Spring Boot Microservices with Spring Cloud Stream Example

In this tutorial, you will learn how to build two Spring Boot microservices that communicate asynchronously using Spring Cloud Stream with Apache Kafka.

Learn Microservices development: Spring Boot Microservices Tutorial.

What You’ll Learn:

• How to build microservices using Spring Boot.
• How to set up Spring Cloud Stream to send and receive messages using Kafka.
• How to run microservices with Kafka as a messaging broker.

Introduction to Spring Cloud Stream and Kafka

Spring Cloud Stream is a module for building event-driven microservices connected to messaging systems like Kafka or RabbitMQ. It abstracts the messaging infrastructure, allowing developers to focus on writing business logic.

Why Use Spring Cloud Stream with Kafka?

• Asynchronous Communication: Microservices can communicate without direct calls, improving system resilience.
• Decoupling: Services are loosely coupled, as they only communicate through messages.
• Scalability: Kafka handles large volumes of data efficiently.

Prerequisites

Before starting, ensure that you have the following tools installed:

• JDK 17 or later
• Maven (to build the project)
• Kafka and Zookeeper installed (or use Docker to run Kafka)
• IDE (IntelliJ IDEA, Eclipse, etc.)

Step 1: Create the Projects

We will create two microservices:

1. employee-service: Sends employee data to Kafka.
2. department-service: Listens to Kafka and receives employee data.

Step 2: Set Up employee-service

2.1 Create the Project

Go to Spring Initializr and generate a Spring Boot project with the following dependencies:

• Spring Web
• Spring Cloud Stream
• Spring for Apache Kafka

2.2 Configure application.yml

Create a configuration file src/main/resources/application.yml for the employee-service to define Kafka bindings.

spring:
  cloud:
    stream:
      bindings:
        output:
          destination: employee-topic
          content-type: application/json
      kafka:
        binder:
          brokers: localhost:9092

Explanation:

• output.destination=employee-topic: Specifies the Kafka topic for sending messages.
• brokers=localhost:9092: Defines the Kafka broker address.

2.3 Create the Employee Model

Define an Employee class to represent employee data.

public class Employee {
    private String id;
    private String name;
    private String department;

    // Constructors, getters, and setters
    public Employee(String id, String name, String department) {
        this.id = id;
        this.name = name;
        this.department = department;
    }

    public String getId() {
        return id;
    }

    public String getName() {
        return name;
    }

    public String getDepartment() {
        return department;
    }
}

2.4 Create a Message Producer

Create a service that will send employee data to Kafka.

import org.springframework.cloud.stream.function.StreamBridge;
import org.springframework.stereotype.Service;

@Service
public class EmployeeProducer {

    private final StreamBridge streamBridge;

    public EmployeeProducer(StreamBridge streamBridge) {
        this.streamBridge = streamBridge;
    }

    public void sendEmployee(Employee employee) {
        streamBridge.send("output", employee);
    }
}

Explanation:

• StreamBridge: Allows sending messages to a Kafka topic dynamically.
• sendEmployee(): Sends employee data to the employee-topic.

2.5 Create a REST Controller

Create a REST controller to trigger message sending.

import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class EmployeeController {

    private final EmployeeProducer employeeProducer;

    public EmployeeController(EmployeeProducer employeeProducer) {
        this.employeeProducer = employeeProducer;
    }

    @PostMapping("/employees")
    public String createEmployee(@RequestBody Employee employee) {
        employeeProducer.sendEmployee(employee);
        return "Employee sent: " + employee.getName();
    }
}

2.6 Create a Dockerfile

Create a Dockerfile for employee-service:

FROM openjdk:17-jdk-alpine
WORKDIR /app
COPY target/employee-service-0.0.1-SNAPSHOT.jar employee-service.jar
EXPOSE 8081
ENTRYPOINT ["java", "-jar", "employee-service.jar"]

Step 3: Set Up department-service

3.1 Create the Project

Go to Spring Initializr and generate another Spring Boot project with the following dependencies:

• Spring Web
• Spring Cloud Stream
• Spring for Apache Kafka

3.2 Configure application.yml

Create a configuration file src/main/resources/application.yml for department-service:

spring:
  cloud:
    stream:
      bindings:
        input:
          destination: employee-topic
          content-type: application/json
      kafka:
        binder:
          brokers: localhost:9092

Explanation:

• input.destination=employee-topic: Listens to the same Kafka topic (employee-topic) to receive messages.

3.3 Create the Employee Model

Create the same Employee model as in employee-service to deserialize the received message:

public class Employee {
    private String id;
    private String name;
    private String department;

    // Constructors, getters, and setters
    public Employee() {}

    public Employee(String id, String name, String department) {
        this.id = id;
        this.name = name;
        this.department = department;
    }

    public String getId() {
        return id;
    }

    public String getName() {
        return name;
    }

    public String getDepartment() {
        return department;
    }
}

3.4 Create a Message Consumer

Create a service to consume employee data from Kafka.

import org.springframework.context.annotation.Bean;
import org.springframework.stereotype.Service;
import java.util.function.Consumer;

@Service
public class EmployeeConsumer {

    @Bean
    public Consumer<Employee> input() {
        return employee -> {
            System.out.println("Received employee: " + employee.getName() + " from department " + employee.getDepartment());
        };
    }
}

Explanation:

• @Bean Consumer<Employee> input(): Registers a function that consumes messages from Kafka.

3.5 Create a Dockerfile

Create a Dockerfile for department-service:

FROM openjdk:17-jdk-alpine
WORKDIR /app
COPY target/department-service-0.0.1-SNAPSHOT.jar department-service.jar
EXPOSE 8082
ENTRYPOINT ["java", "-jar", "department-service.jar"]

Step 4: Set Up Kafka with Docker Compose

Create a docker-compose.yml file to run Kafka and Zookeeper:

version: '3.8'

services:
  zookeeper:
    image: confluentinc/cp-zookeeper:latest
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
    ports:
      - "2181:2181"  

kafka:
    image: confluentinc/cp-kafka:latest
    environment:
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
    ports:
      - "9092:9092"
    depends_on:
      - zookeeper

Run Kafka and Zookeeper:

docker-compose up -d

Step 5: Build Docker Images

Navigate to the root directories of each service and run:

For employee-service:

mvn clean package
docker build -t employee-service .

For department-service:

mvn clean package
docker build -t department-service .

Step 6: Create a Docker Compose File for Services

Create a docker-compose.yml file to run both microservices with Kafka:

version: '3.8'

services:
  employee-service:
    image: employee-service
    build:
      context: ./employee-service
    ports:
      - "8081:8081"
    networks:
      - microservices-net

  department-service:
    image: department-service
    build:
      context: ./department-service
    ports:
      - "8082:8082"
    networks:
      - microservices-net

  kafka:
    image: confluentinc/cp-kafka:latest
    ports:
      - "9092:9092"
    environment:
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
    networks:
      - microservices-net

  zookeeper:
    image: confluentinc/cp-zookeeper:latest
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
    ports:
      - "2181:2181"
    networks:
      - microservices-net

networks:
  microservices-net:
    driver: bridge

Step 7: Run Docker Compose

Navigate to the directory containing the docker-compose.yml file and run:

docker-compose up --build

Docker Compose will build and start the containers.

Step 8: Test the Microservices Communication

Use Postman or curl to send employee data to the employee-service:

curl -X POST http://localhost:8081/employees \
-H "Content-Type: application/json" \
-d '{"id":"1", "name":"John Doe", "department":"Engineering"}'

The department-service should log the received employee data in the console.

Conclusion

You have successfully built two Spring Boot microservices that communicate asynchronously using Spring Cloud Stream and Kafka. This setup demonstrates how to build scalable, event-driven microservices architecture.

Original Tutorial:

Spring Boot Microservices with Spring Cloud Stream Example

Learn Microservices development: Spring Boot Microservices Tutorial.

References:

Ramesh Fadatare

Java Guides