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Apache Kafka & Stream Processing Fundamentals · Lesson

Building a Simple Kafka Streams App

Create your first Kafka Streams application to process data in real-time from Kafka topics.

Building a Simple Kafka Streams App is a free Apache Kafka & Stream Processing Fundamentals lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Apache Kafka & Stream Processing Fundamentals learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Welcome to Kafka Streams!

Ready to build real-time data processing apps? Kafka Streams is a client library for building applications and microservices that process data stored in Kafka.

It lets you write standard Java/Scala applications that leverage Kafka's power for stream processing. Think of it as a toolkit to transform, filter, and analyze data as it flows through Kafka.

The Power of Kafka Streams

Kafka Streams offers several key advantages for your real-time applications:

  • Lightweight: It's just a library, no separate cluster needed.
  • Fault-Tolerant: Automatically handles failures and data recovery.
  • Scalable: Easily scales by adding more instances of your application.
  • Exactly-Once Processing: Guarantees data is processed once, even with failures.

It's great for real-time analytics, data transformations, and event-driven microservices.

Setting Up Your Project

To start, you'll need to add the Kafka Streams library to your project. If you're using Maven, add this dependency to your pom.xml:

<dependency>
  <groupId>org.apache.kafka</groupId>
  <artifactId>kafka-streams</artifactId>
  <version>3.5.1</version>
</dependency>

Core Component: StreamsBuilder

The StreamsBuilder is your main entry point for defining the stream processing topology. Think of it as the architect for your data flow.

You use it to create source streams, apply transformations, and define where the processed data should go. Here's how you'd create one:

import org.apache.kafka.streams.StreamsBuilder;

public class Main {
  public static void main(String[] args) {
    StreamsBuilder builder = new StreamsBuilder();
    // Your stream processing logic will go here
    System.out.println("StreamsBuilder created!");
  }
}

KStream: Records in Motion

A KStream represents an unbounded, continuously updating stream of key-value records. Each record is processed independently as it arrives.

You can create a KStream from a Kafka topic using the stream() method of your StreamsBuilder. This tells your app to start consuming messages from that topic.

import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.KStream;

public class Main {
  public static void main(String[] args) {
    StreamsBuilder builder = new StreamsBuilder();
    KStream<String, String> sourceStream =
        builder.stream("input-topic");
    System.out.println("KStream created from input-topic!");
  }
}

Simple Transformation: mapValues

One common operation is to transform the value of each record in a KStream. The mapValues() method is perfect for this.

It applies a function to each record's value, keeping the key unchanged. Let's write code to make all text values uppercase!

import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.KStream;

public class Main {
  public static void main(String[] args) {
    StreamsBuilder builder = new StreamsBuilder();
    KStream<String, String> sourceStream =
        builder.stream("input-topic");

    KStream<String, String> transformedStream =
        sourceStream.mapValues(value -> value.toUpperCase());

    System.out.println("Stream values will be uppercased!");
  }
}

Essential Stream Configuration

Before running your app, you need to configure it. This is done using a Properties object and StreamsConfig. Key settings include:

  • APPLICATION_ID_CONFIG: Unique ID for your app (like a consumer group).
  • BOOTSTRAP_SERVERS_CONFIG: Your Kafka broker addresses.
  • DEFAULT_KEY_SERDE_CLASS_CONFIG: How to serialize/deserialize keys.
  • DEFAULT_VALUE_SERDE_CLASS_CONFIG: How to serialize/deserialize values.

Serdes (Serializer/Deserializer) are crucial for converting data to/from bytes.

Your First Kafka Streams App

Let's put everything together! This app will read messages from an 'input-topic', convert their values to uppercase, and then write the results to an 'output-topic'.

Remember to create these topics in your Kafka cluster before running this code!

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.kstream.KStream;

import java.util.Properties;

public class UppercaseStreamApp {

  public static void main(String[] args) {
    Properties props = new Properties();
    props.put(StreamsConfig.APPLICATION_ID_CONFIG, "uppercase-app");
    props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
    props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

    StreamsBuilder builder = new StreamsBuilder();
    KStream<String, String> sourceStream = builder.stream("input-topic");

    KStream<String, String> transformedStream =
        sourceStream.mapValues(value -> value.toUpperCase());

    transformedStream.to("output-topic");

    KafkaStreams streams = new KafkaStreams(builder.build(), props);

    // Clean up local state on shutdown (for development)
    streams.cleanUp(); 
    
    streams.start();

    // Add shutdown hook to close Kafka Streams cleanly
    Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
    System.out.println("UppercaseStreamApp started!");
  }
}

Managing Your Stream App

After defining your topology and configuration, you create a KafkaStreams instance and call start() to begin processing.

It's crucial to add a shutdown hook (Runtime.getRuntime().addShutdownHook) to ensure your application closes gracefully, flushing any buffered data and releasing resources.

The streams.cleanUp() call is useful during development to clear any local state store data, but should generally be avoided in production.

Test Your Knowledge

Which of the following is NOT a core component or essential configuration for a basic Kafka Streams application?

Recap: Your First Stream App

Great job! You've successfully learned the fundamentals of building a simple Kafka Streams application.

You now understand how to:

  • Add the necessary Kafka Streams dependency.
  • Use StreamsBuilder to define your processing topology.
  • Create a KStream from an input topic.
  • Apply simple transformations like mapValues().
  • Configure your application with StreamsConfig.
  • Start and gracefully stop your Kafka Streams application.

Next, we'll dive deeper into KStream and KTable concepts!

Frequently asked questions

Is the “Building a Simple Kafka Streams App” lesson free?

Yes — the full text of “Building a Simple Kafka Streams App” is free to read here on the web, and the Apache Kafka & Stream Processing Fundamentals course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Apache Kafka & Stream Processing Fundamentals course, upgrade to CoddyKit PRO.

What will I learn in “Building a Simple Kafka Streams App”?

Create your first Kafka Streams application to process data in real-time from Kafka topics. You practise Apache Kafka & Stream Processing Fundamentals with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Apache Kafka & Stream Processing Fundamentals?

No prior experience is required. Apache Kafka & Stream Processing Fundamentals on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Building a Simple Kafka Streams App” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Apache Kafka & Stream Processing Fundamentals lesson?

Yes. Every Apache Kafka & Stream Processing Fundamentals lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Building a Simple Kafka Streams App
  2. KStream & KTable Concepts
  3. Stateless vs. Stateful Operations
  4. Serdes & Data Serialization in Kafka Streams
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