How to transform a stream of events


How do you transform a field in a stream of events in a Kafka topic?

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Example use case:

Consider a topic with events that represent movies. Each event has a single attribute that combines its title and its release year into a string. In this tutorial, we'll write a program that creates a new topic with the title and release date turned into their own attributes.

Hands-on code example:

New to Confluent Cloud? Get started here.

Short Answer

Use the map() method to take each input record and create a new stream with transformed records in it. The records are transformed via a custom function, in this case convertRawMovie().

KStream<Long, Movie> movies =, rawMovie) ->
                                                new KeyValue<>(rawMovie.getId(), convertRawMovie(rawMovie)));

Run it

Initialize the project


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

mkdir transform-stream && cd transform-stream

Next, create a directory for configuration data:

mkdir configuration

Provision your Kafka cluster


This tutorial requires access to an Apache Kafka cluster, and the quickest way to get started free is on Confluent Cloud, which provides Kafka as a fully managed service.

Take me to Confluent Cloud
  1. After you log in to Confluent Cloud, 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.

  2. 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.

  3. Click on LEARN and follow the instructions to launch a Kafka cluster and enable Schema Registry.

Confluent Cloud

Write the cluster information into a local file


From the Confluent Cloud Console, navigate to your Kafka cluster and then select Clients in the lefthand navigation. From the Clients view, create a new client and click Java to get the connection information customized to your cluster.

Create new credentials for your Kafka cluster and Schema Registry, writing in appropriate descriptions so that the keys are easy to find and delete later. The Confluent Cloud Console will show a configuration similar to below with your new credentials automatically populated (make sure Show API keys is checked). Copy and paste it into a configuration/ file on your machine.

# Required connection configs for Kafka producer, consumer, and admin
bootstrap.servers={{ BOOTSTRAP_SERVERS }}
security.protocol=SASL_SSL required username='{{ CLUSTER_API_KEY }}' password='{{ CLUSTER_API_SECRET }}';
# Required for correctness in Apache Kafka clients prior to 2.6

# Best practice for Kafka producer to prevent data loss

# Required connection configs for Confluent Cloud Schema Registry
schema.registry.url={{ SR_URL }}
basic.auth.credentials.source=USER_INFO{{ SR_API_KEY }}:{{ SR_API_SECRET }}
Do not directly copy and paste the above configuration. You must copy it from the Confluent Cloud Console so that it includes your Confluent Cloud information and credentials.

Download and set up the Confluent CLI


This tutorial has some steps for Kafka topic management and producing and consuming events, for which you can use the Confluent Cloud Console or the Confluent CLI. Follow the instructions here to install the Confluent CLI, and then follow these steps connect the CLI to your Confluent Cloud cluster.

Configure the project


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

buildscript {
  repositories {
  dependencies {
    classpath ''

plugins {
  id 'java'
  id "com.github.davidmc24.gradle.plugin.avro" version "1.7.0"

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

repositories {

  maven {
    url ''

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

dependencies {
  implementation 'org.apache.avro:avro:1.11.1'
  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'
  implementation 'io.confluent:kafka-streams-avro-serde:7.3.0'
  testImplementation 'org.apache.kafka:kafka-streams-test-utils:3.4.0'
  testImplementation 'junit:junit:4.13.2'

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

task run(type: JavaExec) {
  mainClass = 'io.confluent.developer.TransformStream'
  classpath = sourceSets.main.runtimeClasspath
  args = ['configuration/']

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

shadowJar {
  archiveBaseName = "kstreams-transform-standalone"
  archiveClassifier = ''

And be sure to run the following command to obtain the Gradle wrapper:

gradle wrapper

Then create a development configuration file at configuration/

Update the properties file with Confluent Cloud information


Using the command below, append the contents of configuration/ (with your Confluent Cloud configuration) to configuration/ (with the application properties).

cat configuration/ >> configuration/

Create a schema for the events


Create a directory for the schemas that represent the events in the stream:

mkdir -p src/main/avro

Then create the following Avro schema file at src/main/avro/input_movie_event.avsc for the raw movies:

  "namespace": "io.confluent.developer.avro",
  "type": "record",
  "name": "RawMovie",
  "fields": [
    {"name": "id", "type": "long"},
    {"name": "title", "type": "string"},
    {"name": "genre", "type": "string"}

While you’re at it, create another Avro schema file at src/main/avro/parsed_movies.avsc for the transformed movies:

  "namespace": "io.confluent.developer.avro",
  "type": "record",
  "name": "Movie",
  "fields": [
    {"name": "id", "type": "long"},
    {"name": "title", "type": "string"},
    {"name": "release_year", "type": "int"},
    {"name": "genre", "type": "string"}

Because we will use this Avro schema in our Java code, we’ll need to compile it. The Gradle Avro plugin is a part of the build, so it will see your new Avro files, generate Java code for them, and compile those and all other Java sources. Run this command to get it all done:

./gradlew build

Create the Kafka Streams topology


Create a directory for the Java files in this project:

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

Then create the following file at src/main/java/io/confluent/developer/ Let’s take a close look at the buildTopology() method, which uses the Kafka Streams DSL.

The first thing the method does is create an instance of StreamsBuilder, which is the helper object that lets us build our topology. Next we call the stream() method, which creates a KStream object (called rawMovies in this case) out of an underlying Kafka topic. Note the type of that stream is Long, RawMovie, because the topic contains the raw movie objects we want to transform. RawMovie’s title field contains the title and the release year together, which we want to make into separate fields in a new object.

We get that transforming work done with the next line, which is a call to the map() method. map() takes each input record and creates a new stream with transformed records in it. Its parameter is a single Java Lambda that takes the input key and value and returns an instance of the KeyValue class with the new record in it. This does two things. First, it rekeys the incoming stream, using the movieId as the key. We don’t absolutely need to do that to accomplish the transformation, but it’s easy enough to do at the same time, and it sets a useful key on the output stream, which is generally a good idea. Second, it calls the convertRawMovie() method to turn the RawMovie value into a Movie. This is the essence of the transformation. The convertRawMovie() method contains the sort of unpleasant string parsing that is a part of many stream processing pipelines, which we are happily able to encapsulate in a single, easily testable method. Any further stages we might build in the pipeline after this point are blissfully unaware that we ever had a string to parse in the first place.

Moreover, it’s worth noting that we’re calling map() and not mapValues():

package io.confluent.developer;

import java.time.Duration;
import org.apache.kafka.clients.admin.AdminClient;
import org.apache.kafka.clients.admin.NewTopic;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.KeyValue;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.Produced;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.CountDownLatch;

import io.confluent.developer.avro.Movie;
import io.confluent.developer.avro.RawMovie;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;

public class TransformStream {

    public Topology buildTopology(Properties allProps) {
        final StreamsBuilder builder = new StreamsBuilder();
        final String inputTopic = allProps.getProperty("");

        KStream<String, RawMovie> rawMovies =;
        KStream<Long, Movie> movies =, rawMovie) ->
                                                        new KeyValue<>(rawMovie.getId(), convertRawMovie(rawMovie)));"movies", Produced.with(Serdes.Long(), movieAvroSerde(allProps)));


    public static Movie convertRawMovie(RawMovie rawMovie) {
        String[] titleParts = rawMovie.getTitle().split("::");
        String title = titleParts[0];
        int releaseYear = Integer.parseInt(titleParts[1]);
        return new Movie(rawMovie.getId(), title, releaseYear, rawMovie.getGenre());

    private SpecificAvroSerde<Movie> movieAvroSerde(Properties allProps) {
        SpecificAvroSerde<Movie> movieAvroSerde = new SpecificAvroSerde<>();
        movieAvroSerde.configure((Map)allProps, false);
        return movieAvroSerde;

    public void createTopics(Properties allProps) {
        AdminClient client = AdminClient.create(allProps);

        List<NewTopic> topics = new ArrayList<>();

        topics.add(new NewTopic(

        topics.add(new NewTopic(


    public Properties loadEnvProperties(String fileName) throws IOException {
        Properties allProps = new Properties();
        FileInputStream input = new FileInputStream(fileName);

        return allProps;

    public static void main(String[] args) throws Exception {
        if (args.length < 1) {
            throw new IllegalArgumentException("This program takes one argument: the path to an environment configuration file.");

        TransformStream ts = new TransformStream();
        Properties allProps = ts.loadEnvProperties(args[0]);
        allProps.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        allProps.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, SpecificAvroSerde.class);
        Topology topology = ts.buildTopology(allProps);


        final KafkaStreams streams = new KafkaStreams(topology, allProps);
        final CountDownLatch latch = new CountDownLatch(1);

        // Attach shutdown handler to catch Control-C.
        Runtime.getRuntime().addShutdownHook(new Thread("streams-shutdown-hook") {
            public void run() {

        try {
        } catch (Throwable e) {

Compile and run the Kafka Streams program


In your terminal, run:

./gradlew shadowJar

Now that an uberjar for the Kafka Streams application has been built, you can launch it locally. When you run the following, the prompt won’t return, because the application will run until you exit it:

java -jar build/libs/kstreams-transform-standalone-0.0.1.jar configuration/

Produce events to the input topic


In a new terminal, run:

confluent kafka topic produce raw-movies \
      --value-format avro \
      --schema src/main/avro/input_movie_event.avsc

You will be prompted for the Confluent Cloud Schema Registry credentials as shown below, which you can find in the configuration/ configuration file. Look for the configuration parameter, whereby the ":" is the delimiter between the key and secret.

Enter your Schema Registry API key:
Enter your Schema Registry API secret:

When the console producer starts, it will log some messages and hang, waiting for your input. Type in one line at a time and press enter to send it. Each line represents an event. To send all of the events below, paste the following into the prompt and press enter:

{"id": 294, "title": "Die Hard::1988", "genre": "action"}
{"id": 354, "title": "Tree of Life::2011", "genre": "drama"}
{"id": 782, "title": "A Walk in the Clouds::1995", "genre": "romance"}
{"id": 128, "title": "The Big Lebowski::1998", "genre": "comedy"}

Observe the transformed movies in the output topic


Leave your original terminal running. To consume the events produced by your Streams application you’ll need another terminal open.

First, to consume the events of drama films, run the following:

confluent kafka topic consume movies \
      --from-beginning \
      --value-format avro

This should yield the following messages:

{"id":294,"title":"Die Hard","release_year":1988,"genre":"action"}
{"id":354,"title":"Tree of Life","release_year":2011,"genre":"drama"}
{"id":782,"title":"A Walk in the Clouds","release_year":1995,"genre":"romance"}
{"id":128,"title":"The Big Lebowski","release_year":1998,"genre":"comedy"}

Teardown Confluent Cloud resources


You may try another tutorial, but if you don’t plan on doing other tutorials, use the Confluent Cloud Console or CLI to destroy all of the resources you created. Verify they are destroyed to avoid unexpected charges.

Test it

Create a test configuration file


First, create a test file at configuration/

Write a test


Then, create a directory for the tests to live in:

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

Create the following test file at src/test/java/io/confluent/developer/ Testing a Kafka streams application requires a bit of test harness code, but happily the org.apache.kafka.streams.TopologyTestDriver class makes this much more pleasant that it would otherwise be.

There are two methods in TransformStreamTest annotated with @Test: testMovieConverter() and testTransformStream(). testMovieConverter() is a simple method that tests the string that is core to the transformation action of this Streams application. testMovieConverter() actually runs our Streams topology using the TopologyTestDriver and some mocked data that is set up inside the test method.

package io.confluent.developer;

import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;
import org.apache.kafka.common.serialization.Deserializer;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.common.serialization.Serializer;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.TestInputTopic;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.TopologyTestDriver;
import org.junit.After;
import org.junit.Test;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.Properties;

import io.confluent.developer.avro.Movie;
import io.confluent.developer.avro.RawMovie;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroDeserializer;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerializer;

import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;

public class TransformStreamTest {

    private final static String TEST_CONFIG_FILE = "configuration/";
    private TopologyTestDriver testDriver;

    public SpecificAvroSerializer<RawMovie> makeSerializer(Properties allProps) {
        SpecificAvroSerializer<RawMovie> serializer = new SpecificAvroSerializer<>();

        Map<String, String> config = new HashMap<>();
        config.put("schema.registry.url", allProps.getProperty("schema.registry.url"));
        serializer.configure(config, false);

        return serializer;

    public SpecificAvroDeserializer<Movie> makeDeserializer(Properties allProps) {
        SpecificAvroDeserializer<Movie> deserializer = new SpecificAvroDeserializer<>();

        Map<String, String> config = new HashMap<>();
        config.put("schema.registry.url", allProps.getProperty("schema.registry.url"));
        deserializer.configure(config, false);

        return deserializer;

    private List<Movie> readOutputTopic(TopologyTestDriver testDriver,
                                        String topic,
                                        Deserializer<String> keyDeserializer,
                                        SpecificAvroDeserializer<Movie> valueDeserializer) {

        return testDriver
            .createOutputTopic(topic, keyDeserializer, valueDeserializer)
            .map(record -> record.value)

    public void testMovieConverter() {
        Movie movie;

        movie = TransformStream.convertRawMovie(new RawMovie(294L, "Tree of Life::2011", "drama"));
        assertEquals(294L, movie.getId());
        assertEquals("Tree of Life", movie.getTitle());
        assertEquals(2011, movie.getReleaseYear());
        assertEquals("drama", movie.getGenre());

    public void testTransformStream() throws IOException {
        TransformStream ts = new TransformStream();
        Properties allProps = ts.loadEnvProperties(TEST_CONFIG_FILE);
        allProps.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        allProps.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, SpecificAvroSerde.class);

        String inputTopic = allProps.getProperty("");
        String outputTopic = allProps.getProperty("");

        Topology topology = ts.buildTopology(allProps);
        testDriver = new TopologyTestDriver(topology, allProps);

        Serializer<String> keySerializer = Serdes.String().serializer();
        SpecificAvroSerializer<RawMovie> valueSerializer = makeSerializer(allProps);

        Deserializer<String> keyDeserializer = Serdes.String().deserializer();
        SpecificAvroDeserializer<Movie> valueDeserializer = makeDeserializer(allProps);

        List<RawMovie> input = new ArrayList<>();
        input.add(RawMovie.newBuilder().setId(294).setTitle("Die Hard::1988").setGenre("action").build());
        input.add(RawMovie.newBuilder().setId(354).setTitle("Tree of Life::2011").setGenre("drama").build());
        input.add(RawMovie.newBuilder().setId(782).setTitle("A Walk in the Clouds::1995").setGenre("romance").build());
        input.add(RawMovie.newBuilder().setId(128).setTitle("The Big Lebowski::1998").setGenre("comedy").build());

        List<Movie> expectedOutput = new ArrayList<>();
        expectedOutput.add(Movie.newBuilder().setTitle("Die Hard").setId(294).setReleaseYear(1988).setGenre("action").build());
        expectedOutput.add(Movie.newBuilder().setTitle("Tree of Life").setId(354).setReleaseYear(2011).setGenre("drama").build());
        expectedOutput.add(Movie.newBuilder().setTitle("A Walk in the Clouds").setId(782).setReleaseYear(1995).setGenre("romance").build());
        expectedOutput.add(Movie.newBuilder().setTitle("The Big Lebowski").setId(128).setReleaseYear(1998).setGenre("comedy").build());

        final TestInputTopic<String, RawMovie>
            testDriverInputTopic =
            testDriver.createInputTopic(inputTopic, keySerializer, valueSerializer);

        for (RawMovie rawMovie : input) {
            testDriverInputTopic.pipeInput(rawMovie.getTitle(), rawMovie);
        List<Movie> actualOutput = readOutputTopic(testDriver, outputTopic, keyDeserializer, valueDeserializer);

        assertEquals(expectedOutput, actualOutput);

    public void cleanup() {
        if (testDriver != null) {


Invoke the tests


Now run the test, which is as simple as:

./gradlew test