Enter your Schema Registry API key:
Enter your Schema Registry API secret:
How can you count the number of events in a Kafka topic based on some criteria?
Use the count()
method to apply the count aggregation.
To get started, make a new directory anywhere you’d like for this project:
mkdir aggregate-count && cd aggregate-count
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 "idea"
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"
}
}
jar {
manifest {
attributes(
"Class-Path": configurations.compileClasspath.collect { it.getName() }.join(" "),
"Main-Class": "io.confluent.developer.AggregatingCount"
)
}
}
shadowJar {
archiveBaseName = "kstreams-aggregating-count-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
:
input.topic.name=movie-ticket-sales
input.topic.partitions=6
input.topic.replication.factor=3
output.topic.name=movie-tickets-sold
output.topic.partitions=6
output.topic.replication.factor=3
application.id=aggregating-count-app
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
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/ticket-sale.avsc
for the ticket sale events:
{
"namespace": "io.confluent.developer.avro",
"type": "record",
"name": "TicketSale",
"fields": [
{"name": "title", "type": "string"},
{"name": "sale_ts", "type": "string"},
{"name": "ticket_total_value", "type": "int"}
]
}
Because this Avro schema is used in the Java code, it needs to compile it. Run the following:
./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/AggregatingCount.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, we can apply the following methods:
Call the stream()
method to create a KStream<String, TicketSale>
object.
Since we can’t make any assumptions about the key of this stream, we have to repartition it explicitly. We use the map()
method for that, creating a new KeyValue
instance for each record, using the movie title as the new key.
Group the events by that new key by calling the groupByKey()
method. This returns a KGroupedStream
object.
Use the count()
method to apply the count aggregation.
Use the toStream()
method to produce the count results to the specified output topic.
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.Consumed;
import org.apache.kafka.streams.kstream.Grouped;
import org.apache.kafka.streams.kstream.Produced;
import java.io.FileInputStream;
import java.io.InputStream;
import java.io.IOException;
import java.time.Duration;
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.TicketSale;
import io.confluent.common.utils.TestUtils;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;
import static io.confluent.kafka.serializers.AbstractKafkaSchemaSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG;
public class AggregatingCount {
private SpecificAvroSerde<TicketSale> ticketSaleSerde(final Properties allProps) {
final SpecificAvroSerde<TicketSale> serde = new SpecificAvroSerde<>();
Map<String, String> config = (Map)allProps;
serde.configure(config, false);
return serde;
}
public Topology buildTopology(Properties allProps,
final SpecificAvroSerde<TicketSale> ticketSaleSerde) {
final StreamsBuilder builder = new StreamsBuilder();
final String inputTopic = allProps.getProperty("input.topic.name");
final String outputTopic = allProps.getProperty("output.topic.name");
builder.stream(inputTopic, Consumed.with(Serdes.String(), ticketSaleSerde))
// Set key to title and value to ticket value
.map((k, v) -> new KeyValue<>(v.getTitle(), v.getTicketTotalValue()))
// Group by title
.groupByKey(Grouped.with(Serdes.String(), Serdes.Integer()))
// Apply COUNT method
.count()
// Write to stream specified by outputTopic
.toStream().mapValues(v -> v.toString() + " tickets sold").to(outputTopic, Produced.with(Serdes.String(), Serdes.String()));
return builder.build();
}
public void createTopics(Properties allProps) {
AdminClient client = AdminClient.create(allProps);
List<NewTopic> topics = new ArrayList<>();
topics.add(new NewTopic(
allProps.getProperty("input.topic.name"),
Integer.parseInt(allProps.getProperty("input.topic.partitions")),
Short.parseShort(allProps.getProperty("input.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);
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 IOException {
if (args.length < 1) {
throw new IllegalArgumentException(
"This program takes one argument: the path to an environment configuration file.");
}
new AggregatingCount().runRecipe(args[0]);
}
private void runRecipe(final String configPath) throws IOException {
final Properties allProps = new Properties();
try (InputStream inputStream = new FileInputStream(configPath)) {
allProps.load(inputStream);
}
allProps.put(StreamsConfig.APPLICATION_ID_CONFIG, allProps.getProperty("application.id"));
allProps.put(StreamsConfig.STATE_DIR_CONFIG, TestUtils.tempDirectory().getPath());
allProps.put(StreamsConfig.STATESTORE_CACHE_MAX_BYTES_CONFIG, 0);
Topology topology = this.buildTopology(allProps, this.ticketSaleSerde(allProps));
this.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.cleanUp();
streams.start();
latch.await();
} catch (Throwable e) {
System.exit(1);
}
System.exit(0);
}
}
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-aggregating-count-standalone-0.0.1.jar configuration/dev.properties
In a new terminal, run:
confluent kafka topic produce movie-ticket-sales \
--parse-key \
--delimiter ":" \
--value-format avro \
--schema src/main/avro/ticket-sale.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:
"Die Hard":{"title":"Die Hard","sale_ts":"2019-07-18T10:00:00Z","ticket_total_value":12}
"Die Hard":{"title":"Die Hard","sale_ts":"2019-07-18T10:01:00Z","ticket_total_value":12}
"The Godfather":{"title":"The Godfather","sale_ts":"2019-07-18T10:01:31Z","ticket_total_value":12}
"Die Hard":{"title":"Die Hard","sale_ts":"2019-07-18T10:01:36Z","ticket_total_value":24}
"The Godfather":{"title":"The Godfather","sale_ts":"2019-07-18T10:02:00Z","ticket_total_value":18}
"The Big Lebowski":{"title":"The Big Lebowski","sale_ts":"2019-07-18T11:03:21Z","ticket_total_value":12}
"The Big Lebowski":{"title":"The Big Lebowski","sale_ts":"2019-07-18T11:03:50Z","ticket_total_value":12}
"The Godfather":{"title":"The Godfather","sale_ts":"2019-07-18T11:40:00Z","ticket_total_value":36}
"The Godfather":{"title":"The Godfather","sale_ts":"2019-07-18T11:40:09Z","ticket_total_value":18}
Enter Ctrl+C
to exit.
Run the following command to start a Confluent CLI consumer to view consume the events that have been filtered by your application:
confluent kafka topic consume movie-tickets-sold -b --print-key
After the consumer starts, you should see the following messages. Note that for every key (movie), a sequence of output records (count) is emitted. Each record represents an update to the count, which is sent on every movie event specifically because caching is disabled in the code with StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG
set to 0
. Read more on Record caches in the DSL
.
The consumer prompt will hang, waiting for more events to arrive. To continue studying the example, send more events through the input terminal prompt. Otherwise, you can Control-C
to exit the process.
Die Hard 1
Die Hard 2
The Godfather 1
Die Hard 3
The Godfather 2
The Big Lebowski 1
The Big Lebowski 2
The Godfather 3
The Godfather 4
Enter Ctrl+C
to exit.
First, create a test file at configuration/test.properties
:
application.id=aggregating-count-app
bootstrap.servers=127.0.0.1:29092
schema.registry.url=mock://SR_DUMMY_URL:8081
input.topic.name=movie-ticket-sales
input.topic.partitions=1
input.topic.replication.factor=1
output.topic.name=movie-tickets-sold
output.topic.partitions=1
output.topic.replication.factor=1
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/AggregatingCountTest.java
:
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.KeyValue;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.TopologyTestDriver;
import org.junit.After;
import org.junit.Assert;
import org.junit.Test;
import java.io.IOException;
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.stream.Collectors;
import io.confluent.developer.avro.TicketSale;
import io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde;
import static java.util.Arrays.asList;
public class AggregatingCountTest {
private final static String TEST_CONFIG_FILE = "configuration/test.properties";
private TopologyTestDriver testDriver;
private SpecificAvroSerde<TicketSale> makeSerializer(Properties allProps) {
SpecificAvroSerde<TicketSale> serde = new SpecificAvroSerde<>();
Map<String, String> config = new HashMap<>();
config.put("schema.registry.url", allProps.getProperty("schema.registry.url"));
serde.configure(config, false);
return serde;
}
@Test
public void shouldCountTicketSales() throws IOException {
AggregatingCount aggCount = new AggregatingCount();
Properties allProps = aggCount.loadEnvProperties(TEST_CONFIG_FILE);
String inputTopic = allProps.getProperty("input.topic.name");
String outputTopic = allProps.getProperty("output.topic.name");
final SpecificAvroSerde<TicketSale> ticketSaleSpecificAvroSerde = makeSerializer(allProps);
Topology topology = aggCount.buildTopology(allProps, ticketSaleSpecificAvroSerde);
testDriver = new TopologyTestDriver(topology, allProps);
Serializer<String> keySerializer = Serdes.String().serializer();
Deserializer<String> keyDeserializer = Serdes.String().deserializer();
final List<TicketSale>
input = asList(
new TicketSale("Die Hard", "2019-07-18T10:00:00Z", 12),
new TicketSale("Die Hard", "2019-07-18T10:01:00Z", 12),
new TicketSale("The Godfather", "2019-07-18T10:01:31Z", 12),
new TicketSale("Die Hard", "2019-07-18T10:01:36Z", 24),
new TicketSale("The Godfather", "2019-07-18T10:02:00Z", 18),
new TicketSale("The Big Lebowski", "2019-07-18T11:03:21Z", 12),
new TicketSale("The Big Lebowski", "2019-07-18T11:03:50Z", 12),
new TicketSale("The Godfather", "2019-07-18T11:40:00Z", 36),
new TicketSale("The Godfather", "2019-07-18T11:40:09Z", 18)
);
testDriver
.createInputTopic(inputTopic, keySerializer, ticketSaleSpecificAvroSerde.serializer())
.pipeValueList(input);
final String outputLabel = " tickets sold";
List<String> originalCounts = new ArrayList<String>(Arrays.asList("1", "2", "1", "3", "2", "1", "2", "3", "4"));
List<String> expectedOutput = originalCounts.stream().map(v -> v + outputLabel).collect(Collectors.toList());
final List<KeyValue<String, String>> keyValues =
testDriver
.createOutputTopic(outputTopic, keyDeserializer, Serdes.String().deserializer())
.readKeyValuesToList();
List<String> actualOutput;
actualOutput = keyValues
.stream()
.filter(record -> record.value != null)
.map(record -> record.value)
.collect(Collectors.toList());
// System.out.println(actualOutput);
Assert.assertEquals(expectedOutput, actualOutput);
}
@After
public void cleanup() {
testDriver.close();
}
}
Now run the test, which is as simple as:
./gradlew test
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 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=aggregating-count-app
bootstrap.servers=<< FILL ME IN >>
schema.registry.url=<< FILL ME IN >>
input.topic.name=movie-ticket-sales
input.topic.partitions=<< FILL ME IN >>
input.topic.replication.factor=<< FILL ME IN >>
output.topic.name=movie-tickets-sold
output.topic.partitions=<< FILL ME IN >>
output.topic.replication.factor=<< FILL ME IN >>
In your terminal, execute the following to invoke the Jib plugin to build an image:
gradle jibDockerBuild --image=io.confluent.developer/kstreams-aggregating-count: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/kstreams-aggregating-count:0.0.1 config.properties