前提
其中docker-compose不是必须的,单单使用docker也是能够的,这里主要介绍docker和docker-compose两种方式java
docker部署
docker部署kafka很是简单，只须要两条命令便可完成kafka服务器的部署。git
docker run -d --name zookeeper -p 2181:2181 wurstmeister/zookeeper docker run -d --name kafka -p 9092:9092 -e KAFKA_BROKER_ID=0 -e KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181 --link zookeeper -e KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://192.168.1.60(机器IP):9092 -e KAFKA_LISTENERS=PLAINTEXT://0.0.0.0:9092 -t wurstmeister/kafka
因为kafka是须要和zookeeper共同工做的,因此须要部署一个zookeeper,但有了docker这对部署来讲很是轻松.
能够经过docker ps查看到两个容器的状态,这里再也不展现.github
接下来能够进行生产者和消费者的尝试docker
经过kafka自带工具生产消费消息测试
- 首先,进入到kafka的docker容器中
docker exec -it kafka sh
- 运行消费者,进行消息的监听
kafka-console-consumer.sh --bootstrap-server 192.168.1.60:9094 --topic kafeidou --from-beginning
- 打开一个新的ssh窗口,一样进入kafka的容器中,执行下面这条命令生产消息
kafka-console-producer.sh --broker-list 192.168.1.60(机器IP):9092 --topic kafeidou
输入完这条命令后会进入到控制台，能够输入任何想发送的消息,这里发送一个helloapache
>> >hello > > >
- 能够看到,在生产者的控制台中输入消息后,消费者的控制台马上看到了消息
到目前为止,一个kafka完整的hello world就完成了.kafka的部署加上生产者消费者测试.bootstrap
经过java代码进行测试
- 新建一个maven项目并加入如下依赖
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>2.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>0.11.0.2</version>
</dependency>
- 生产者代码
producer.java
import org.apache.kafka.clients.producer.*;
import java.util.Date;
import java.util.Properties;
import java.util.Random;
public class HelloWorldProducer {
public static void main(String[] args) {
long events = 30;
Random rnd = new Random();
Properties props = new Properties();
props.put("bootstrap.servers", "192.168.1.60:9092");
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");
props.put("message.timeout.ms", "3000");
Producer<String, String> producer = new KafkaProducer<>(props);
String topic = "kafeidou";
for (long nEvents = 0; nEvents < events; nEvents++) {
long runtime = new Date().getTime();
String ip = "192.168.2." + rnd.nextInt(255);
String msg = runtime + ",www.example.com," + ip;
System.out.println(msg);
ProducerRecord<String, String> data = new ProducerRecord<String, String>(topic, ip, msg);
producer.send(data,
new Callback() {
public void onCompletion(RecordMetadata metadata, Exception e) {
if(e != null) {
e.printStackTrace();
} else {
System.out.println("The offset of the record we just sent is: " + metadata.offset());
}
}
});
}
System.out.println("send message done");
producer.close();
System.exit(-1);
}
}
- 消费者代码
consumer.java
import java.util.Arrays;
import java.util.Properties;
import org.apache.kafka.clients.consumer.Consumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.serialization.StringDeserializer;
public class HelloWorldConsumer2 {
public static void main(String[] args) {
Properties props = new Properties();
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.1.60:9092");
props.put(ConsumerConfig.GROUP_ID_CONFIG ,"kafeidou_group") ;
props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "true");
props.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, "1000");
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
props.put("auto.offset.reset", "earliest");
Consumer<String, String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Arrays.asList("kafeidou"));
while (true) {
ConsumerRecords<String, String> records = consumer.poll(1000);
for (ConsumerRecord<String, String> record : records) {
System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
}
}
}
}
- 分别运行生产者和消费者便可
生产者打印消息
1581651496176,www.example.com,192.168.2.219 1581651497299,www.example.com,192.168.2.112 1581651497299,www.example.com,192.168.2.20
消费者打印消息服务器
offset = 0, key = 192.168.2.202, value = 1581645295298,www.example.com,192.168.2.202 offset = 1, key = 192.168.2.102, value = 1581645295848,www.example.com,192.168.2.102 offset = 2, key = 192.168.2.63, value = 1581645295848,www.example.com,192.168.2.63
源码地址:FISHStack/kafka-demodom
经过docker-compose部署kafka
首先建立一个docker-compose.yml文件ssh
version: '3.7'
services:
zookeeper:
image: wurstmeister/zookeeper
volumes:
- ./data:/data
ports:
- 2182:2181
kafka9094:
image: wurstmeister/kafka
ports:
- 9092:9092
environment:
KAFKA_BROKER_ID: 0
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://192.168.1.60:9092
KAFKA_CREATE_TOPICS: "kafeidou:2:0" #kafka启动后初始化一个有2个partition(分区)0个副本名叫kafeidou的topic
KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092
volumes:
- ./kafka-logs:/kafka
depends_on:
- zookeeper
部署起来很简单,在docker-compose.yml文件的目录下执行docker-compose up -d就能够了,测试方式和上面的同样。
这个docker-compose作的东西比上面docker方式部署的东西要多一些maven
- 数据持久化，在当前目录下挂在了两个目录分别存储zookeeper和kafka的数据,固然在
docker run命令中添加-v 选项也是能够作到这样的效果的 - kafka在启动后会初始化一个有分区的topic,一样的,
docker run的时候添加-e KAFKA_CREATE_TOPICS=kafeidou:2:0也是能够作到的。
总结:优先推荐docker-compose方式部署
为何呢?
由于单纯使用docker方式部署的话，若是有改动(例如:修改对外开放的端口号)的状况下,docker须要把容器中止docker stop 容器ID/容器NAME,而后删除容器docker rm 容器ID/容器NAME,最后启动新效果的容器docker run ...
而若是在docker-compose部署的状况下若是修改内容只须要修改docker-compose.yml文件对应的地方,例如2181:2181改为2182:2182,而后再次在docker-compose.yml文件对应的目录下执行docker-compose up -d就能达到更新后的效果。
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