How to count a stream of events


How can you count the number of events in a Kafka topic based on some criteria?

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Example use case:

Suppose you have a topic with events that represent ticket sales for movies. In this tutorial, you'll see an example of 'groupby count' in Kafka Streams, ksqlDB, and Flink SQL. We'll write a program that calculates the total number of tickets sold per movie.

Hands-on code example:

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Short Answer

Use the count() method to apply the count aggregation.

Run it

Initialize the project


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

Provision your Kafka cluster


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.

Take me to Confluent Cloud
  1. After you log in to Confluent Cloud, 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.

  2. 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.

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

Confluent Cloud

Write the cluster information into a local file


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/ file on your machine.

# Required connection configs for Kafka producer, consumer, and admin
bootstrap.servers={{ BOOTSTRAP_SERVERS }}
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={{ SR_URL }}
basic.auth.credentials.source=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.

Download and set up the Confluent CLI


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.

Configure the project


Create the following Gradle build file, named build.gradle for the project:

buildscript {
    repositories {
    dependencies {
        classpath ""

plugins {
    id "idea"
    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"

jar {
  manifest {
      "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/

Update the properties file with Confluent Cloud information


Using the command below, append the contents of configuration/ (with your Confluent Cloud configuration) to configuration/ (with the application properties).

cat configuration/ >> configuration/

Create a schema for the events


Create a directory for the schemas that represent the events in the stream:

mkdir -p src/main/avro

Then create the following Avro schema file at src/main/avro/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 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, we can apply the following methods:

  1. Call the stream() method to create a KStream<String, TicketSale> object.

  2. 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.

  3. Group the events by that new key by calling the groupByKey() method. This returns a KGroupedStream object.

  4. Use the count() method to apply the count aggregation.

  5. 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.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("");
    final String outputTopic = allProps.getProperty("");, 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
        // Write to stream specified by outputTopic
        .toStream().mapValues(v -> v.toString() + " tickets sold").to(outputTopic, Produced.with(Serdes.String(), Serdes.String()));


  public void createTopics(Properties allProps) {
    AdminClient client = AdminClient.create(allProps);

    List<NewTopic> topics = new ArrayList<>();
    topics.add(new NewTopic(
    topics.add(new NewTopic(


  public Properties loadEnvProperties(String fileName) throws IOException {
    Properties allProps = new Properties();
    FileInputStream input = new FileInputStream(fileName);

    return allProps;

  public static void main(String[] args) throws 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.put(StreamsConfig.APPLICATION_ID_CONFIG, allProps.getProperty(""));
    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));

    final KafkaStreams streams = new KafkaStreams(topology, allProps);
    final CountDownLatch latch = new CountDownLatch(1);

    // Attach shutdown handler to catch Control-C.
    Runtime.getRuntime().addShutdownHook(new Thread("streams-shutdown-hook") {
      public void run() {

    try {
    } catch (Throwable e) {


Compile and run the Kafka Streams program


In your terminal, run:

./gradlew shadowJar

Now that an uberjar for the Kafka Streams application has been built, you can launch it locally. When you run the following, the prompt won’t return, because the application will run until you exit it:

java -jar build/libs/kstreams-aggregating-count-standalone-0.0.1.jar configuration/

Produce events to the input topic


In a new terminal, run:

confluent kafka topic produce movie-ticket-sales \
  --parse-key \
  --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/ configuration file. Look for the configuration parameter, 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}

Use Ctrl-C to exit.

Consume aggregated count from the output topic


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

Use Ctrl-C to exit.

Test it

Create a test configuration file


First, create a test file at configuration/

Write a test


Then, create a directory for the tests to live in:

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

Create the following test file at src/test/java/io/confluent/developer/

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.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;

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/";
  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;

  public void shouldCountTicketSales() throws IOException {
    AggregatingCount aggCount = new AggregatingCount();
    Properties allProps = aggCount.loadEnvProperties(TEST_CONFIG_FILE);

    String inputTopic = allProps.getProperty("");
    String outputTopic = allProps.getProperty("");

    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)

        .createInputTopic(inputTopic, keySerializer, ticketSaleSpecificAvroSerde.serializer())

    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 = -> v + outputLabel).collect(Collectors.toList());

    final List<KeyValue<String, String>> keyValues =
            .createOutputTopic(outputTopic, keyDeserializer, Serdes.String().deserializer())

    List<String> actualOutput;
    actualOutput = keyValues
        .filter(record -> record.value != null)
        .map(record -> record.value)

//    System.out.println(actualOutput);
    Assert.assertEquals(expectedOutput, actualOutput);


  public void cleanup() {

Invoke the tests


Now run the test, which is as simple as:

./gradlew test

Teardown Confluent Cloud resources


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.