUp() throws Exception {
    // wait until the partitions are assigned
    for (MessageListenerContainer messageListenerContainer : kafkaListenerEndpointRegistry
        .getListenerContainers()) {
      ContainerTestUtils.waitForAssignment(messageListenerContainer,
          embeddedKafka.getPartitionsPerTopic());
    }
  }

  @Test
  public void testReceiver() throws Exception {
    User user = User.newBuilder().setName("John Doe").setFavoriteColor("green")
        .setFavoriteNumber(null).build();
    sender.send(user);

    receiver.getLatch().await(10000, TimeUnit.MILLISECONDS);
    assertThat(receiver.getLatch().getCount()).isEqualTo(0);
  }
}

Note that the sample code also contains AvroSerializerTest and AvroDeserializerTest unit test cases to verify the serialization classes.

In order to run the above tests open a command prompt and execute following Maven command:

mvn test

Maven will download the needed dependencies, compile the code and run the unit test case. The result should be a successful build during which following logs are generated:

  .   ____          _            __ _ _
 /// / ___'_ __ _ _(_)_ __  __ _ / / / /
( ( )/___ | '_ | '_| | '_ // _` | / / / /
 ///  ___)| |_)| | | | | || (_| |  ) ) ) )
  '  |____| .__|_| |_|_| |_/__, | / / / /
 =========|_|==============|___/=/_/_/_/
 :: Spring Boot ::        (v1.5.4.RELEASE)

08:36:56.175 [main] INFO  c.c.kafka.SpringKafkaApplicationTest - Starting SpringKafkaApplicationTest on cnf-pc with PID 700 (started by CodeNotFound in c:/codenotfound/spring-kafka/spring-kafka-avro)
08:36:56.175 [main] INFO  c.c.kafka.SpringKafkaApplicationTest - No active profile set, falling back to default profiles: default
08:36:56.889 [main] INFO  c.c.kafka.SpringKafkaApplicationTest - Started SpringKafkaApplicationTest in 1.068 seconds (JVM running for 5.293)
08:36:58.223 [main] INFO  c.codenotfound.kafka.producer.Sender - sending user='{"name": "John Doe", "favorite_number": null, "favorite_color": "green"}'
08:36:58.271 [org.springframework.kafka.KafkaListenerEndpointContainer#0-0-L-1] INFO  c.c.kafka.consumer.Receiver - received user='{"name": "John Doe", "favorite_number": null, "favorite_color": "green"}'
08:37:00.240 [main] ERROR o.a.zookeeper.server.ZooKeeperServer - ZKShutdownHandler is not registered, so ZooKeeper server won't take any action on ERROR or SHUTDOWN server state changes
Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 8.871 sec - in com.codenotfound.kafka.SpringKafkaApplicationTest

Results :

Tests run: 3, Failures: 0, Errors: 0, Skipped: 0

[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 41.632 s
[INFO] Finished at: 2017-04-17T08:37:31+02:00
[INFO] Final Memory: 18M/212M
[INFO] ------------------------------------------------------------------------

github mark
If you would like to run the above code sample you can get the full source code here.

This concludes the example of how to send/receive Avro messages using Spring Kafka.

I created this blog post based on a user request so if you found this tutorial useful or would like to see another variation, let me know.

原创文章，作者：ItWorker，如若转载，请注明出处：https://blog.ytso.com/271520.html

Spring Kafka – Apache Avro Serializer Deserializer Example

General Project Setup

Producing Avro Messages to a Kafka Topic

Consuming Avro Messages from a Kafka Topic

Test Sending and Receiving Avro Messages on Kafka

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Spring Kafka – Apache Avro Serializer Deserializer Example

General Project Setup

Producing Avro Messages to a Kafka Topic

Consuming Avro Messages from a Kafka Topic

Test Sending and Receiving Avro Messages on Kafka

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