Advanced Spring Boot 4: Event-Driven Architecture (Kafka) · Lesson

Stream Processing with KStream & KTable

Learn to use KStream for immutable event streams and KTable for stateful, updatable views of data, performing operations like filtering and mapping.

Stream Processing with KStream & KTable is a free Advanced Spring Boot 4: Event-Driven Architecture (Kafka) lesson on CoddyKit — lesson 2 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 Advanced Spring Boot 4: Event-Driven Architecture (Kafka) learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

KStream & KTable Unveiled

Welcome! In Kafka Streams, KStream and KTable are your primary tools for processing data. They represent different views of your data in motion.

Think of them as two sides of the same coin, each suited for distinct stream processing tasks. Understanding their differences is key to building powerful stream applications.

KStream: Immutable Events

A KStream represents an infinite, immutable sequence of events. Each record in a KStream is a self-contained fact, an independent event that happened at a specific point in time.

It's like a transaction log: once an event is added, it's never changed.
Operations on a KStream produce new KStreams, leaving the original untouched.
It's ideal for processing individual events like clicks, sensor readings, or log entries.

Filtering KStream Events

One common KStream operation is filtering. You can selectively keep records that match certain criteria, creating a new KStream with only the relevant events.

Here's a simple example filtering messages that contain 'hello'.

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

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

        KStream<String, String> filteredStream = sourceStream.filter(
            (key, value) -> value.contains("hello")
        );

        filteredStream.to("output-topic");

        KafkaStreams streams = new KafkaStreams(builder.build(), props);
        // In a real app, you'd start and manage this lifecycle:
        // streams.start();
        // Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
        System.out.println("KStream filter setup complete. Send 'hello world' to input-topic!");
    }
}

Transforming KStream Values

The mapValues operation transforms the value of each record in a KStream, producing a new KStream with the modified values. The key remains unchanged.

This is useful for cleaning data, changing formats, or enriching information without altering the message's key.

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

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

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

        uppercasedStream.to("output-topic");

        KafkaStreams streams = new KafkaStreams(builder.build(), props);
        System.out.println("KStream mapValues setup complete. Send 'test' to input-topic!");
    }
}

KTable: A Materialized View

A KTable represents a changelog stream, where each record is an update to a specific key. It's essentially a materialized view of a table, reflecting the latest state for each key.

It's like a database table: keys have associated values, and new records for a key overwrite previous ones.
KTable is stateful, maintaining the latest value for each key over time.
It's perfect for aggregating data, maintaining counts, or storing user profiles.

KTable's Stateful Nature

The core idea behind a KTable is that it keeps track of the latest value for each unique key. When a new record with an existing key arrives, the KTable updates its internal state.

This makes KTable ideal for scenarios where you care about the current state of an entity, rather than every single event that led to that state.

KStream to KTable: Counting

You can transform a KStream into a KTable, typically to perform aggregations. A common example is counting occurrences of keys using groupByKey().count().

Each time a message arrives, the count for its key is updated, and the KTable emits the new total.

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 org.apache.kafka.streams.kstream.KTable;
import org.apache.kafka.streams.kstream.Materialized;

import java.util.Properties;

public class Main {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "kstream-to-ktable-app");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_BY_DEFAULT, Serdes.String().getClass());
        props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_BY_DEFAULT, Serdes.String().getClass());

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

        KTable<String, Long> wordCounts = sourceStream
            .groupByKey() // Group by the existing key
            .count(Materialized.as("counts-store")); // Count occurrences, store in state

        wordCounts.toStream().to("output-topic"); // Convert back to stream to send out

        KafkaStreams streams = new KafkaStreams(builder.build(), props);
        System.out.println("KStream to KTable count setup. Send 'word' with key 'A' to input-topic!");
    }
}

KTable for Aggregation

KTables are excellent for continuous aggregation. Beyond simple counts, you can use operations like aggregate to maintain sums, averages, or custom aggregates over time.

This allows your application to always have an up-to-date summary of data for specific keys.

When to Use Which?

The choice between KStream and KTable depends on your processing needs:

Use KStream when you need to process individual events, react to every occurrence, or build a pipeline of transformations that don't depend on historical state. Think real-time alerts or event logging.
Use KTable when you need to maintain a current state, aggregate data over time, or join with other data sources based on the latest value. Think user profiles, stock prices, or aggregated metrics.

KStream vs. KTable Check

You've learned about KStream and KTable. Let's test your understanding.

KStream & KTable Recap

Great job! You've explored the core differences and uses of KStream and KTable.

KStream handles individual, immutable events, perfect for event-by-event processing.
KTable maintains a materialized view, tracking the latest state for each key, ideal for aggregations and stateful processing.

These two primitives are the foundation for building powerful and flexible stream processing applications with Kafka Streams.

Frequently asked questions

Is the “Stream Processing with KStream & KTable” lesson free?

Yes — the full text of “Stream Processing with KStream & KTable” is free to read here on the web, and the Advanced Spring Boot 4: Event-Driven Architecture (Kafka) 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 Advanced Spring Boot 4: Event-Driven Architecture (Kafka) course, upgrade to CoddyKit PRO.

What will I learn in “Stream Processing with KStream & KTable”?

Learn to use KStream for immutable event streams and KTable for stateful, updatable views of data, performing operations like filtering and mapping. You practise Advanced Spring Boot 4: Event-Driven Architecture (Kafka) 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 Advanced Spring Boot 4: Event-Driven Architecture (Kafka)?

No prior experience is required. Advanced Spring Boot 4: Event-Driven Architecture (Kafka) on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Stream Processing with KStream & KTable” 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 Advanced Spring Boot 4: Event-Driven Architecture (Kafka) lesson?

Yes. Every Advanced Spring Boot 4: Event-Driven Architecture (Kafka) 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

Introduction to Kafka Streams
Stream Processing with KStream & KTable
Building a Simple Stream Application
Windowing and Stateful Aggregations in Kafka Streams

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