How do you combine aggregate values, like `count`, from multiple streams into a single result?
You can run your application with Confluent Cloud.
In the Kafka Streams application, use a combination of cogroup
and and aggregate
methods as shown below.
final Aggregator<String, LoginEvent, LoginRollup> loginAggregator = new LoginAggregator();
final KGroupedStream<String, LoginEvent> appOneGrouped = appOneStream.groupByKey();
final KGroupedStream<String, LoginEvent> appTwoGrouped = appTwoStream.groupByKey();
final KGroupedStream<String, LoginEvent> appThreeGrouped = appThreeStream.groupByKey();
appOneGrouped.cogroup(loginAggregator)
.cogroup(appTwoGrouped, loginAggregator)
.cogroup(appThreeGrouped, loginAggregator)
.aggregate(() -> new LoginRollup(new HashMap<>()), Materialized.with(Serdes.String(), loginRollupSerde))
.toStream().to(totalResultOutputTopic, Produced.with(stringSerde, loginRollupSerde));
To get started, make a new directory anywhere you’d like for this project:
mkdir cogrouping-streams && cd cogrouping-streams
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/or reading from or writing to Kafka topics, for which you can use the Confluent Cloud Console or install the Confluent CLI.
Instructions for installing Confluent CLI and configuring it to your Confluent Cloud environment is available from within the Confluent Cloud Console: navigate to your Kafka cluster, click on the CLI and tools
link, and run through the steps in the Confluent CLI
tab.
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 "idea"
id "eclipse"
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"
testImplementation 'org.hamcrest:hamcrest:2.2'
}
test {
testLogging {
outputs.upToDateWhen { false }
showStandardStreams = true
exceptionFormat = "full"
}
}
jar {
manifest {
attributes(
"Class-Path": configurations.compileClasspath.collect { it.getName() }.join(" "),
"Main-Class": "io.confluent.developer.CogroupingStreams"
)
}
}
shadowJar {
archiveBaseName = "cogrouping-streams-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=cogrouping-streams
replication.factor=3
app-one.topic.name=app-one-topic
app-one.topic.partitions=6
app-one.topic.replication.factor=3
app-two.topic.name=app-two-topic
app-two.topic.partitions=6
app-two.topic.replication.factor=3
app-three.topic.name=app-three-topic
app-three.topic.partitions=6
app-three.topic.replication.factor=3
output.topic.name=output-topic
output.topic.partitions=6
output.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 4 streams. The three input streams have a record type of LoginEvent
used to represent a user logging into an application. The fourth stream is an output stream that writes a LoginRollup
object out to a topic. In the next steps you’ll create the Avro schemas for these objects.
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/login-event.avsc
to create the LoginEvent
event:
{
"namespace": "io.confluent.developer.avro",
"type": "record",
"name": "LoginEvent",
"fields": [
{"name": "app_id", "type": "string"},
{"name": "user_id", "type": "string"},
{"name": "time", "type": "long"}
]
}
Next create another schema file src/main/avro/login-rollup.avsc
to create the LoginRollup
for the cogrouping result:
{
"namespace": "io.confluent.developer.avro",
"type": "record",
"name": "LoginRollup",
"fields": [
{"name": "login_by_app_and_user", "type": {
"type": "map",
"values": {
"type": "map",
"values": {"type": "long"}
}
}
}
]
}
Because we will use an 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
Before you create the Java class to run the Cogrouping
example, let’s dive into the main point of this tutorial, how we use cogrouping:
final Aggregator<String, LoginEvent, LoginRollup> loginAggregator = new LoginAggregator();
final KGroupedStream<String, LoginEvent> appOneGrouped = appOneStream.groupByKey();
final KGroupedStream<String, LoginEvent> appTwoGrouped = appTwoStream.groupByKey();
final KGroupedStream<String, LoginEvent> appThreeGrouped = appThreeStream.groupByKey();
appOneGrouped.cogroup(loginAggregator)
.cogroup(appTwoGrouped, loginAggregator)
.cogroup(appThreeGrouped, loginAggregator)
.aggregate(() -> new LoginRollup(new HashMap<>()), Materialized.with(Serdes.String(), loginRollupSerde))
.toStream().to(totalResultOutputTopic, Produced.with(stringSerde, loginRollupSerde));
You’re using the cogrouping functionality here to get an overall grouping of logins per application. Kafka Streams creates this total grouping by using an Aggregator
who knows how to extract records from each grouped stream. Your Aggregator
instance here knows how to correctly combine each LoginEvent
into the larger LoginRollup
object. You’ll learn more about Aggregator
in the next step.
Next, you have three input streams: appOneStream
, appTwoStream
, and appThreeStream
. You need the intermediate object KGroupedStream
, so you execute the groupByKey()
method on each stream. For this tutorial, we have assumed the incoming records already have keys. In cases where records lack keys, you need to use a key-selecting method (selectKey()
, map()
, or groupBy()
) to successfully group by key.
Now with your KGroupedStream
objects, you start creating your larger aggregate by calling KGroupedStream.cogroup()
on the first stream, using your Aggregator
. This first step returns a CogroupedKStream
instance. Then for each remaining KGroupedStream
, you execute CogroupedKSteam.cogroup()
using one of the KGroupedStream
instances and the Aggregator
you created previously. You repeat this sequence of calls for all of the KGroupedStream
objects you want to combine into an overall aggregate.
For more background on cogrouping functionality in stream you can read the KIP-150 proposal.
Now go ahead and create the Java file at src/main/java/io/confluent/developer/CogroupingStreams.java
.
package io.confluent.developer;
import io.confluent.common.utils.TestUtils;
import io.confluent.developer.avro.LoginEvent;
import io.confluent.developer.avro.LoginRollup;
import io.confluent.kafka.serializers.AbstractKafkaSchemaSerDeConfig;
import io.confluent.kafka.serializers.KafkaAvroDeserializer;
import io.confluent.kafka.serializers.KafkaAvroSerializer;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;
import java.io.FileInputStream;
import java.io.IOException;
import java.time.Duration;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.CountDownLatch;
import org.apache.avro.specific.SpecificRecord;
import org.apache.kafka.clients.admin.AdminClient;
import org.apache.kafka.clients.admin.NewTopic;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.common.serialization.Serde;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.common.serialization.StringSerializer;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.kstream.Aggregator;
import org.apache.kafka.streams.kstream.Consumed;
import org.apache.kafka.streams.kstream.KGroupedStream;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.Materialized;
import org.apache.kafka.streams.kstream.Produced;
public class CogroupingStreams {
public Topology buildTopology(Properties allProps) {
final StreamsBuilder builder = new StreamsBuilder();
final String appOneInputTopic = allProps.getProperty("app-one.topic.name");
final String appTwoInputTopic = allProps.getProperty("app-two.topic.name");
final String appThreeInputTopic = allProps.getProperty("app-three.topic.name");
final String totalResultOutputTopic = allProps.getProperty("output.topic.name");
final Serde<String> stringSerde = Serdes.String();
final Serde<LoginEvent> loginEventSerde = getSpecificAvroSerde(allProps);
final Serde<LoginRollup> loginRollupSerde = getSpecificAvroSerde(allProps);
final KStream<String, LoginEvent> appOneStream = builder.stream(appOneInputTopic, Consumed.with(stringSerde, loginEventSerde));
final KStream<String, LoginEvent> appTwoStream = builder.stream(appTwoInputTopic, Consumed.with(stringSerde, loginEventSerde));
final KStream<String, LoginEvent> appThreeStream = builder.stream(appThreeInputTopic, Consumed.with(stringSerde, loginEventSerde));
final Aggregator<String, LoginEvent, LoginRollup> loginAggregator = new LoginAggregator();
final KGroupedStream<String, LoginEvent> appOneGrouped = appOneStream.groupByKey();
final KGroupedStream<String, LoginEvent> appTwoGrouped = appTwoStream.groupByKey();
final KGroupedStream<String, LoginEvent> appThreeGrouped = appThreeStream.groupByKey();
appOneGrouped.cogroup(loginAggregator)
.cogroup(appTwoGrouped, loginAggregator)
.cogroup(appThreeGrouped, loginAggregator)
.aggregate(() -> new LoginRollup(new HashMap<>()), Materialized.with(Serdes.String(), loginRollupSerde))
.toStream().to(totalResultOutputTopic, Produced.with(stringSerde, loginRollupSerde));
return builder.build();
}
static <T extends SpecificRecord> SpecificAvroSerde<T> getSpecificAvroSerde(final Properties allProps) {
final SpecificAvroSerde<T> specificAvroSerde = new SpecificAvroSerde<>();
specificAvroSerde.configure((Map)allProps, false);
return specificAvroSerde;
}
public void createTopics(final Properties allProps) {
try (final AdminClient client = AdminClient.create(allProps)) {
final List<NewTopic> topics = new ArrayList<>();
topics.add(new NewTopic(
allProps.getProperty("app-one.topic.name"),
Integer.parseInt(allProps.getProperty("app-one.topic.partitions")),
Short.parseShort(allProps.getProperty("app-one.topic.replication.factor"))));
topics.add(new NewTopic(
allProps.getProperty("app-two.topic.name"),
Integer.parseInt(allProps.getProperty("app-two.topic.partitions")),
Short.parseShort(allProps.getProperty("app-two.topic.replication.factor"))));
topics.add(new NewTopic(
allProps.getProperty("app-three.topic.name"),
Integer.parseInt(allProps.getProperty("app-three.topic.partitions")),
Short.parseShort(allProps.getProperty("app-three.topic.replication.factor"))));
topics.add(new NewTopic(
allProps.getProperty("output.topic.name"),
Integer.parseInt(allProps.getProperty("output.topic.partitions")),
Short.parseShort(allProps.getProperty("output.topic.replication.factor"))));
client.createTopics(topics);
}
}
public Properties loadEnvProperties(String fileName) throws IOException {
final Properties allProps = new Properties();
final 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.");
}
final CogroupingStreams instance = new CogroupingStreams();
final Properties allProps = instance.loadEnvProperties(args[0]);
final Topology topology = instance.buildTopology(allProps);
instance.createTopics(allProps);
TutorialDataGenerator dataGenerator = new TutorialDataGenerator(allProps);
dataGenerator.generate();
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);
}
static class TutorialDataGenerator {
final Properties properties;
public TutorialDataGenerator(final Properties properties) {
this.properties = properties;
}
public void generate() {
properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, KafkaAvroSerializer.class);
try (Producer<String, LoginEvent> producer = new KafkaProducer<String, LoginEvent>(properties)) {
HashMap<String, List<LoginEvent>> entryData = new HashMap<>();
List<LoginEvent> messages1 = Arrays.asList(new LoginEvent("one", "Ted", 12456L),
new LoginEvent("one", "Ted", 12457L),
new LoginEvent("one", "Carol", 12458L),
new LoginEvent("one", "Carol", 12458L),
new LoginEvent("one", "Alice", 12458L),
new LoginEvent("one", "Carol", 12458L));
final String topic1 = properties.getProperty("app-one.topic.name");
entryData.put(topic1, messages1);
List<LoginEvent> messages2 = Arrays.asList(new LoginEvent("two", "Bob", 12456L),
new LoginEvent("two", "Carol", 12457L),
new LoginEvent("two", "Ted", 12458L),
new LoginEvent("two", "Carol", 12459L));
final String topic2 = properties.getProperty("app-two.topic.name");
entryData.put(topic2, messages2);
List<LoginEvent> messages3 = Arrays.asList(new LoginEvent("three", "Bob", 12456L),
new LoginEvent("three", "Alice", 12457L),
new LoginEvent("three", "Alice", 12458L),
new LoginEvent("three", "Carol", 12459L));
final String topic3 = properties.getProperty("app-three.topic.name");
entryData.put(topic3, messages3);
entryData.forEach((topic, list) ->
list.forEach(message ->
producer.send(new ProducerRecord<String, LoginEvent>(topic, message.getAppId(), message), (metadata, exception) -> {
if (exception != null) {
exception.printStackTrace(System.out);
} else {
System.out.printf("Produced record at offset %d to topic %s %n", metadata.offset(), metadata.topic());
}
})
)
);
}
}
}
}
The Aggregator
you saw in the previous step constructs a map of maps: the count of logins per user, per application. Below is the core logic of the LoginAggregator
.
Each call to Aggregator.apply
retrieves the user login map for the given application id (or creates one if it doesn’t exist). From there, the Aggregator
increments the login count for the given user.
final String userId = loginEvent.getUserId();
final Map<String, Map<String, Long>> allLogins = loginRollup.getLoginByAppAndUser();
final Map<String, Long> userLogins = allLogins.computeIfAbsent(appId, key -> new HashMap<>());
userLogins.compute(userId, (k, v) -> v == null ? 1L : v + 1L);
While you could add the Aggregator
instance as an in-line lambda to the topology, creating a separate class allows you to test the aggregator in isolation.
Next, create the following file at src/main/java/io/confluent/developer/LoginAggregator.java
.
package io.confluent.developer;
import io.confluent.developer.avro.LoginEvent;
import io.confluent.developer.avro.LoginRollup;
import java.util.HashMap;
import java.util.Map;
import org.apache.kafka.streams.kstream.Aggregator;
public class LoginAggregator implements Aggregator<String, LoginEvent, LoginRollup> {
@Override
public LoginRollup apply(final String appId,
final LoginEvent loginEvent,
final LoginRollup loginRollup) {
final String userId = loginEvent.getUserId();
final Map<String, Map<String, Long>> allLogins = loginRollup.getLoginByAppAndUser();
final Map<String, Long> userLogins = allLogins.computeIfAbsent(appId, key -> new HashMap<>());
userLogins.compute(userId, (k, v) -> v == null ? 1L : v + 1L);
return loginRollup;
}
}
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.
The application for this tutorial includes a record generator to populate three topics with data.
java -jar build/libs/cogrouping-streams-standalone-0.0.1.jar configuration/dev.properties
Now that you have sent the login events, let’s run a consumer to read the cogrouped output from your streams application
confluent kafka topic consume output-topic -b --value-format avro
You should see something like this
{"login_by_app_and_user":{"one":{"Carol":3,"Alice":1,"Ted":2}}}
{"login_by_app_and_user":{"two":{"Carol":2,"Bob":1,"Ted":1}}}
{"login_by_app_and_user":{"three":{"Carol":1,"Bob":1,"Alice":2}}}
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=cogrouping-streams
bootstrap.servers=localhost:29092
schema.registry.url=mock://cogrouping-streams-test
state.dir=cogrouping-test-state
app-one.topic.name=app-one-topic
app-one.topic.partitions=1
app-one.topic.replication.factor=1
app-two.topic.name=app-two-topic
app-two.topic.partitions=1
app-two.topic.replication.factor=1
app-three.topic.name=app-three-topic
app-three.topic.partitions=1
app-three.topic.replication.factor=1
output.topic.name=output-topic
output.topic.partitions=1
output.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/LoginAggregatorTest.java
.
This tests the Aggregator
the Cogrouping
operation uses. As I said previously, you can easily include an instance of the Aggregator
in-line as a lambda in the original topology. But by having it as a stand alone class, you can easily test the Aggregator
in a unit test.
package io.confluent.developer;
import org.junit.Test;
import java.util.HashMap;
import io.confluent.developer.avro.LoginEvent;
import io.confluent.developer.avro.LoginRollup;
import static org.hamcrest.MatcherAssert.assertThat;
import static org.hamcrest.Matchers.is;
public class LoginAggregatorTest {
@Test
public void shouldAggregateValues() {
final LoginAggregator loginAggregator = new LoginAggregator();
final LoginRollup loginRollup = new LoginRollup();
loginRollup.setLoginByAppAndUser(new HashMap<>());
final String appOne = "app-one";
final String appTwo = "app-two";
final String appThree = "app-three";
final String user1 = "user1";
final String user2 = "user2";
loginAggregator.apply(appOne, login(appOne, user1), loginRollup);
loginAggregator.apply(appTwo, login(appTwo, user1), loginRollup);
loginAggregator.apply(appThree, login(appThree, user1), loginRollup);
assertThat(loginRollup.getLoginByAppAndUser().get(appOne).get(user1), is(1L));
assertThat(loginRollup.getLoginByAppAndUser().get(appTwo).get(user1), is(1L));
assertThat(loginRollup.getLoginByAppAndUser().get(appThree).get(user1), is(1L));
loginAggregator.apply(appOne, login(appOne, user1), loginRollup);
loginAggregator.apply(appTwo, login(appTwo, user1), loginRollup);
assertThat(loginRollup.getLoginByAppAndUser().get(appOne).get(user1), is(2L));
assertThat(loginRollup.getLoginByAppAndUser().get(appTwo).get(user1), is(2L));
assertThat(loginRollup.getLoginByAppAndUser().get(appThree).get(user1), is(1L));
loginAggregator.apply(appOne, login(appOne, user2), loginRollup);
loginAggregator.apply(appTwo, login(appTwo, user2), loginRollup);
loginAggregator.apply(appThree, login(appThree, user2), loginRollup);
loginAggregator.apply(appOne, login(appOne, user1), loginRollup);
loginAggregator.apply(appTwo, login(appTwo, user1), loginRollup);
loginAggregator.apply(appThree, login(appThree, user1), loginRollup);
assertThat(loginRollup.getLoginByAppAndUser().get(appOne).get(user1), is(3L));
assertThat(loginRollup.getLoginByAppAndUser().get(appTwo).get(user1), is(3L));
assertThat(loginRollup.getLoginByAppAndUser().get(appThree).get(user1), is(2L));
assertThat(loginRollup.getLoginByAppAndUser().get(appOne).get(user2), is(1L));
assertThat(loginRollup.getLoginByAppAndUser().get(appTwo).get(user2), is(1L));
assertThat(loginRollup.getLoginByAppAndUser().get(appThree).get(user2), is(1L));
}
private LoginEvent login(String appId, String userId) {
return new LoginEvent(appId, userId, System.currentTimeMillis());
}
}
Now create the following file at src/test/java/io/confluent/developer/CogroupingStreamsTest.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 CogroupingStreamsTest
annotated with @Test
, and that is cogroupingTest()
. 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 static org.junit.Assert.assertEquals;
import io.confluent.developer.avro.LoginEvent;
import io.confluent.developer.avro.LoginRollup;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.TreeMap;
import org.apache.kafka.common.serialization.Deserializer;
import org.apache.kafka.common.serialization.Serde;
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.junit.Test;
public class CogroupingStreamsTest {
private final static String TEST_CONFIG_FILE = "configuration/test.properties";
@Test
public void cogroupingTest() throws IOException {
final CogroupingStreams instance = new CogroupingStreams();
final Properties allProps = instance.loadEnvProperties(TEST_CONFIG_FILE);
final String appOneInputTopicName = allProps.getProperty("app-one.topic.name");
final String appTwoInputTopicName = allProps.getProperty("app-two.topic.name");
final String appThreeInputTopicName = allProps.getProperty("app-three.topic.name");
final String totalResultOutputTopicName = allProps.getProperty("output.topic.name");
final Topology topology = instance.buildTopology(allProps);
try (final TopologyTestDriver testDriver = new TopologyTestDriver(topology, allProps)) {
final Serde<String> stringAvroSerde = Serdes.String();
final SpecificAvroSerde<LoginEvent> loginEventSerde = CogroupingStreams.getSpecificAvroSerde(allProps);
final SpecificAvroSerde<LoginRollup> rollupSerde = CogroupingStreams.getSpecificAvroSerde(allProps);
final Serializer<String> keySerializer = stringAvroSerde.serializer();
final Deserializer<String> keyDeserializer = stringAvroSerde.deserializer();
final Serializer<LoginEvent> loginEventSerializer = loginEventSerde.serializer();
final TestInputTopic<String, LoginEvent> appOneInputTopic = testDriver.createInputTopic(appOneInputTopicName, keySerializer, loginEventSerializer);
final TestInputTopic<String, LoginEvent> appTwoInputTopic = testDriver.createInputTopic(appTwoInputTopicName, keySerializer, loginEventSerializer);
final TestInputTopic<String, LoginEvent> appThreeInputTopic = testDriver.createInputTopic(appThreeInputTopicName, keySerializer, loginEventSerializer);
final TestOutputTopic<String, LoginRollup> outputTopic = testDriver.createOutputTopic(totalResultOutputTopicName, keyDeserializer, rollupSerde.deserializer());
final List<LoginEvent> appOneEvents = new ArrayList<>();
appOneEvents.add(LoginEvent.newBuilder().setAppId("one").setUserId("foo").setTime(5L).build());
appOneEvents.add(LoginEvent.newBuilder().setAppId("one").setUserId("bar").setTime(6l).build());
appOneEvents.add(LoginEvent.newBuilder().setAppId("one").setUserId("bar").setTime(7L).build());
final List<LoginEvent> appTwoEvents = new ArrayList<>();
appTwoEvents.add(LoginEvent.newBuilder().setAppId("two").setUserId("foo").setTime(5L).build());
appTwoEvents.add(LoginEvent.newBuilder().setAppId("two").setUserId("foo").setTime(6l).build());
appTwoEvents.add(LoginEvent.newBuilder().setAppId("two").setUserId("bar").setTime(7L).build());
final List<LoginEvent> appThreeEvents = new ArrayList<>();
appThreeEvents.add(LoginEvent.newBuilder().setAppId("three").setUserId("foo").setTime(5L).build());
appThreeEvents.add(LoginEvent.newBuilder().setAppId("three").setUserId("foo").setTime(6l).build());
appThreeEvents.add(LoginEvent.newBuilder().setAppId("three").setUserId("bar").setTime(7L).build());
appThreeEvents.add(LoginEvent.newBuilder().setAppId("three").setUserId("bar").setTime(9L).build());
final Map<String, Map<String, Long>> expectedEventRollups = new TreeMap<>();
final Map<String, Long> expectedAppOneRollup = new HashMap<>();
final LoginRollup expectedLoginRollup = new LoginRollup(expectedEventRollups);
expectedAppOneRollup.put("foo", 1L);
expectedAppOneRollup.put("bar", 2L);
expectedEventRollups.put("one", expectedAppOneRollup);
final Map<String, Long> expectedAppTwoRollup = new HashMap<>();
expectedAppTwoRollup.put("foo", 2L);
expectedAppTwoRollup.put("bar", 1L);
expectedEventRollups.put("two", expectedAppTwoRollup);
final Map<String, Long> expectedAppThreeRollup = new HashMap<>();
expectedAppThreeRollup.put("foo", 2L);
expectedAppThreeRollup.put("bar", 2L);
expectedEventRollups.put("three", expectedAppThreeRollup);
sendEvents(appOneEvents, appOneInputTopic);
sendEvents(appTwoEvents, appTwoInputTopic);
sendEvents(appThreeEvents, appThreeInputTopic);
final List<LoginRollup> actualLoginEventResults = outputTopic.readValuesToList();
final Map<String, Map<String, Long>> actualRollupMap = new HashMap<>();
for (LoginRollup actualLoginEventResult : actualLoginEventResults) {
actualRollupMap.putAll(actualLoginEventResult.getLoginByAppAndUser());
}
final LoginRollup actualLoginRollup = new LoginRollup(actualRollupMap);
assertEquals(expectedLoginRollup, actualLoginRollup);
}
}
private void sendEvents(List<LoginEvent> events, TestInputTopic<String, LoginEvent> testInputTopic) {
for (LoginEvent event : events) {
testInputTopic.pipeInput(event.getAppId(), event);
}
}
}
Now run the test, which is as simple as:
./gradlew test
First, create a new configuration file at configuration/prod.properties
with the following content. Be sure to fill in the addresses of your production hosts and change any other parameters that make sense for your setup.
application.id=cogrouping-streams
bootstrap.servers=<<FILL ME IN>>
schema.registry.url=<<FILL ME IN>>
app-one.topic.name=app-one-topic
app-one.topic.partitions=1
app-one.topic.replication.factor=1
app-two.topic.name=app-two-topic
app-two.topic.partitions=1
app-two.topic.replication.factor=1
app-three.topic.name=app-three-topic
app-three.topic.partitions=1
app-three.topic.replication.factor=1
output.topic.name=output-topic
output.topic.partitions=1
output.topic.replication.factor=1
In your terminal, execute the following to invoke the Jib plugin to build an image:
gradle jibDockerBuild --image=io.confluent.developer/cogrouping-streams-join:0.0.1
Finally, launch the container using your preferred container orchestration service. If you want to run it locally, you can execute the following:
docker run -v $PWD/configuration/prod.properties:/config.properties io.confluent.developer/cogrouping-streams-join:0.0.1 config.properties