Welcome to our guide on Reactive Kafka! In this tutorial, we'll learn about Apache Kafka's reactive streams API, a powerful tool for building resilient, high-throughput, and low-latency applications. Let's dive in!
<a name="what-is-apache-kafka"></a>
Apache Kafka is a distributed streaming platform designed to handle real-time data processing at scale. It provides a messaging system that can process streams of records, making it an ideal choice for building data pipelines, real-time analytics, and event-driven applications.
<a name="why-reactive-kafka"></a>
Reactive Kafka introduces a reactive streams API to Kafka, allowing developers to write applications using a non-blocking, event-driven approach. This leads to more responsive, resilient, and efficient applications, especially when dealing with high-throughput and low-latency scenarios.
<a name="understanding-reactive-streams"></a>
Reactive Streams is a specification that provides a common API for asynchronous, non-blocking streams of data between libraries, frameworks, and programming languages. It simplifies the development of event-driven applications, enabling efficient resource utilization and improved responsiveness.
<a name="reactive-kafka-key-concepts"></a>
A Publisher is a producer of data, emitting records to a Kafka Topic. A Subscriber consumes records from a Topic. In Reactive Kafka, these components are represented using reactive streams API.
Signals are used to communicate between the Publisher and Subscriber. They represent the demand and supply of data. Backpressure is the mechanism used to handle situations when the Subscriber can't consume data as fast as the Publisher produces it.
Processors are components that receive data from a Publisher, perform some processing, and send the result to a Subscriber. They can be chained together to build complex data processing pipelines.
<a name="setting-up-reactive-kafka"></a>
To set up Reactive Kafka, you'll need:
Follow the official guide to set up a Reactive Kafka project using Spring Boot.
<a name="practical-example-building-a-reactive-kafka-microservice"></a>
Let's build a simple microservice that sends messages to a Kafka Topic and consumes messages from another Topic.
Create two Kafka Topics using the Kafka CLI:
kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic messages-in
kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic messages-outCreate a simple Java class for the Producer and Consumer:
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.stereotype.Service;
@Service
public class KafkaService {
private final KafkaTemplate<String, String> template;
public KafkaService(KafkaTemplate<String, String> template) {
this.template = template;
}
// Sending a message to messages-out topic
public void sendMessage(String message) {
template.send("messages-out", message);
}
// Consuming messages from messages-in topic
@KafkaListener(topics = "messages-in")
public void consumeMessage(String message) {
System.out.println("Received: " + message);
}
}Build and run the microservice using Maven:
mvn clean package
java -jar target/my-reactive-kafka-app.jarNow you can send messages to the messages-out topic and see them being consumed from the messages-in topic.
<a name="quiz"></a>
Which component in Reactive Kafka represents a producer of data?