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

KStream ve KTable ile Akış İşleme

KStream ve KTable ile Akış İşleme, CoddyKit'te ücretsiz bir Advanced Spring Boot 4: Event-Driven Architecture (Kafka) dersidir. Bu, 4 dersinin 2. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Advanced Spring Boot 4: Event-Driven Architecture (Kafka) öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Advanced Spring Boot 4: Event-Driven Architecture (Kafka) kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

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.

Sıkça Sorulan Sorular

“KStream ve KTable ile Akış İşleme” dersi ücretsiz mi?

Evet — “KStream ve KTable ile Akış İşleme” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Advanced Spring Boot 4: Event-Driven Architecture (Kafka) kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Advanced Spring Boot 4: Event-Driven Architecture (Kafka) kursu toplamda 4 dersten oluşur.

“KStream ve KTable ile Akış İşleme” dersinde ne öğreneceğim?

Değiştirilemez olay akışları için KStream'i, durum bilgili ve güncellenebilir veri görünümleri için KTable'ı kullanmayı; filtreleme ve eşleme gibi işlemleri gerçekleştirmeyi öğrenin. Advanced Spring Boot 4: Event-Driven Architecture (Kafka) ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

Advanced Spring Boot 4: Event-Driven Architecture (Kafka) öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te Advanced Spring Boot 4: Event-Driven Architecture (Kafka), başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 2. dersidir.

“KStream ve KTable ile Akış İşleme” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu Advanced Spring Boot 4: Event-Driven Architecture (Kafka) dersinde kod yazıp çalıştırabilir miyim?

Evet. Her Advanced Spring Boot 4: Event-Driven Architecture (Kafka) dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

Bu kursun tüm dersleri

Kafka Streams’e Giriş
KStream ve KTable ile Akış İşleme
Basit Bir Akış Uygulaması Geliştirme
Kafka Streams'te Pencereleme ve Durum Bilgili Toplamalar

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