Spring Boot and Kafka Integration Tutorial

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Spring Boot and Kafka Integration Tutorial

1. Introduction

Apache Kafka is a distributed event streaming platform that enables you to build real-time streaming applications. In this tutorial, we’ll explore how to integrate Kafka with a Spring Boot application.

2. Prerequisites

Before you begin, ensure you have the following installed:

3. Setting up Kafka

  1. Extract the Kafka archive and navigate to the Kafka directory.
  2. Start ZooKeeper:
   bin/zookeeper-server-start.sh config/zookeeper.properties
  1. Start Kafka server:
   bin/kafka-server-start.sh config/server.properties

Now, Kafka is up and running.

4. Creating a Spring Boot Project

Use Spring Initializr to create a new Spring Boot project:

  • Project: Gradle or Maven
  • Language: Java
  • Spring Boot: Latest version
  • Group: com.example
  • Artifact: kafka-demo
  • Dependencies: Select “Spring Web” and “Spring for Apache Kafka.”

Generate the project and open it in your preferred IDE.

5. Adding Kafka Dependencies

If you chose Maven, add the following dependencies to your pom.xml:

<dependency>
    <groupId>org.springframework.kafka</groupId>
    <artifactId>spring-kafka</artifactId>
</dependency>

If you’re using Gradle, add the following to your build.gradle:

implementation 'org.springframework.kafka:spring-kafka'

6. Configuring Kafka in Spring Boot

Create a configuration class for Kafka in your project:

Replace "your-topic" with the name of the Kafka topic you want to listen to.

7. Producer Example

Create a simple Kafka producer:

8. Consumer Example

Create a Kafka consumer:

9. Running the Application

Now, you can use the KafkaProducer to send messages to Kafka, and the KafkaConsumer will listen for incoming messages.

Example usage in a Spring Boot controller:

Run your Spring Boot application and use a tool like Kafka Tool or the Kafka command line tools to observe the messages being sent and received.

10. Conclusion

This tutorial covered setting up Kafka, creating a Spring Boot project, configuring Kafka, and implementing a simple producer and consumer. You can now extend this foundation to build more complex real-time streaming applications.

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