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Working with Stream Processing in Kafka

Kafka streams provide the ability to perform powerful data processing operations against Kafka data in real-time. In this lab, we will have the opportunity to work with Kafka streams by building a Java application capable of transforming data about individual purchases into a running total of each item that was bought. In addition to general stream processing, this lab will provide hands-on experience with topics such as stateless and stateful transformations, data aggregation, and type conversion.

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Path Info

Clock icon Intermediate
Clock icon 1h 0m
Clock icon Oct 18, 2019

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Table of Contents

  1. Challenge

    Clone the Starter Project from GitHub and Perform a Test Run

    1. Clone the starter project from GitHub:
    cd ~/
    git clone
    1. Perform a test to ensure that the code can compile and run:
    cd content-ccdak-kafka-streams-lab/
    ./gradlew run

    The output should contain the message printed by the Main class: Hello, world!

  2. Challenge

    Implement and Run the Kafka Streams Application

    1. Access the build.gradle file that must be edited:
    vi build.gradle
    1. Add the following dependencies to the dependencies {...} section of the file:
    implementation 'org.apache.kafka:kafka-streams:2.2.1'
    implementation 'org.apache.kafka:kafka-clients:2.2.1'
    1. Implement our streams logic in
    vi src/main/java/com/linuxacademy/ccdak/streams/

    Note: Remember to check the code comments for some helpful info, or the solution video for a more thorough explanation.

    Here is an example of

      package com.linuxacademy.ccdak.streams;
      import java.util.Properties;
      import java.util.concurrent.CountDownLatch;
      import org.apache.kafka.common.serialization.Serdes;
      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.Grouped;
      import org.apache.kafka.streams.kstream.KStream;
      import org.apache.kafka.streams.kstream.KTable;
      import org.apache.kafka.streams.kstream.Produced;
      public class Main {
          public static void main(String[] args) {
              // Set up the configuration.
              final Properties props = new Properties();
              props.put(StreamsConfig.APPLICATION_ID_CONFIG, "inventory-data");
              props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
              props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);
              // Since the input topic uses Strings for both key and value, set the default Serdes to String.
              props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
              props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
              // Get the source stream.
              final StreamsBuilder builder = new StreamsBuilder();
              final KStream<String, String> source ="inventory_purchases");
              // Convert the value from String to Integer. If the value is not a properly-formatted number, print a message and set it to 0.
              final KStream<String, Integer> integerValuesSource = source.mapValues(value -> {
                  try {
                      return Integer.valueOf(value);
                  } catch (NumberFormatException e) {
                      System.out.println("Unable to convert to Integer: \"" + value + "\"");
                      return 0;
              // Group by the key and reduce to provide a total quantity for each key.
              final KTable<String, Integer> productCounts = integerValuesSource
                  .groupByKey(Grouped.with(Serdes.String(), Serdes.Integer()))
                  .reduce((total, newQuantity) -> total + newQuantity);
              // Output to the output topic.
                  .to("total_purchases", Produced.with(Serdes.String(), Serdes.Integer()));
              final Topology topology =;
              final KafkaStreams streams = new KafkaStreams(topology, props);
              // Print the topology to the console.
              final CountDownLatch latch = new CountDownLatch(1);
              // Attach a shutdown handler to catch control-c and terminate the application gracefully.
              Runtime.getRuntime().addShutdownHook(new Thread("streams-wordcount-shutdown-hook") {
                  public void run() {
              try {
              } catch (final Throwable e) {
    1. Run the code:
    ./gradlew run
    1. We can check the output of the application by consuming from the output topic:
    kafka-console-consumer --bootstrap-server localhost:9092 --topic total_purchases --from-beginning --property print.key=true --property value.deserializer=org.apache.kafka.common.serialization.IntegerDeserializer
    1. If we want to visually compare the output with the input, in a new terminal window, we can consume from the input topic with:
    kafka-console-consumer --bootstrap-server localhost:9092 --topic inventory_purchases --property print.key=true

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