kafka的安装非常简单, 只需要配置几个必须的参数

首先, 必须要有zookeeper 集群正常启动

1, conf/server.properties配置

broker.id=0　　# 第几个broker就写几, 从0开始
port=9092

num.network.threads=3
num.io.threads=8  
socket.send.buffer.bytes=1048576  
socket.receive.buffer.bytes=1048576  
socket.request.max.bytes=104857600  
log.dirs=/tmp/kafka-logs  　　　　# 数据存放路径 
num.partitions=2  　　　　　　# 分区数量
log.retention.hours=168  
  
log.segment.bytes=536870912  　　# 设置 segment的大小
log.retention.check.interval.ms=60000  
log.cleaner.enable=false
log.reention.hours=　　　　#消息保留时间, 默认168h, 一周

  
  
zookeeper.connect=node1:2181,node2:2181,node3:2181
zookeeper.connection.timeout.ms=1000000

2, 创建启动脚本./bin/startkfaka.sh, 并赋值启动权限

nohup bin/kafka-server-start.sh config/server.properties > kafka.log 2>&1 &
　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　# 　　　　　　文件存储目录   1: 标准输出, 2: 标准错误, &1: 取值　　后台运行
chmod +x startkafka.sh

3, 分发

注意更改每个从机的broker

4, 启动

bash startkafka.sh

常用的kafka命令

1, kafka-topics.sh

1), 查看topic的列表

kafka-topics.sh --zookeeper node1:2181,node2:2181,node3:2181 --list

2), 查看topic的详细, 可看prtition的具体分配

kafka-topics.sh --zookeeper node1:2181,node2:2181,node3:2181 --describe

[wenbronk@dock bin]$ ./kafka-topics.sh --zookeeper dock:2181 --describe
Topic:20180311    PartitionCount:3    ReplicationFactor:2    Configs:
Topic: 20180311    Partition: 0    Leader: 0    Replicas: 0,1    Isr: 0,1
Topic: 20180311    Partition: 1    Leader: 1    Replicas: 1,2    Isr: 1,2
Topic: 20180311    Partition: 2    Leader: 2    Replicas: 2,0    Isr: 2,0

参数　　

　　partition: 分区

　　leader: 一个主分区负责读写

　　replicas: 副本

　　Isr:

3), 创建topic

kafka-topics.sh --zookeeper node1:2181,node2:2181,node3:2181 --create  --topic 20170926 --partitions 3 --replication-factor 2

参数解释:

　　replication-factor 副本数量,

　　partition 分区数

创建成功以后, 可用 describe进行查看

replication-factor的数量不可超过broker的数量

如果在配置kafka时有指定zookeeper的路径, 那么创建等操作时也需要指定路径

/opt/install/kafka_2.13-2.4.1/bin/kafka-topics.sh --zookeeper 10.144.91.9:2181,10.144.91.10:2181,10.144.91.11:2181/cdn_kafka --create  --topic test1 --partitions 3 --replication-factor 2

4), 删除topic, 并不会真正的删除, 而是更加一个删除位

kafka-topics.sh --zookeeper node1:2181,node2:2181,node3:2181 --delete --topic 20170926

可通过 --list 查看, 添加了 -marked for deletion

2, kafka-consumer-producer.sh

1), 监听一个topic

./kafka-console-consumer.sh --bootstrap-server 10.183.93.127:9093,10.183.93.128:9093,10.183.93.130:9093 --topic letv_env

通过 --from-beginning 从头开始消费

3, kafka-console-producer.sh

2), 启动另一个, 传送消息

./kafka-console-producer.sh --broker-list 10.183.93.127:9093,10.183.93.128:9093,10.183.93.130:9093 --topic letv_qy

更多关于kafka的原理及消息存储, 见美团团队: https://tech.meituan.com/kafka-fs-design-theory.html

3, java操作kafka

老版本的需要依赖 zk, 新版本的不需要依赖zk, 只需要bootstrap-server就可以了

链接时, 只要有几个种子节点就可以发现整个集群

1), provider

import java.util.Properties; 
   
import kafka.javaapi.producer.Producer; 
import kafka.producer.KeyedMessage; 
import kafka.producer.ProducerConfig; 
   
public class MyProducer {   
     
        public static void main(String[] args) {   
            Properties props = new Properties();   
            props.setProperty("metadata.broker.list","localhost:9092");   
            props.setProperty("serializer.class","kafka.serializer.StringEncoder");   
            props.put("request.required.acks","1");   
            ProducerConfig config = new ProducerConfig(props);   
            //创建生产这对象
            Producer<String, String> producer = new Producer<String, String>(config);
            //生成消息
            KeyedMessage<String, String> data = new KeyedMessage<String, String>("mykafka","test-kafka");
            try {   
                int i =1; 
                while(i < 100){    
                    //发送消息
                    producer.send(data);   
                } 
            } catch (Exception e) {   
                e.printStackTrace();   
            }   
            producer.close();   
        }   
}

2) cosumer

import java.util.HashMap; 
import java.util.List;   
import java.util.Map;   
import java.util.Properties;   
     
import kafka.consumer.ConsumerConfig;   
import kafka.consumer.ConsumerIterator;   
import kafka.consumer.KafkaStream;   
import kafka.javaapi.consumer.ConsumerConnector;  
   
public class MyConsumer extends Thread{ 
        //消费者连接
        private final ConsumerConnector consumer;   
        //要消费的话题
        private final String topic;   
     
        public MyConsumer(String topic) {   
            consumer =kafka.consumer.Consumer   
                    .createJavaConsumerConnector(createConsumerConfig());   
            this.topic =topic;   
        }   
     
    //配置相关信息
    private static ConsumerConfig createConsumerConfig() {   
        Properties props = new Properties();   
//        props.put("zookeeper.connect","localhost:2181,10.XX.XX.XX:2181,10.XX.XX.XX:2181");
        //配置要连接的zookeeper地址与端口
        //The ‘zookeeper.connect’ string identifies where to find once instance of Zookeeper in your cluster.
        //Kafka uses ZooKeeper to store offsets of messages consumed for a specific topic and partition by this Consumer Group
        props.put("zookeeper.connect","localhost:2181");
        
        //配置zookeeper的组id (The ‘group.id’ string defines the Consumer Group this process is consuming on behalf of.)
        props.put("group.id", "0");
        
        //配置zookeeper连接超时间隔
        //The ‘zookeeper.session.timeout.ms’ is how many milliseconds Kafka will wait for 
        //ZooKeeper to respond to a request (read or write) before giving up and continuing to consume messages.
        props.put("zookeeper.session.timeout.ms","10000"); 
 
        //The ‘zookeeper.sync.time.ms’ is the number of milliseconds a ZooKeeper ‘follower’ can be behind the master before an error occurs.
        props.put("zookeeper.sync.time.ms", "200");

        //The ‘auto.commit.interval.ms’ setting is how often updates to the consumed offsets are written to ZooKeeper. 
        //Note that since the commit frequency is time based instead of # of messages consumed, if an error occurs between updates to ZooKeeper on restart you will get replayed messages.
        props.put("auto.commit.interval.ms", "1000");
        return new ConsumerConfig(props);   
    }   
     
    public void run(){ 
        
        Map<String,Integer> topickMap = new HashMap<String, Integer>();   
        topickMap.put(topic, 1);   
        Map<String, List<KafkaStream<byte[],byte[]>>>  streamMap =consumer.createMessageStreams(topickMap);   
        
        KafkaStream<byte[],byte[]>stream = streamMap.get(topic).get(0);   
        ConsumerIterator<byte[],byte[]> it =stream.iterator();   
        System.out.println("*********Results********");   
        while(true){   
            if(it.hasNext()){ 
                //打印得到的消息   
                System.err.println(Thread.currentThread()+" get data:" +new String(it.next().message()));   
            } 
            try {   
                Thread.sleep(1000);   
            } catch (InterruptedException e) {   
                e.printStackTrace();   
            }   
        }   
    }  
    
    
    public static void main(String[] args) {   
        MyConsumer consumerThread = new MyConsumer("mykafka");   
        consumerThread.start();   
    }   
}

3), 调用

import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
public class Consumer implements Runnable {
    
    private KafkaStream stream;
    private int threadNumber;
 
    public Consumer(KafkaStream a_stream, int a_threadNumber) {
        threadNumber = a_threadNumber;
        stream = a_stream;
    }
 
    public void run() {
        ConsumerIterator<byte[], byte[]> it = stream.iterator();
        while (it.hasNext())
            System.out.println("Thread " + threadNumber + ": " + new String(it.next().message()));
        System.out.println("Shutting down Thread: " + threadNumber);
    }
}

系列来自尚学堂

来源：oschina

链接：https://my.oschina.net/u/4351246/blog/4309088

标签

ZooKeeper

51-kafka-安装及常用的命令