云服务器docker-compose部署kafka并编写ava使用kafka示例代码

一、部署Kafka

1. 在云服务器上安装Docker和Docker Compose；
2. 创建一个文件夹，命名为kafka，进入该文件夹；
3. 创建docker-compose.yml文件，写入以下内容：

version: '3.7'
services:
  zookeeper:
    image: wurstmeister/zookeeper
    container_name: zookeeper
    restart: always
    ports:
      - "2181:2181"
  kafka:
    image: wurstmeister/kafka
    container_name: kafka
    restart: always
    ports:
      - "9092:9092"
    environment:
      KAFKA_ADVERTISED_HOST_NAME: <云服务器IP>
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_CREATE_TOPICS: "test_topic:1:1"
    depends_on:
      - zookeeper

4. 执行以下命令启动Kafka：

docker-compose up -d

二、编写Java使用Kafka示例代码

1. 在本地开发环境中创建一个Java项目，并在pom.xml文件中添加以下依赖：

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>2.5.0</version>
</dependency>

2. 编写一个生产者类Producer.java：

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.util.Properties;

public class Producer {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put("bootstrap.servers", "<云服务器IP>:9092");
        props.put("acks", "all");
        props.put("retries", 0);
        props.put("batch.size", 16384);
        props.put("linger.ms", 1);
        props.put("buffer.memory", 33554432);
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        Producer<String, String> producer = new KafkaProducer<>(props);

        for (int i = 0; i < 10; i++)
            producer.send(new ProducerRecord<>("test_topic", Integer.toString(i), Integer.toString(i)));

        producer.close();
    }
}

3. 编写一个消费者类Consumer.java：

import org.apache.kafka.clients.consumer.Consumer;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import java.util.Collections;
import java.util.Properties;

public class Consumer {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put("bootstrap.servers", "<云服务器IP>:9092");
        props.put("group.id", "test");
        props.put("enable.auto.commit", "true");
        props.put("auto.commit.interval.ms", "1000");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

        Consumer<String, String> consumer = new KafkaConsumer<>(props);
        consumer.subscribe(Collections.singletonList("test_topic"));

        while (true) {
            ConsumerRecords<String, String> records = consumer.poll(100);
            for (org.apache.kafka.clients.consumer.ConsumerRecord<String, String> record : records) {
                System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
            }
        }
    }
}

4. 分别编译Producer.java和Consumer.java，并在命令行中执行以下命令运行Producer：

java Producer

5. 在另一个命令行中执行以下命令运行Consumer：

java Consumer

6. 可以在Consumer的命令行中看到Producer发送的消息。