How to join a stream and a lookup table


If you have events in a Kafka topic and a table of reference data (also known as a lookup table), how can you join each event in the stream to a piece of data in the table based on a common key?

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Example use case:

Suppose you have a set of movies that have been released and a stream of ratings from moviegoers about how entertaining they are. In this tutorial, we'll write a program that joins each rating with content about the movie. Related pattern: Event Joiner

Hands-on code example:

Short Answer

Use the builder.table() method to create a KTable. Then use the ValueJoiner interface in the Streams API to join the KStream and KTable.

KStream<String, Rating> ratings = ...
KTable<String, Movie> movies = ...
final MovieRatingJoiner joiner = new MovieRatingJoiner();
KStream<String, RatedMovie> ratedMovie = ratings.join(movies, joiner);

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 join-stream && cd join-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.JoinStreamToTable'
  classpath = sourceSets.main.runtimeClasspath
  args = ['configuration/']

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

shadowJar {
  archiveBaseName = "kstreams-stream-table-join-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


This tutorial uses three streams: one called movies that holds movie reference data, one called ratings that holds a stream of inbound movie ratings, and one called rated-movies that holds the result of the join between ratings and movies. Let’s create schemas for all three.

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/movie.avsc for the movies lookup table:

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

Next, create another Avro schema file at src/main/avro/rating.avsc for the stream of ratings:

  "namespace": "io.confluent.developer.avro",
  "type": "record",
  "name": "Rating",
  "fields": [
    {"name": "id", "type": "long"},
    {"name": "rating", "type": "double"}

And finally, create another Avro schema file at src/main/avro/rated-movie.avsc for the result of the join:

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

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. With our builder in hand, there are three things we need to do. First, we call the stream() method to create a KStream<String, Movie> object. The problem is that we can’t make any assumptions about the key of this stream, so we have to repartition it explicitly. We use the map() method for that, creating a new KeyValue instance for each record, using the movie ID as the new key.

The movies start their life in a stream, but fundamentally, movies are entities that belong in a table. To turn them into a table, we first emit the rekeyed stream to a Kafka topic using the to() method. We can then use the builder.table() method to create a KTable<String,Movie>. We have successfully turned a topic full of movie entities into a scalable, key-addressable table of Movie objects. With that, we’re ready to move on to ratings.

Creating the KStream<String,Rating> of ratings looks just like our first step with the movies: we create a stream from the topic, then repartition it with the map() method. Note that we must choose the same key—movie ID—for our join to work.

With the ratings stream and the movie table in hand, all that remains is to join them using the join() method. It’s a wonderfully simply one-liner, but we have concealed a bit of complexity in the form of the MovieRatingJoiner class. More on that in a moment.

package io.confluent.developer;

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.KTable;
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 java.time.Duration;

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

import static io.confluent.kafka.serializers.AbstractKafkaSchemaSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG;

public class JoinStreamToTable {

    public Topology buildTopology(Properties allProps) {
        final StreamsBuilder builder = new StreamsBuilder();
        final String movieTopic = allProps.getProperty("");
        final String rekeyedMovieTopic = allProps.getProperty("");
        final String ratingTopic = allProps.getProperty("");
        final String ratedMoviesTopic = allProps.getProperty("");
        final MovieRatingJoiner joiner = new MovieRatingJoiner();

        KStream<String, Movie> movieStream = builder.<String, Movie>stream(movieTopic)
                .map((key, movie) -> new KeyValue<>(String.valueOf(movie.getId()), movie));;

        KTable<String, Movie> movies = builder.table(rekeyedMovieTopic);

        KStream<String, Rating> ratings = builder.<String, Rating>stream(ratingTopic)
                .map((key, rating) -> new KeyValue<>(String.valueOf(rating.getId()), rating));

        KStream<String, RatedMovie> ratedMovie = ratings.join(movies, joiner);, Produced.with(Serdes.String(), ratedMovieAvroSerde(allProps)));


    private SpecificAvroSerde<RatedMovie> ratedMovieAvroSerde(Properties allProps) {
        SpecificAvroSerde<RatedMovie> 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(

        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.");

        JoinStreamToTable ts = new JoinStreamToTable();
        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) {

Implement a ValueJoiner class


For the ValueJoiner class, create the following file at src/main/java/io/confluent/developer/

When you join two tables in a relational database, by default you get a new table containing all of the columns of the left table plus all of the columns of the right table. When you join a stream and a table, you get a new stream, but you must be explicit about the value of that stream—the combination between the value in the stream and the associated value in the table. The ValueJoiner interface in the Streams API does this work. The single apply() method takes the stream and table values as parameters, and returns the value of the joined stream as output. (Their keys are not a part of the equation, because they are equal by definition and do not change in the result.) As you can see here, this is just a matter of creating a RatedMovie object and populating it with the relevant fields of the input movie and rating.

You can do this in a Java Lambda in the call to the join() method where you’re building the stream topology, but the joining logic may become complex, and breaking it off into its own trivially testable class is a good move.

package io.confluent.developer;

import org.apache.kafka.streams.kstream.ValueJoiner;

import io.confluent.developer.avro.Movie;
import io.confluent.developer.avro.RatedMovie;
import io.confluent.developer.avro.Rating;

public class MovieRatingJoiner implements ValueJoiner<Rating, Movie, RatedMovie> {

  public RatedMovie apply(Rating rating, Movie movie) {
    return RatedMovie.newBuilder()

Compile and run the Kafka Streams program


In your terminal, run:

./gradlew shadowJar

Now that you have an uberjar for the Kafka Streams application, you can launch it locally. When you run the following, the prompt won’t return, because the application will run until you exit it. There is always another message to process, so streaming applications don’t exit until you force them.

java -jar build/libs/kstreams-stream-table-join-standalone-0.0.1.jar configuration/

Load in some movie reference data


In a new terminal, run:

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

When the console producer starts, it will log some messages and hang, waiting for your input. Copy and paste one line at a time and press enter to send it. Note that these lines contain hard tabs between the key and the value, so retyping them without the tab will not work.

Each line represents a movie we will be able to rate. To send all of the events below, paste the following into the prompt and press enter:

{"id": 294, "title": "Die Hard", "release_year": 1988}
{"id": 354, "title": "Tree of Life", "release_year": 2011}
{"id": 782, "title": "A Walk in the Clouds", "release_year": 1995}
{"id": 128, "title": "The Big Lebowski", "release_year": 1998}
{"id": 780, "title": "Super Mario Bros.", "release_year": 1993}

In this case the table data originates from a Kafka topic that was populated by a console producer but this doesn’t always have to be the case. You can use Kafka Connect to stream data from a source system (such as a database) into a Kafka topic, which could then be the foundation for a lookup table. For further reading checkout this tutorial on creating a Kafka Streams table from SQLite data using Kafka Connect.

Get ready to observe the rated movies in the output topic


Before you start producing ratings, it’s a good idea to set up the consumer on the output topic. This way, as soon as you produce ratings (and they’re joined to movies), you’ll see the results right away. Run this to get ready to consume the rated movies:

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

You won’t see any results until the next step.

Produce some ratings to the input topic


Run the following in a new terminal window. This process is the most fun if you can see this and the previous terminal (which is consuming the rated movies) at the same time. If your terminal program lets you do horizontal split panes, try it that way:

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

When the producer starts up, copy and paste these lines into the terminal. Try doing them one at a time, observing the results in the consumer terminal:

{"id": 294, "rating": 8.2}
{"id": 294, "rating": 8.5}
{"id": 354, "rating": 9.9}
{"id": 354, "rating": 9.7}
{"id": 782, "rating": 7.8}
{"id": 782, "rating": 7.7}
{"id": 128, "rating": 8.7}
{"id": 128, "rating": 8.4}
{"id": 780, "rating": 2.1}

Speaking of that consumer terminal, these are the results you should see there if you paste in all the movies and ratings as shown in this tutorial:

{"id":294,"title":"Die Hard","release_year":1988,"rating":8.2}
{"id":294,"title":"Die Hard","release_year":1988,"rating":8.5}
{"id":354,"title":"Tree of Life","release_year":2011,"rating":9.9}
{"id":354,"title":"Tree of Life","release_year":2011,"rating":9.7}
{"id":782,"title":"A Walk in the Clouds","release_year":1995,"rating":7.8}
{"id":782,"title":"A Walk in the Clouds","release_year":1995,"rating":7.7}
{"id":128,"title":"The Big Lebowski","release_year":1998,"rating":8.7}
{"id":128,"title":"The Big Lebowski","release_year":1998,"rating":8.4}
{"id":780,"title":"Super Mario Bros.","release_year":1993,"rating":2.1}

You have now joined a stream to a table! Well done.

Test it

Create a test configuration file


First, create a test file at configuration/

Test the MovieRatingJoiner class


Create a directory for the tests to live in:

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

Create the following file at src/test/java/io/confluent/developer/ This tests the helper class that merges the value of the movie and the rating as each rating is joined to a movie. The class has a dependency on the ValueJoiner interface, but otherwise does not depend on anything external to our domain; it just needs Movie, Rating, and RatedMovie` domain objects. As such, it’s about as testable as code gets:

package io.confluent.developer;

import org.junit.Test;

import io.confluent.developer.avro.Movie;
import io.confluent.developer.avro.RatedMovie;
import io.confluent.developer.avro.Rating;

import static org.junit.Assert.assertEquals;

public class MovieRatingJoinerTest {

  public void apply() {
    RatedMovie actualRatedMovie;

    Movie treeOfLife = Movie.newBuilder().setTitle("Tree of Life").setId(354).setReleaseYear(2011).build();
    Rating rating = Rating.newBuilder().setId(354).setRating(9.8).build();
    RatedMovie expectedRatedMovie = RatedMovie.newBuilder()
        .setTitle("Tree of Life")

    MovieRatingJoiner joiner = new MovieRatingJoiner();
    actualRatedMovie = joiner.apply(rating, treeOfLife);

    assertEquals(actualRatedMovie, expectedRatedMovie);

Test the streams topology


Now create the following 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 is only one method in JoinStreamToTableTest annotated with @Test, and that is testJoin(). This method 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 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.TestInputTopic;
import org.apache.kafka.streams.TestOutputTopic;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.TopologyTestDriver;
import org.apache.kafka.streams.test.TestRecord;
import org.apache.kafka.streams.StreamsConfig;
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.Properties;

import io.confluent.developer.avro.Movie;
import io.confluent.developer.avro.RatedMovie;
import io.confluent.developer.avro.Rating;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroDeserializer;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerializer;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;

import static org.junit.Assert.assertEquals;

public class JoinStreamToTableTest {

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

    private SpecificAvroSerializer<Movie> makeMovieSerializer(Properties allProps) {
        SpecificAvroSerializer<Movie> 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;

    private SpecificAvroSerializer<Rating> makeRatingSerializer(Properties allProps) {
        SpecificAvroSerializer<Rating> 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;

    private SpecificAvroDeserializer<RatedMovie> makeRatedMovieDeserializer(Properties allProps) {
        SpecificAvroDeserializer<RatedMovie> 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<RatedMovie> readOutputTopic(TopologyTestDriver testDriver,
                                             String topic,
                                             Deserializer<String> keyDeserializer,
                                             SpecificAvroDeserializer<RatedMovie> makeRatedMovieDeserializer) {
        List<RatedMovie> results = new ArrayList<>();
        final TestOutputTopic<String, RatedMovie>
            testOutputTopic =
            testDriver.createOutputTopic(topic, keyDeserializer, makeRatedMovieDeserializer);
            .forEach(record -> {
                         if (record != null) {
        return results;

    public void testJoin() throws IOException {
        JoinStreamToTable jst = new JoinStreamToTable();
        Properties allProps = jst.loadEnvProperties(TEST_CONFIG_FILE);

        String tableTopic = allProps.getProperty("");
        String streamTopic = allProps.getProperty("");
        String outputTopic = allProps.getProperty("");
        allProps.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        allProps.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, SpecificAvroSerde.class);

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

        Serializer<String> keySerializer = Serdes.String().serializer();
        SpecificAvroSerializer<Movie> movieSerializer = makeMovieSerializer(allProps);
        SpecificAvroSerializer<Rating> ratingSerializer = makeRatingSerializer(allProps);

        Deserializer<String> stringDeserializer = Serdes.String().deserializer();
        SpecificAvroDeserializer<RatedMovie> valueDeserializer = makeRatedMovieDeserializer(allProps);

        List<Movie> movies = new ArrayList<>();
        movies.add(Movie.newBuilder().setId(294).setTitle("Die Hard").setReleaseYear(1988).build());
        movies.add(Movie.newBuilder().setId(354).setTitle("Tree of Life").setReleaseYear(2011).build());
        movies.add(Movie.newBuilder().setId(782).setTitle("A Walk in the Clouds").setReleaseYear(1998).build());
        movies.add(Movie.newBuilder().setId(128).setTitle("The Big Lebowski").setReleaseYear(1998).build());
        movies.add(Movie.newBuilder().setId(780).setTitle("Super Mario Bros.").setReleaseYear(1993).build());

        List<Rating> ratings = new ArrayList<>();

        List<RatedMovie> ratedMovies = new ArrayList<>();
        ratedMovies.add(RatedMovie.newBuilder().setTitle("Die Hard").setId(294).setReleaseYear(1988).setRating(8.2).build());
        ratedMovies.add(RatedMovie.newBuilder().setTitle("Die Hard").setId(294).setReleaseYear(1988).setRating(8.5).build());
        ratedMovies.add(RatedMovie.newBuilder().setTitle("Tree of Life").setId(354).setReleaseYear(2011).setRating(9.9).build());
        ratedMovies.add(RatedMovie.newBuilder().setTitle("Tree of Life").setId(354).setReleaseYear(2011).setRating(9.7).build());
        ratedMovies.add(RatedMovie.newBuilder().setId(782).setTitle("A Walk in the Clouds").setReleaseYear(1998).setRating(7.8).build());
        ratedMovies.add(RatedMovie.newBuilder().setId(782).setTitle("A Walk in the Clouds").setReleaseYear(1998).setRating(7.7).build());
        ratedMovies.add(RatedMovie.newBuilder().setId(128).setTitle("The Big Lebowski").setReleaseYear(1998).setRating(8.7).build());
        ratedMovies.add(RatedMovie.newBuilder().setId(128).setTitle("The Big Lebowski").setReleaseYear(1998).setRating(8.4).build());
        ratedMovies.add(RatedMovie.newBuilder().setId(780).setTitle("Super Mario Bros.").setReleaseYear(1993).setRating(2.1).build());

        final TestInputTopic<String, Movie>
            movieTestInputTopic = testDriver.createInputTopic(tableTopic, keySerializer, movieSerializer);

        for (Movie movie : movies) {
            movieTestInputTopic.pipeInput(String.valueOf(movie.getId()), movie);

        final TestInputTopic<String, Rating>
            ratingTestInputTopic =
            testDriver.createInputTopic(streamTopic, keySerializer, ratingSerializer);
        for (Rating rating : ratings) {
            ratingTestInputTopic.pipeInput(String.valueOf(rating.getId()), rating);

        List<RatedMovie> actualOutput = readOutputTopic(testDriver, outputTopic, stringDeserializer, valueDeserializer);

        assertEquals(ratedMovies, actualOutput);

    public void cleanup() {


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.