用 Docker 快速搭建 Kafka 集群

安装 Zookeeper 和 Kafka

首先安装docker-compose，二进制文件地址 https://share.weiyun.com/ABWUW8xZ

将15M的docker-compose文件 放入/usr/local/bin
chmod +x /usr/local/bin/docker-compose
docker-compose -v

Kafka 依赖 Zookeeper，所以我们需要在安装 Kafka 之前先拥有 Zookeeper。准备如下的 docker-compose.yaml 文件，将文件中的主机地址 192.168.1.100 替换成你自己的环境中的主机地址即可。

 

编辑docker-compose.yaml 文件

version: "3"
services:
  zookeeper:
    image: zookeeper
    build:
      context: ./
    container_name: zookeeper
    ports:
      - 2181:2181
    volumes:
      - ./data/zookeeper/data:/data
      - ./data/zookeeper/datalog:/datalog
      - ./data/zookeeper/logs:/logs
    restart: always
  kafka_node_0:
    depends_on:
      - zookeeper
    build:
      context: ./
    container_name: kafka-node-0
    image: wurstmeister/kafka
    environment:
      KAFKA_BROKER_ID: 0
      KAFKA_ZOOKEEPER_CONNECT: 192.168.1.100:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://192.168.1.100:9092
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092
      KAFKA_NUM_PARTITIONS: 3
      KAFKA_DEFAULT_REPLICATION_FACTOR: 2
    ports:
      - 9092:9092
    volumes:
      - ./data/kafka/node_0:/kafka
    restart: unless-stopped
  kafka_node_1:
    depends_on:
      - kafka_node_0
    build:
      context: ./
    container_name: kafka-node-1
    image: wurstmeister/kafka
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: 192.168.1.100:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://192.168.1.100:9093
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9093
      KAFKA_NUM_PARTITIONS: 3
      KAFKA_DEFAULT_REPLICATION_FACTOR: 2
    ports:
      - 9093:9093
    volumes:
      - ./data/kafka/node_1:/kafka
    restart: unless-stopped
  kafka_node_2:
    depends_on:
      - kafka_node_1
    build:
      context: ./
    container_name: kafka-node-2
    image: wurstmeister/kafka
    environment:
      KAFKA_BROKER_ID: 2
      KAFKA_ZOOKEEPER_CONNECT: 192.168.1.100:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://192.168.1.100:9094
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9094
      KAFKA_NUM_PARTITIONS: 3
      KAFKA_DEFAULT_REPLICATION_FACTOR: 2
    ports:
      - 9094:9094
    volumes:
      - ./data/kafka/node_2:/kafka
    restart: unless-stopped

输入 docker-compose up -d 运行脚本文件进行集群构建。等待一会儿。

SpringBoot 集成 Kafka 集群

创建一个全新的 SpringBoot 工程，在 build.gradle 文件中添加下列依赖

1.在 application.properties 进行 Kafka 相关参数配置。

spring.kafka.bootstrap-servers=192.168.1.100:9092,192.168.1.100:9093,192.168.1.100:9094
spring.kafka.producer.retries=0
spring.kafka.producer.batch-size=16384
spring.kafka.producer.buffer-memory=33554432
spring.kafka.producer.key-serializer=org.apache.kafka.common.serialization.StringSerializer
spring.kafka.producer.value-serializer=org.apache.kafka.common.serialization.StringSerializer
spring.kafka.consumer.auto-offset-reset=latest
spring.kafka.consumer.enable-auto-commit=true
spring.kafka.consumer.auto-commit-interval=100

2.创建消息体类。

public class Message {    private Long id;    private String message;    private Date sendAt;}

3.创建消息发送者

public class Sender {   
 @Autowired   
 private KafkaTemplate<String, String> kafkaTemplate;    
public void send() {       
 Message message = new Message();       
 message.setId(System.currentTimeMillis());       
 message.setMessage(UUID.randomUUID().toString());       
 message.setSendAt(new Date());       
 log.info("message = {}", JSON.toJSONString(message));        
kafkaTemplate.send("test", JSON.toJSONString(message));  
  }}

4.创建消息接收者

public class Receiver {   
 @KafkaListener(topics = {"test"}, groupId = "test")    
public void listen(ConsumerRecord<?, ?> record) {     
   Optional<?> message = Optional.ofNullable(record.value());    
    if (message.isPresent()) {          
   log.info("receiver record = " + record);        
    log.info("receiver message = " + message.get());    
    }   
 }
}

5.测试消息队列

public class QueueController {    
@Autowired   
 private Sender sender;    
@PostMapping("/test")    
public void testQueue() {        
sender.send();        
sender.send();        
sender.send();  
  }}

 

到这里就我们就成功搭建了一个 Kafka 伪集群，并成功与 SpringBoot 进行整合