本文主要是介绍Flink项目4 双流connect项目，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

1、一个是订单流，一个是对账流

定时器螫不区分key的，是项目视角的

package flinkProject

import java.text.SimpleDateFormat

import flinkSourse.SensorReading
import org.apache.flink.api.common.state.{ValueState, ValueStateDescriptor}
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.functions.co.CoProcessFunction
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.util.Collector

case class ReceiptEvent(txid:String,payChannel:String,timestamp:Long)
case class OrderEvent(txid:String,payChannel:String,timestamp:Long)

object TxConnectedMatch {
  def main(args: Array[String]): Unit = {
    val executionEnvironment: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    executionEnvironment.setParallelism(1)
    executionEnvironment.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) //watermark周期性生成，默认是200ms

    val stream1: DataStream[String] = executionEnvironment.socketTextStream("127.0.0.1", 1111)

    val receiptDataStream: DataStream[ReceiptEvent] = stream1.map(data => {
      val tmpList = data.split(" ")
      val simpleDateFormat = new SimpleDateFormat("dd/mm/yy:HH:mm:ss")
      val ts = simpleDateFormat.parse(tmpList(2)).getTime
      ReceiptEvent(tmpList(0), tmpList(1), ts)
    }).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[ReceiptEvent](Time.seconds(0)) {
      override def extractTimestamp(t: ReceiptEvent) = t.timestamp
    })


    val stream2: DataStream[String] = executionEnvironment.socketTextStream("127.0.0.1", 2222)

    val orderStram: DataStream[OrderEvent] = stream2.map(data => {
      val tmpList = data.split(" ")
      val simpleDateFormat = new SimpleDateFormat("dd/mm/yy:HH:mm:ss")
      val ts = simpleDateFormat.parse(tmpList(2)).getTime
      OrderEvent(tmpList(0), tmpList(1), ts)
    }).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[OrderEvent](Time.seconds(0)) {
      override def extractTimestamp(t: OrderEvent) = t.timestamp
    })

    val result: DataStream[(ReceiptEvent, OrderEvent)] = receiptDataStream.connect(orderStram)
      .keyBy((receipt => receipt.txid), (order => order.txid))
      .process(new ConnectedCoProcessFunction())

    result.print("result")
    result.getSideOutput(new OutputTag[OrderEvent]("order_output_tag")).print("order_output_tag")
    result.getSideOutput(new OutputTag[ReceiptEvent]("receipt_output_tag")).print("receipt_output_tag  ")


    executionEnvironment.execute("connected Stream")
  }

}

class ConnectedCoProcessFunction extends CoProcessFunction[ReceiptEvent,OrderEvent,(ReceiptEvent,OrderEvent)] {
  var receiptValueState:ValueState[ReceiptEvent]=_
  var orderValueState:ValueState[OrderEvent]=_

  override def open(parameters: Configuration): Unit = {
    receiptValueState=getRuntimeContext.getState[ReceiptEvent](new ValueStateDescriptor[ReceiptEvent]("receipt",classOf[ReceiptEvent]))
    orderValueState=getRuntimeContext.getState[OrderEvent](new ValueStateDescriptor[OrderEvent]("order",classOf[OrderEvent]))
  }

  override def processElement1(in1: ReceiptEvent, context: CoProcessFunction[ReceiptEvent, OrderEvent, (ReceiptEvent, OrderEvent)]#Context, collector: Collector[(ReceiptEvent, OrderEvent)]): Unit = {
    var order=orderValueState.value()
    //订单先来
    if(order!=null){
      collector.collect((in1,order))
      orderValueState.clear()
    }else{
      receiptValueState.update(in1)
      context.timerService().registerEventTimeTimer(in1.timestamp+3000l)
    }
  }

  override def processElement2(in2: OrderEvent, context: CoProcessFunction[ReceiptEvent, OrderEvent, (ReceiptEvent, OrderEvent)]#Context, collector: Collector[(ReceiptEvent, OrderEvent)]): Unit = {
    var receipt=receiptValueState.value()
    //receipt先来
    if(receipt!=null){
      collector.collect(receipt,in2)
      receiptValueState.clear()
    }else{
      orderValueState.update(in2)
      context.timerService().registerEventTimeTimer(in2.timestamp+3000l)
    }
  }

  override def onTimer(timestamp: Long, ctx: CoProcessFunction[ReceiptEvent, OrderEvent, (ReceiptEvent, OrderEvent)]#OnTimerContext, out: Collector[(ReceiptEvent, OrderEvent)]): Unit = {
    if(receiptValueState.value()!=null){
      ctx.output(new OutputTag[ReceiptEvent]("receipt_output_tag"),receiptValueState.value())
    }
    if(orderValueState.value()!=null){
      ctx.output(new OutputTag[OrderEvent]("order_output_tag"),orderValueState.value() )
    }
    receiptValueState.clear()
    orderValueState.clear()
  }
}

2、输入数据

正常的只要两个流有匹配的txId就会输出，不管延迟多长时间

定时器是不区分key的，是项目视角的

只有一个流里面有的时候，定时器延迟3s，每个流根据自己的watermark，如下

流1输入：4 404 17/05/2015:10:26:45   不会有输出

流1输入：5 404 17/05/2015:10:26:47   不会有输出，

流1输入：7 404 17/05/2015:10:26:49   

输出：receipt_output_tag  > ReceiptEvent(4,404,1421461605000)

流1输入：9 404 17/05/2015:10:26:59   watermark是10:26:59

输出：

receipt_output_tag  > ReceiptEvent(5,404,1421461607000)
receipt_output_tag  > ReceiptEvent(7,404,1421461609000)

流2输入：6 505 17/05/2015:10:26:55   不会有输出

流2输入：8 505 17/05/2015:10:26:56   不会有输出

流2输入：1 505 17/05/2015:10:27:01

输出：

order_output_tag> OrderEvent(6,505,1421461615000)
order_output_tag> OrderEvent(8,505,1421461616000)

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