sparkStreaming的mapWithState函数【案例二】

sparkStreaming是以连续bathinterval为单位，进行bath计算，在流式计算中，如果我们想维护一段数据的状态，就需要持久化上一段的数据，sparkStreaming提供的MapwithState函数，用于更新数据状态。

例子：（更新用户当前的操作状态）

1:定义用户会话类

package com.streamkafka.user_state_update

import org.omg.CORBA.UserException

/**
 * userNo:用户账号
 * userName:用户名称
 * userOperation:用户的操作 枚举
 * userIsVIP：用户是否是会员 枚举
 */
class UserSession (userNO:String,userName:String,userOperation:String,userIsVIP:Int)  extends Serializable{

   var userNo="";
   var user_name="";
   var user_operation="";
   var user_vip=false;

  /**
   * 如果要在构造方法里调用其他方法，需要在构造方法的第一行调用构造方法
   */
 def userTrans(userNO:String,user_Name:String,userOperation:Int,userIsVIP:Int){
   //this(userNO,user_Name,userOperation.toString(),userIsVIP);
   userNo=userNO;
   user_name=user_Name;
   this.operationTran(userOperation.toString())
   this.isVipTran(userIsVIP);
 }
 //定义无参的构造方法
  def this()={
     this("","","",);
  }
  def operationTran(userOp:String){
   if(userOp.equals(UserEnum.login)){
     user_operation="login";
   }
   if(userOp.equals(UserEnum.loginOut)){
     user_operation="loginOut";
   }
   if(userOp.equals(UserEnum.clickNextPage)){
     user_operation="clickNextPage";
   }
   if(userOp.equals(UserEnum.clickPrePage)){
     user_operation="clickPrePage";
   }
   if(userOp.equals(UserEnum.createUser)){
     user_operation="createUser";
   }
  }
   def isVipTran(userIsVIP:Int){
      if(userIsVIP==UserEnum.Yvip){
        user_vip=true;
      }
      if(userIsVIP==UserEnum.Nvip){
        user_vip=false;
       }
    }
    @Override
  def toStrings:String= {
    return "UserSession [param: userNo("+userNo+"),userName("+user_name+"),userOperation("+user_operation+"),user("+user_vip+")]";
  }
}

2：定义状态枚举类

package com.streamkafka.user_state_update

import java.io.Serializable

object  UserEnum extends Serializable{
  //operation 枚举
  val  login="";//登录操作
  val loginOut="";//退出操作
  val clickNextPage="";//点击下一页操作
  val clickPrePage="";//点击上一页操作
  val createUser="";//创建用户操作
 //是否是会员 枚举
  val Yvip=;
  val Nvip=;
}

3：定义生产者（生产者类是使用java写的）

package com.streamkafka.user_state_update;

import java.io.Serializable;
import java.util.Properties;
import java.util.Random;

import org.apache.commons.lang.math.RandomUtils;
import org.apache.kafka.clients.producer.Callback;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;

/**
 * 用户信息的格式
 * userNo:用户账号  （数字+字母 长度：5）
 * userName:用户名称  （汉字）
 * userOperation:用户的操作 枚举 （数字：0,1,2,3,5）
 * userIsVIP：用户是否是会员 枚举 (0,,1)
 * @author a
 */
public class UserInfiProducer extends Thread  implements Serializable{
    private String topic="test";
    private String userNo="userId-9iVEYecP";
    private String userName="zhangxs";
    private int userOper=;
    private int isvip=;
    private String message="";
     Properties props=null;
    RandomUtils rand=new RandomUtils();
    Random r=new Random();
    private static int msgCount=;
    //消费者配置
    private Properties producerParam(){
         props = new Properties();
         props.put("bootstrap.servers", "192.168.99.xxx:9092");//kafka的服务器ip
         props.put("zk.connect", "192.168.99.143:2181");
         props.put("acks", "all");
         props.put("retries", );
         props.put("batch.size", );
         props.put("linger.ms", );
         props.put("buffer.memory", );
         props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
         props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
         return props;
    }
    //生产用户消息
    private String producerInfo(){
        StringBuffer mess=new StringBuffer();
        int oper=r.nextInt();//随机产生用户的操作
        if(!=oper){
            userOper=oper;
        }
        isvip=r.nextInt();//用户是否是会员
        mess.append(userNo).append("|")
        .append(userName).append("|")
        .append(String.valueOf(userOper)).append("|")
        .append(String.valueOf(isvip));
        return mess.toString();
    }
    KafkaProducer kp=new KafkaProducer(producerParam());
    private void sendMsg(String message){
        //生产者消息配置参数
        Properties proper=this.producerParam();
          //回调函数
        Callback c=new Callback() {

            @Override
            public void onCompletion(RecordMetadata paramRecordMetadata, Exception paramException) {
                // TODO Auto-generated method stub
                System.out.println("topic:"+paramRecordMetadata.topic());
                System.out.println("partition:"+paramRecordMetadata.partition());
                if(null!=paramException){
                    System.out.println("getMessage:"+paramException.getMessage());
                }
            }
        };
        //创建消息发送器
        ProducerRecord<String,String> precord=new ProducerRecord<String,String>(topic, message);
        //发送消息
        kp.send(precord, c);
    }

    @Override
    public void run() {
        while(true){
            try {
                message=producerInfo();
                System.out.println("producer:"+message);
                sendMsg(message);
                System.out.println("message send success");
                msgCount++;
                System.out.println("成功发送【"+msgCount+"】消息");
                Thread.sleep();
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
        }

    }
    public static void main(String[] args) {
        UserInfiProducer userPord=new UserInfiProducer();
        Thread thread=new Thread(userPord);
        thread.start();
    }
}

4：定义消费者

package com.streamkafka.user_state_update

import org.apache.spark.streaming.kafka010.KafkaUtils
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.SparkConf
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.streaming.dstream.DStream
import org.apache.commons.logging.LogFactory
import org.apache.commons.logging.Log
import scala.actors.threadpool.ExecutorService
import scala.actors.threadpool.Executors
import org.apache.spark.streaming.State
import org.apache.spark.streaming.StateSpec
import org.apache.spark.streaming.Time

object UserInfoConsumer extends Serializable {
  var logs = LogFactory.getLog(UserInfoConsumer.getClass);
  def main(args: Array[String]): Unit = {
    /* val threadPool:ExecutorService=Executors.newFixedThreadPool(5)
    threadPool.execute(new ConsumerProcess())*/
    var cc = new ConsumerProcess();
    cc.resoleMethod();

  }
  class ConsumerProcess extends Serializable {
    def resoleMethod() {
      var conf = new SparkConf();
      conf.setMaster("spark://192.168.99.xxx:7077").setAppName("user_state_update");
      var ssc = new StreamingContext(conf, Seconds());//创建sparkContext，这是sparkStreaming的入口
      var topic = Array("test");
      ssc.checkpoint(".");
      var kafkaParams = Map(
        //建立初始链接到kafka的服务器，这个可以是动态的所以不需要已下载配置完所有的服务器 "192.168.99.xxx:9092,anotherhost:9092"
        "bootstrap.servers" -> "192.168.99.xxx:9092",
        //反序列化器类实现了串并转换器接口的关键。 新的消费者配置
        "key.deserializer" -> classOf[StringDeserializer],
        //反序列化器类值,实现了串并转换器接口。 新的消费者配置
        "value.deserializer" -> classOf[StringDeserializer],
        //这是一个唯一的标识，用来标识这个消费者时属于哪个消费者组 。   新的消费者配置
        "group.id" -> "user-consumer-group1",
        //
        "auto.offset.reset" -> "latest",
        //true 定期在后台提交
        "enable.auto.commit" -> (false: java.lang.Boolean))
      //println("接受到的消息【"+count+"】")
      //Subscribe 指定订阅的主题和配置
      var dStream = KafkaUtils.createDirectStream(ssc, PreferConsistent, Subscribe[String, String](topic, kafkaParams));
      dStream.foreachRDD(rdd => {
        var user: UserSession = new UserSession();
        rdd.foreach(f => {
          var msgStr = f.value().split("\\|");//获取消息体
          println("usreNo:" + msgStr())
          //对消息进行解析，并封装成userSession
          if ( == msgStr.length) {
            print("组装userSession")
            user.userTrans(msgStr().toString(), msgStr().toString(), msgStr().toInt, msgStr().toString().toInt)
          } else {
            print("消息格式不符合定义!!!")
          }
          //打印组装后的userInfo
          println("userInfo:" + user.toStrings);
        })
        //更新用户的状态
      })
　　　　　

//更新用户状态的函数
var mapWithStateMethod = (userState: String, one: Option[Int], state: State[Int]) => {
        var stateInt = userState.toInt;
        var userM = new UserSession();
        userM.operationTran(stateInt.toString());
        //返回用户当前状态和对应的枚举
        var output=(userM.user_operation,stateInt);
        println("当前用户的状态为:" + userM.user_operation)
        state.update(stateInt);
        output
      }

      var mapState = dStream.map(x => (x.value().split("\\|")(), ));
      var userState = mapState.mapWithState(StateSpec.function(mapWithStateMethod);
      println("userStatePrint：" + userState.print());
      print("=============================================================================")
      ssc.start();
      ssc.awaitTermination();
    }
    /* override def run(){
      while(true){
        print("进入run方法.................");
        resoleMethod();
         ssc.start();
        ssc.awaitTermination();
         Thread.sleep(5000L);
      }
    }*/
  }
}