zoukankan      html  css  js  c++  java
  • kafka java api生产者

    import java.util.HashMap;

    import java.util.List;
    import java.util.Map;
    import java.util.Properties;

    import org.junit.Test;

    import kafka.consumer.Consumer;
    import kafka.consumer.ConsumerConfig;
    import kafka.consumer.ConsumerIterator;
    import kafka.consumer.KafkaStream;
    import kafka.javaapi.consumer.ConsumerConnector;
    import kafka.javaapi.producer.Producer;
    import kafka.producer.KeyedMessage;
    import kafka.producer.ProducerConfig;


    public class KafkaProducer {

    private final Producer<String, String> producer;
    public final static String TOPIC = "test";

    private KafkaProducer(){

    Properties props = new Properties();

    // 此处配置的是kafka的broker地址:端口列表
    props.put("metadata.broker.list", "192.168.170.185:9092,192.168.170.185:9093,192.168.170.185:9094");

    //配置value的序列化类
    props.put("serializer.class", "kafka.serializer.StringEncoder");

    //配置key的序列化类
    props.put("key.serializer.class", "kafka.serializer.StringEncoder");

    //request.required.acks
    //0, which means that the producer never waits for an acknowledgement from the broker (the same behavior as 0.7). This option provides the lowest latency but the weakest durability guarantees (some data will be lost when a server fails).
    //1, which means that the producer gets an acknowledgement after the leader replica has received the data. This option provides better durability as the client waits until the server acknowledges the request as successful (only messages that were written to the now-dead leader but not yet replicated will be lost).
    //-1, which means that the producer gets an acknowledgement after all in-sync replicas have received the data. This option provides the best durability, we guarantee that no messages will be lost as long as at least one in sync replica remains.
    // props.put("request.required.acks","-1");

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

    void produce() {
    int messageNo = 1;
    final int COUNT = 101;

    int messageCount = 0;
    while (messageNo < COUNT) {
    String key = String.valueOf(messageNo);
    String data = "Hello kafka message :" + key;
    producer.send(new KeyedMessage<String, String>(TOPIC, key ,data));
    System.out.println(data);
    messageNo ++;
    messageCount++;
    }

    System.out.println("Producer端一共产生了" + messageCount + "条消息!");
    }

    public static void main( String[] args )
    {
    new KafkaProducer().produce();
    }

    }

  • 相关阅读:
    Visual Studio 2019 使用 Web Deploy 发布远程站点到IIS服务器
    postman下载地址
    ASP.NET Core开发-Docker部署运行
    C# ffmpeg 视频处理格式转换具体案例
    C# ffmpeg 视频处理格式转换和添加水印
    C# ffmpeg 视频处理
    Tomcat 安装与配置
    Maven 快速入门
    Jenkins 快速搭建
    Google SRE 读书笔记 扒一扒SRE用的那些工具
  • 原文地址:https://www.cnblogs.com/wangjing666/p/6860748.html
Copyright © 2011-2022 走看看