KStream<String, Rating> ratings = ...
KTable<String, Movie> movies = ...
final MovieRatingJoiner joiner = new MovieRatingJoiner();
KStream<String, RatedMovie> ratedMovie = ratings.join(movies, joiner);
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?
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);
To get started, make a new directory anywhere you’d like for this project:
mkdir join-stream && cd join-stream
Next, create a directory for configuration data:
mkdir configuration
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.
After you log in to Confluent Cloud, 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).
Click on LEARN and follow the instructions to launch a Kafka cluster and to enable Schema Registry.
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/ccloud.properties
file on your machine.
# Required connection configs for Kafka producer, consumer, and admin
bootstrap.servers={{ BOOTSTRAP_SERVERS }}
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={{ SR_URL }}
basic.auth.credentials.source=USER_INFO
basic.auth.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. |
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.
Create the following Gradle build file, named build.gradle
for the project:
buildscript {
repositories {
mavenCentral()
}
dependencies {
classpath 'gradle.plugin.com.github.jengelman.gradle.plugins:shadow:7.0.0'
}
}
plugins {
id 'java'
id 'com.google.cloud.tools.jib' version '3.3.1'
id "com.github.davidmc24.gradle.plugin.avro" version "1.7.0"
}
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.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/dev.properties']
}
jar {
manifest {
attributes(
'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
Then create a development configuration file at configuration/dev.properties
:
application.id=joining-app
replication.factor=3
movie.topic.name=movies
movie.topic.partitions=6
movie.topic.replication.factor=3
rekeyed.movie.topic.name=rekeyed-movies
rekeyed.movie.topic.partitions=6
rekeyed.movie.topic.replication.factor=3
rating.topic.name=ratings
rating.topic.partitions=6
rating.topic.replication.factor=3
rated.movies.topic.name=rated-movies
rated.movies.topic.partitions=6
rated.movies.topic.replication.factor=3
Using the command below, append the contents of configuration/ccloud.properties
(with your Confluent Cloud configuration) to configuration/dev.properties
(with the application properties).
cat configuration/ccloud.properties >> configuration/dev.properties
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 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/JoinStreamToTable.java
. 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.io.FileInputStream;
import java.io.IOException;
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("movie.topic.name");
final String rekeyedMovieTopic = allProps.getProperty("rekeyed.movie.topic.name");
final String ratingTopic = allProps.getProperty("rating.topic.name");
final String ratedMoviesTopic = allProps.getProperty("rated.movies.topic.name");
final MovieRatingJoiner joiner = new MovieRatingJoiner();
KStream<String, Movie> movieStream = builder.<String, Movie>stream(movieTopic)
.map((key, movie) -> new KeyValue<>(String.valueOf(movie.getId()), movie));
movieStream.to(rekeyedMovieTopic);
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);
ratedMovie.to(ratedMoviesTopic, Produced.with(Serdes.String(), ratedMovieAvroSerde(allProps)));
return builder.build();
}
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(
allProps.getProperty("movie.topic.name"),
Integer.parseInt(allProps.getProperty("movie.topic.partitions")),
Short.parseShort(allProps.getProperty("movie.topic.replication.factor"))));
topics.add(new NewTopic(
allProps.getProperty("rekeyed.movie.topic.name"),
Integer.parseInt(allProps.getProperty("rekeyed.movie.topic.partitions")),
Short.parseShort(allProps.getProperty("rekeyed.movie.topic.replication.factor"))));
topics.add(new NewTopic(
allProps.getProperty("rating.topic.name"),
Integer.parseInt(allProps.getProperty("rating.topic.partitions")),
Short.parseShort(allProps.getProperty("rating.topic.replication.factor"))));
topics.add(new NewTopic(
allProps.getProperty("rated.movies.topic.name"),
Integer.parseInt(allProps.getProperty("rated.movies.topic.partitions")),
Short.parseShort(allProps.getProperty("rated.movies.topic.replication.factor"))));
client.createTopics(topics);
client.close();
}
public Properties loadEnvProperties(String fileName) throws IOException {
Properties allProps = new Properties();
FileInputStream input = new FileInputStream(fileName);
allProps.load(input);
input.close();
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);
ts.createTopics(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") {
@Override
public void run() {
streams.close(Duration.ofSeconds(5));
latch.countDown();
}
});
try {
streams.start();
latch.await();
} catch (Throwable e) {
System.exit(1);
}
System.exit(0);
}
}
For the ValueJoiner class, create the following file at src/main/java/io/confluent/developer/MovieRatingJoiner.java
.
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()
.setId(movie.getId())
.setTitle(movie.getTitle())
.setReleaseYear(movie.getReleaseYear())
.setRating(rating.getRating())
.build();
}
}
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/dev.properties
In a new terminal, run:
confluent kafka topic produce movies --value-format avro --schema src/main/avro/movie.avsc
You will be prompted for the Confluent Cloud Schema Registry credentials as shown below, which you can find in the configuration/ccloud.properties
configuration file.
Look for the configuration parameter basic.auth.user.info
, 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", "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 using ccloud
CLI 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.
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:
confluent kafka topic consume rated-movies --from-beginning --value-format avro
You won’t see any results until the next step.
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:
confluent kafka topic produce ratings --value-format avro --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.
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.
First, create a test file at configuration/test.properties
:
application.id=joining-app
bootstrap.servers=localhost:29092
schema.registry.url=mock://joining-stream-table:8081
movie.topic.name=movies
movie.topic.partitions=1
movie.topic.replication.factor=1
rekeyed.movie.topic.name=rekeyed-movies
rekeyed.movie.topic.partitions=1
rekeyed.movie.topic.replication.factor=1
rating.topic.name=ratings
rating.topic.partitions=1
rating.topic.replication.factor=1
rated.movies.topic.name=rated-movies
rated.movies.topic.partitions=1
rated.movies.topic.replication.factor=1
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/MovieRatingJoinerTest.java
. 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 {
@Test
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")
.setId(354)
.setReleaseYear(2011)
.setRating(9.8)
.build();
MovieRatingJoiner joiner = new MovieRatingJoiner();
actualRatedMovie = joiner.apply(rating, treeOfLife);
assertEquals(actualRatedMovie, expectedRatedMovie);
}
}
Now create the following file at src/test/java/io/confluent/developer/JoinStreamToTableTest.java
. 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.io.IOException;
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/test.properties";
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);
testOutputTopic
.readKeyValuesToList()
.forEach(record -> {
if (record != null) {
results.add(record.value);
}
}
);
return results;
}
@Test
public void testJoin() throws IOException {
JoinStreamToTable jst = new JoinStreamToTable();
Properties allProps = jst.loadEnvProperties(TEST_CONFIG_FILE);
String tableTopic = allProps.getProperty("movie.topic.name");
String streamTopic = allProps.getProperty("rating.topic.name");
String outputTopic = allProps.getProperty("rated.movies.topic.name");
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<>();
ratings.add(Rating.newBuilder().setId(294).setRating(8.2).build());
ratings.add(Rating.newBuilder().setId(294).setRating(8.5).build());
ratings.add(Rating.newBuilder().setId(354).setRating(9.9).build());
ratings.add(Rating.newBuilder().setId(354).setRating(9.7).build());
ratings.add(Rating.newBuilder().setId(782).setRating(7.8).build());
ratings.add(Rating.newBuilder().setId(782).setRating(7.7).build());
ratings.add(Rating.newBuilder().setId(128).setRating(8.7).build());
ratings.add(Rating.newBuilder().setId(128).setRating(8.4).build());
ratings.add(Rating.newBuilder().setId(780).setRating(2.1).build());
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);
}
@After
public void cleanup() {
testDriver.close();
}
}
Now run the test, which is as simple as:
./gradlew test