How to build an Apache Kafka ® producer application with callbacks using Kafka

Overload the KafkaProducer.send method with an instance of the Callback interface as the second parameter.

producer.send(producerRecord, (recordMetadata, exception) -> {
      if (exception == null) {
          System.out.println("Record written to offset " +
                  recordMetadata.offset() + " timestamp " +
                  recordMetadata.timestamp());
      } else {
          System.err.println("An error occurred");
          exception.printStackTrace(System.err);
      }
});

Prerequisites

This tutorial installs Confluent Platform using Docker. Before proceeding:

• Install Docker Desktop (version 4.0.0 or later) or Docker Engine (version 19.03.0 or later) if you don’t already have it
• Install the Docker Compose plugin if you don’t already have it. This isn’t necessary if you have Docker Desktop since it includes Docker Compose.
• Start Docker if it’s not already running, either by starting Docker Desktop or, if you manage Docker Engine with systemd, via systemctl
• Verify that Docker is set up properly by ensuring no errors are output when you run docker info and docker compose version on the command line

Initialize the project

To get started, make a new directory anywhere you’d like for this project:

mkdir kafka-producer-application-callback && cd kafka-producer-application-callback

Get Confluent Platform

Next, create the following docker-compose.yml file to obtain Confluent Platform (for Kafka in the cloud, see Confluent Cloud):

version: '2'
services:
  broker:
    image: confluentinc/cp-kafka:7.4.1
    hostname: broker
    container_name: broker
    ports:
    - 29092:29092
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT,CONTROLLER:PLAINTEXT
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
      KAFKA_PROCESS_ROLES: broker,controller
      KAFKA_NODE_ID: 1
      KAFKA_CONTROLLER_QUORUM_VOTERS: 1@broker:29093
      KAFKA_LISTENERS: PLAINTEXT://broker:9092,CONTROLLER://broker:29093,PLAINTEXT_HOST://0.0.0.0:29092
      KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT
      KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER
      KAFKA_LOG_DIRS: /tmp/kraft-combined-logs
      CLUSTER_ID: MkU3OEVBNTcwNTJENDM2Qk
  schema-registry:
    image: confluentinc/cp-schema-registry:7.3.0
    hostname: schema-registry
    container_name: schema-registry
    depends_on:
    - broker
    ports:
    - 8081:8081
    environment:
      SCHEMA_REGISTRY_HOST_NAME: schema-registry
      SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: broker:9092
      SCHEMA_REGISTRY_LOG4J_ROOT_LOGLEVEL: WARN

And launch it by running:

docker compose up -d

Create a topic

In this step we’re going to create a topic for use during this tutorial.

Open a new terminal window and then run this command to open a shell on the broker docker container

docker exec -it broker bash

Next, create the topic that the producer can write to

kafka-topics --create --topic output-topic --bootstrap-server broker:9092 --replication-factor 1 --partitions 1

Keep this terminal window open for later use when you run a console consumer to verify your producer application.

Configure the project

Create the following Gradle build file for the project, named build.gradle:

buildscript {
    repositories {
        mavenCentral()
    }
    dependencies {
        classpath "gradle.plugin.com.github.jengelman.gradle.plugins:shadow:7.0.0"
    }
}

plugins {
    id "java"
    id "idea"
    id "eclipse"
}

sourceCompatibility = JavaVersion.VERSION_17
targetCompatibility = JavaVersion.VERSION_17
version = "0.0.1"

repositories {
    mavenCentral()

    maven {
        url "https://packages.confluent.io/maven"
    }
}

apply plugin: "com.github.johnrengelman.shadow"

dependencies {
    implementation "org.slf4j:slf4j-simple:2.0.7"
    implementation 'org.apache.kafka:kafka-streams:3.4.0'
    implementation ('org.apache.kafka:kafka-clients') {
       version {
           strictly '3.4.0'
        }
      }
    testImplementation "org.apache.kafka:kafka-streams-test-utils:3.4.0"
    testImplementation "junit:junit:4.13.2"
    testImplementation 'org.hamcrest:hamcrest:2.2'
}

test {
    testLogging {
        outputs.upToDateWhen { false }
        showStandardStreams = true
        exceptionFormat = "full"
    }
}

jar {
  manifest {
    attributes(
      "Class-Path": configurations.compileClasspath.collect { it.getName() }.join(" "),
      "Main-Class": "io.confluent.developer.KafkaProducerCallbackApplication"
    )
  }
}

shadowJar {
    archiveBaseName = "kafka-producer-application-callback-standalone"
    archiveClassifier = ''
}

Run the following command to obtain the Gradle wrapper:

gradle wrapper

Next, create a directory for configuration data:

mkdir configuration

Add application and producer properties

Then create a development file at configuration/dev.properties:

bootstrap.servers=localhost:29092

key.serializer=org.apache.kafka.common.serialization.StringSerializer
value.serializer=org.apache.kafka.common.serialization.StringSerializer
acks=all

#Properties below this line are specific to code in this application
output.topic.name=output-topic

Let’s do a quick walkthrough of some of the producer properties.

key.serializer - The serializer the KafkaProducer will use to serialize the key.

value.serializer - The serializer the KafkaProducer will use to serialize the value.

acks - The KafkaProducer uses the acks configuration to tell the lead broker how many acknowledgment to wait for to consider a produce request complete. Acceptable values for acks are: 0, 1 (the default), -1, or all. Setting acks to -1 is the same as setting it to all.

acks=0: "fire and forget", once the producer sends the record batch it is considered successful
acks=1: leader broker added the records to its local log but didn’t wait for any acknowledgment from the followers
acks=all: highest data durability guarantee, the leader broker persisted the record to its log and received acknowledgment of replication from all in-sync replicas. When using acks=all, it’s strongly recommended to update min.insync.replicas as well.

This is only a small sub-set of producer configuration parameters. The full list of producer configuration parameters can be found in the Apache Kafka documentation.

Create the KafkaProducer application

Create a directory for the Java files in this project:

mkdir -p src/main/java/io/confluent/developer

Before you create your application file, let’s look at some of the key points of this program:

KafkaProducerCallbackApplication constructor

public class KafkaProducerCallbackApplication {

  private final Producer<String, String> producer;
  final String outTopic;

  public KafkaProducerCallbackApplication(final Producer<String, String> producer,  (1)
                                  final String topic) {                     (2)
    this.producer = producer;
    outTopic = topic;
  }

1	Passing in the `Producer` instance as a constructor parameter.
2	The topic to write records to

In this tutorial you’ll inject the dependencies in the KafkaProducerCallbackApplication.main() method. Having this thin wrapper class around a Producer is not required, but it does help with making our code easier to test. We’ll go into more details in the testing section of the tutorial.

(In practice you may want to use a dependency injection framework library, such as the Spring Framework).

Next let’s take a look at the KafkaProducerCallbackApplication.produce method

KafkaProducerCallbackApplication.produce

public void produce(final String message) {
    final String[] parts = message.split("-");  (1)
    final String key, value;
    if (parts.length > 1) {
      key = parts[0];
      value = parts[1];
    } else {
      key = "NO-KEY";
      value = parts[0];
    }
    final ProducerRecord<String, String> producerRecord = new ProducerRecord<>(outTopic, key, value);  (2)
    producer.send(producerRecord, (recordMetadata, exception) -> {  (3)
              if (exception == null) {                          (4)
                  System.out.println("Record written to offset " +
                          recordMetadata.offset() + " timestamp " +
                          recordMetadata.timestamp());
              } else {
                  System.err.println("An error occurred"); (5)
                  exception.printStackTrace(System.err);
              }
        });
  }

1	Process the String for sending message
2	Create the `ProducerRecord`
3	Send the record to the broker specifying a `Callback` instance as a lambda function
4	If there’s no exceptions print the offset and timestamp of the acknowledged record
5	Error handling portion-in this case printing the stacktrace to `System.err`

The KafkaProducerCallbackApplication.produce method does some processing on a String, and then sends it as a ProducerRecord. While this code is a trivial example, it’s enough to show the example of using a KafkaProducer.

Notice that this overload of the KafkaProducer.send method accepts a second parameter, an instance of the Callback interface.

The Callback provides a way of handling any actions you want to take on request completion asynchronously. Note that the Callback code executes on the producer’s I/O thread and any time consuming tasks could cause a delay in sending new records, so any code here should be designed to execute quickly.

The KafkaProducer.send method is asynchronous and returns as soon as the provided record is placed in the buffer of records to be sent to the broker. Once the broker acknowledges that the record has been appended to its log, the broker completes the produce request, which the application receives as RecordMetadata—information about the committed message.

In this example, the code in the callback just prints information from each record’s RecordMetadata object, specifically the timestamp and offset.

Now go ahead and create the following file at src/main/java/io/confluent/developer/KafkaProducerCallbackApplication.java.

package io.confluent.developer;


import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.io.FileInputStream;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.List;
import java.util.Properties;

public class KafkaProducerCallbackApplication {

    private final Producer<String, String> producer;
    final String outTopic;

    public KafkaProducerCallbackApplication(final Producer<String, String> producer,
                                    final String topic) {
        this.producer = producer;
        outTopic = topic;
    }

    public void produce(final String message) {
        final String[] parts = message.split("-");
        final String key, value;
        if (parts.length > 1) {
            key = parts[0];
            value = parts[1];
        } else {
            key = null;
            value = parts[0];
        }

        final ProducerRecord<String, String> producerRecord = new ProducerRecord<>(outTopic, key, value);
        producer.send(producerRecord, (recordMetadata, exception) -> {
              if (exception == null) {
                  System.out.println("Record written to offset " +
                          recordMetadata.offset() + " timestamp " +
                          recordMetadata.timestamp());
              } else {
                  System.err.println("An error occurred");
                  exception.printStackTrace(System.err);
              }
        });
    }

    public void shutdown() {
        producer.close();
    }

    public static Properties loadProperties(String fileName) throws IOException {
        final Properties envProps = new Properties();
        final FileInputStream input = new FileInputStream(fileName);
        envProps.load(input);
        input.close();

        return envProps;
    }

    public static void main(String[] args) throws Exception {
        if (args.length < 2) {
            throw new IllegalArgumentException(
                    "This program takes two arguments: the path to an environment configuration file and" +
                            "the path to the file with records to send");
        }

        final Properties props = KafkaProducerCallbackApplication.loadProperties(args[0]);
        final String topic = props.getProperty("output.topic.name");
        final Producer<String, String> producer = new KafkaProducer<>(props);
        final KafkaProducerCallbackApplication producerApp = new KafkaProducerCallbackApplication(producer, topic);

        String filePath = args[1];
        try {
            List<String> linesToProduce = Files.readAllLines(Paths.get(filePath));
            linesToProduce.stream()
                          .filter(l -> !l.trim().isEmpty())
                          .forEach(producerApp::produce);
        } catch (IOException e) {
            System.err.printf("Error reading file %s due to %s %n", filePath, e);
        } finally {
           producerApp.shutdown();
        }
    }
}

Create data to produce to Kafka

Create the following file input.txt in the base directory of the tutorial. The numbers before the - will be the key and the part after will be the value.

1-value
2-words
3-All Streams
4-Lead to
5-Kafka
6-Go to
7-Kafka Summit
8-How can
9-a 10 ounce
10-bird carry a
11-5lb coconut

Compile and run the KafkaProducer application

In your terminal, run:

./gradlew shadowJar

Now that you have an uberjar for the KafkaProducerCallbackApplication, you can launch it locally.

java -jar build/libs/kafka-producer-application-callback-standalone-0.0.1.jar configuration/dev.properties input.txt

After you run the previous command, the application will process the file and you should something like this on the console:

Record written to offset 0 timestamp 1597352120029
Record written to offset 1 timestamp 1597352120037
Record written to offset 2 timestamp 1597352120037
Record written to offset 3 timestamp 1597352120037
Record written to offset 4 timestamp 1597352120037
Record written to offset 5 timestamp 1597352120037
Record written to offset 6 timestamp 1597352120037
Record written to offset 7 timestamp 1597352120037
Record written to offset 8 timestamp 1597352120037
Record written to offset 9 timestamp 1597352120037
Record written to offset 10 timestamp 1597352120038

Now you can experiment some by creating your own file in base directory and re-run the above command and substitute your file name for input.txt

Remember any data before the - is the key and data after is the value.

Confirm records sent by consuming from topic

Now run a console consumer that will read topics from the output topic to confirm your application published the expected records.

kafka-console-consumer --topic output-topic \
 --bootstrap-server broker:9092 \
 --from-beginning \
 --property print.key=true \
 --property key.separator=" : "

The output from the consumer can vary if you added any of your own records, but it should look something like this:

1 : value
2 : words
3 : All Streams
4 : Lead to
5 : Kafka
6 : Go to
7 : Kafka Summit
8 : How can
9 : a 10 ounce
10 : bird carry a
11 : 5lb coconut

Now close the consumer with Ctrl-C then the broker shell with Ctrl-D.

Create a test configuration file

First, create a test file at configuration/test.properties:

key.serializer=org.apache.kafka.common.serialization.StringSerializer
value.serializer=org.apache.kafka.common.serialization.StringSerializer
acks=all


#Properties below this line are specific to code in this application
output.topic.name=output-topic

Write a unit test

Create a directory for the tests to live in:

mkdir -p src/test/java/io/confluent/developer

Next we will see why the thin wrapper class around the KafkaProducer makes testing easier. The KafkaProducerCallbackApplication accepts an instance of the Producer interface. The use of the interface allows us to inject any concrete type we want, including a Mock Producer for testing.

We use a MockProducer because you only want to test your own code. So you really only need to test that the producer receives the expected records and in the expected format. Plus since there is no broker, the tests run very fast, which becomes an important factor as the number of tests increase.

There is only one method in KafkaProducerCallbackApplicationTest annotated with @Test, and that is testProduce(). Before you create the test, let’s go over a few of the key points of the test

final List<String> records = Arrays.asList("foo#bar", "bar#foo", "baz#bar", "great-weather");

records.forEach(producerApp::produce); (1)

final List<KeyValue<String, String>> expectedList = Arrays.asList(KeyValue.pair("foo", "bar"),
            KeyValue.pair("bar", "foo"),
            KeyValue.pair("baz", "bar"),
            KeyValue.pair("NO-KEY","great:weather")); (2)
final List<KeyValue<String, String>> actualList = mockProducer.history().stream().map(this::toKeyValue).collect(Collectors.toList()); (3)

1	Call the `produce` method
2	Build the expected list of records the producer should receive
3	Use the `MockProducer.history()` method to get the records sent to the producer so the test can assert the expected records match the actual ones sent

Now create the following file at src/test/java/io/confluent/developer/KafkaProducerCallbackApplicationTest.java.

package io.confluent.developer;


import static org.hamcrest.CoreMatchers.equalTo;
import static org.hamcrest.MatcherAssert.assertThat;

import java.io.IOException;
import java.util.Arrays;
import java.util.List;
import java.util.Properties;
import java.util.stream.Collectors;
import org.apache.kafka.clients.producer.MockProducer;
import org.apache.kafka.common.serialization.StringSerializer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.streams.KeyValue;
import org.junit.Test;


public class KafkaProducerCallbackApplicationTest {

    private final static String TEST_CONFIG_FILE = "configuration/test.properties";

    @Test
    public void testProduce() throws IOException {
        final StringSerializer stringSerializer = new StringSerializer();
        final MockProducer<String, String> mockProducer = new MockProducer<>(true, stringSerializer, stringSerializer);
        final Properties props = KafkaProducerCallbackApplication.loadProperties(TEST_CONFIG_FILE);
        final String topic = props.getProperty("output.topic.name");
        final KafkaProducerCallbackApplication producerApp = new KafkaProducerCallbackApplication(mockProducer, topic);
        final List<String> records = Arrays.asList("foo-bar", "bar-foo", "baz-bar", "great:weather");

        records.forEach(producerApp::produce);

        final List<KeyValue<String, String>> expectedList = Arrays.asList(KeyValue.pair("foo", "bar"),
            KeyValue.pair("bar", "foo"),
            KeyValue.pair("baz", "bar"),
            KeyValue.pair(null,"great:weather"));

        final List<KeyValue<String, String>> actualList = mockProducer.history().stream().map(this::toKeyValue).collect(Collectors.toList());

        assertThat(actualList, equalTo(expectedList));
        producerApp.shutdown();
    }


    private KeyValue<String, String> toKeyValue(final ProducerRecord<String, String> producerRecord) {
        return KeyValue.pair(producerRecord.key(), producerRecord.value());
    }
}

Invoke the tests

Now run the test, which is as simple as:

./gradlew test

Run your app with Confluent Cloud

Instead of running a local Kafka cluster, you may use Confluent Cloud, a fully managed Apache Kafka service.

Sign up for Confluent Cloud, a fully managed Apache Kafka service.
After you log in to Confluent Cloud Console, click Environments in the lefthand navigation, click on Add cloud environment, and name the environment learn-kafka. Using a new environment keeps your learning resources separate from your other Confluent Cloud resources.
From the Billing & payment section in the menu, apply the promo code CC100KTS to receive an additional $100 free usage on Confluent Cloud (details). To avoid having to enter a credit card, add an additional promo code CONFLUENTDEV1. With this promo code, you will not have to enter a credit card for 30 days or until your credits run out.
Click on LEARN and follow the instructions to launch a Kafka cluster and enable Schema Registry.

Next, from the Confluent Cloud Console, click on Clients to get the cluster-specific configurations, e.g., Kafka cluster bootstrap servers and credentials, Confluent Cloud Schema Registry and credentials, etc., and set the appropriate parameters in your client application. In the case of this tutorial, add the following properties to the client application’s input properties file, substituting all curly braces with your Confluent Cloud values.

# Required connection configs for Kafka producer, consumer, and admin
bootstrap.servers={{ BROKER_ENDPOINT }}
security.protocol=SASL_SSL
sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username='{{ CLUSTER_API_KEY }}' password='{{ CLUSTER_API_SECRET }}';
sasl.mechanism=PLAIN
# Required for correctness in Apache Kafka clients prior to 2.6
client.dns.lookup=use_all_dns_ips

# Best practice for Kafka producer to prevent data loss
acks=all

# Required connection configs for Confluent Cloud Schema Registry
schema.registry.url=https://{{ SR_ENDPOINT }}
basic.auth.credentials.source=USER_INFO
schema.registry.basic.auth.user.info={{ SR_API_KEY }}:{{ SR_API_SECRET }}

Now you’re all set to run your streaming application locally, backed by a Kafka cluster fully managed by Confluent Cloud.

How to build an Apache Kafka ® producer application with callbacks

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