Java 数据流之Broadcast State

编辑: admin 分类: java 发布时间: 2021-12-03 来源:互联网

一、BroadcastState 的介绍

广播状态(Broadcast State)是 Operator State 的一种特殊类型。如果我们需要将配置 、规则等低吞吐事件流广播到下游所有 Task 时,就可以使用 BroadcastState。下游的 Task 接收这些配置、规则并保存为 BroadcastState,所有Task 中的状态保持一致,作用于另一个数据流的计算中。
简单理解:一个低吞吐量流包含一组规则,我们想对来自另一个流的所有元素基于此规则进行评估。
场景:动态更新计算规则。

广播状态与其他操作符状态的区别在于:

  • 它有一个 map 格式,用于定义存储结构
  • 它仅对具有广播流和非广播流输入的特定操作符可用
  • 这样的操作符可以具有不同名称的多个广播状态

二、BroadcastState 操作流程

三、案例实现

  • 从端口读取Json数据作为事件流
  • 从Mysql读取数据作为广播流
  • 关联广播流和事件流
  • 匹配对应的用户信息
package cn.kgc.broadcast
 
import java.sql.{Connection, DriverManager, PreparedStatement}
 
import com.alibaba.fastjson.JSON
import org.apache.flink.api.common.state.{BroadcastState, MapStateDescriptor}
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.datastream.BroadcastStream
import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction
import org.apache.flink.streaming.api.functions.source.{RichParallelSourceFunction, SourceFunction}
import org.apache.flink.streaming.api.scala._
import org.apache.flink.util.Collector
 
// (001,'tom',18,'北京',15830010002)
// 定义样例类 接受 MySQL的用户数据
case class BaseUserInfo(id:Long,name:String,age:Int,city:String,phone:Long)
 
// user_id、user_name、user_addrss、behaviour、url
// 输出数据类型
case class UserVisitInfo(id:Long,name:String,city:String,behaviour:String,url:String)
 
// 实现广播ProcessFunction
class MyBroadcastFunc extends BroadcastProcessFunction[String,(Long, BaseUserInfo),UserVisitInfo]{
 
  lazy val mapStateDes = new MapStateDescriptor[Long, BaseUserInfo]("mapState",classOf[Long],classOf[BaseUserInfo])
 
  // 处理的是日志流中的每条数据
  override def processElement(value: String, ctx: BroadcastProcessFunction[String, (Long, BaseUserInfo), UserVisitInfo]#ReadOnlyContext, out: Collector[UserVisitInfo]): Unit = {
    // {"user_id":"001","ts":"2021-07-10 11:10:05","behaviour":"browse","url":"https://www.tb1.com/1.html"}
    val user_id = JSON.parseObject(value).getLong("user_id")
    val behaviour = JSON.parseObject(value).getString("behaviour")
    val url = JSON.parseObject(value).getString("url")
 
    val mapState = ctx.getBroadcastState(mapStateDes)
    val userInfo = mapState.get(user_id)
 
    out.collect(UserVisitInfo(user_id,userInfo.name,userInfo.city,behaviour,url))
 
  }
 
  // 处理的是广播流的每个值
  override def processBroadcastElement(value: (Long, BaseUserInfo), ctx: BroadcastProcessFunction[String, (Long, BaseUserInfo), UserVisitInfo]#Context, out: Collector[UserVisitInfo]): Unit = {
    val mapState: BroadcastState[Long, BaseUserInfo] = ctx.getBroadcastState(mapStateDes)
    mapState.put(value._1,value._2)
  }
}
 
 
class UserSourceFunc extends RichParallelSourceFunction[BaseUserInfo]{
 
  var conn:Connection = _
  var statement: PreparedStatement = _
  var flag:Boolean = true
 
  override def open(parameters: Configuration): Unit = {
    conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/test?characterEncoding=utf-8&serverTimezone=UTC","root","liu911223")
    statement = conn.prepareStatement("select * from base_user")
  }
 
  override def run(ctx: SourceFunction.SourceContext[BaseUserInfo]): Unit = {
    while (flag){
      Thread.sleep(5000)
      val resultSet = statement.executeQuery()
      while (resultSet.next()){
        val id = resultSet.getLong(1)
        val name = resultSet.getString(2)
        val age = resultSet.getInt(3)
        val city = resultSet.getString(4)
        val phone = resultSet.getLong(5)
        ctx.collect(BaseUserInfo(id,name,age,city,phone))
      }
    }
  }
 
  override def cancel(): Unit = {
    flag = false
  }
 
  override def close(): Unit = {
    if (statement != null) statement.close()
    if (conn != null) conn.close()
  }
}
object BroadcastDemo01 {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)
 
    // 定义为KV,一方面是为了广播的时候定义为map,另一方面是为了做关联操作
    val userBaseDS: DataStream[(Long, BaseUserInfo)] = env.addSource(new UserSourceFunc)
      .map(user => (user.id, user))
    val mapStateDes = new MapStateDescriptor[Long, BaseUserInfo]("mapState",classOf[Long],classOf[BaseUserInfo])
    val broadCastStream: BroadcastStream[(Long, BaseUserInfo)] = userBaseDS.broadcast(mapStateDes)
 
    // 日志JSON数据
    val dataInfoDS: DataStream[String] = env.socketTextStream("master",1314)
 
    dataInfoDS.connect(broadCastStream)
      .process(new MyBroadcastFunc)
      .print()
 
    env.execute()
  }
}

到此这篇关于Java 数据流之Broadcast State的文章就介绍到这了,更多相关Java Broadcast State内容请搜索自由互联以前的文章或继续浏览下面的相关文章希望大家以后多多支持自由互联!

【本文由:防cc 提供,感恩】