How to transform a stream of events


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

Edit this page

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:

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



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 transform-stream && cd transform-stream

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'
    image: confluentinc/cp-kafka:7.4.1
    hostname: broker
    container_name: broker
    - 29092:29092
      KAFKA_PROCESS_ROLES: broker,controller
      KAFKA_NODE_ID: 1
      KAFKA_LOG_DIRS: /tmp/kraft-combined-logs
    image: confluentinc/cp-schema-registry:7.3.0
    hostname: schema-registry
    container_name: schema-registry
    - broker
    - 8081:8081
      SCHEMA_REGISTRY_HOST_NAME: schema-registry

And launch it by running:

docker compose up -d

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

Next, create a directory for configuration data:

mkdir configuration

Then create a development file at 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:

docker exec -i schema-registry /usr/bin/kafka-avro-console-producer --topic raw-movies --bootstrap-server broker:9092 --property value.schema="$(< src/main/avro/input_movie_event.avsc)"

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:

docker exec -it schema-registry /usr/bin/kafka-avro-console-consumer --topic movies --bootstrap-server broker:9092 --from-beginning

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"}

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

Deploy on Confluent Cloud

Run your app with Confluent Cloud


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

  1. Sign up for Confluent Cloud, a fully managed Apache Kafka service.

  2. 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.

  3. 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.

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

Confluent Cloud

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 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=https://{{ SR_ENDPOINT }}
basic.auth.credentials.source=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.