Flink：将使用keyBy运算符创建的值的映射广播到另一个流，冷启动问题

Question

我有一个高频率的流发布事件，每个事件包含一些关于汽车的信息。我需要处理这个事件流，但排除具有特定城市和车牌号组合的事件。这些列入黑名单的城市和车牌号码组合的信息来自一个每天更新的S3文件。

[
    {
        "name": "Car1",
        "plate": "XYZ123",
        "city": "Berlin"
    },
    {
        "name": "Car2",
        "plate": "XYZ1234",
        "city": "Amsterdam"
    },
    {
        "name": "Car3",
        "plate": "ASD 123",
        "city": "Kuala Lumpur"
    },
    {
        "name": "Car1",
        "plate": "XYZ123",
        "city": "Moscow"
    },
    {
        "name": "Car1",
        "plate": "XYZ123",
        "city": "Barcelona"
    }
]

S3文件如下所示：例如。假设它被称为excludedcars

[
    {
        "plate": "XYZ123",
        "city": "Berlin"
    },
    {
        "plate": "ABC1231",
        "city": "Berlin"
    },
    {
        "plate": "AWS121",
        "city": "Berlin"
    },
    {
        "plate": "XYZ1234",
        "city": "Amsterdam"
    },
    {
        "plate": "AMC3421",
        "city": "Amsterdam"
    },
    {
        "plate": "ASD 123",
        "city": "Kuala Lumpur"
    },
    {
        "plate": "XYZ123",
        "city": "Moscow"
    },
    {
        "plate": "XYZ123",
        "city": "Barcelona"
    }
]

方法：

1. 将S3文件excludedcars用作流源。
2. 转换事件以产生以下结构:

{
    "Berlin": ["XYZ123", "ABC1231", "AWS121"],
    "Amsterdam": ["XYZ1234", "AMC3421"],
    "Kuala Lumpur":["ASD 123"],
    "Moscow":["XYZ123"],
    "Barcelona":["XYZ123"]
}

object Cars {

  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val excludedCarStream: DataStream[Array[ExcludedCarDetail]] = getExcludedCarsStream(env)
    val excludedCarDetails = excludedCarStream.flatMap(item => item) // Array of Excluded Car objects
    excludedCarDetails.map(car => (car.cityId, car.plateNumber)).keyBy(0) // As per my understanding, this should result into a map of city to array of plate number maps 
    excludedCarDetails.print() // This just prints the simple tuples without any grouping by city
    env.execute("Scala SocketTextStreamWordCount Example")
  }

  private def getExcludedCarsStream(env: StreamExecutionEnvironment): DataStream[Array[ExcludedCarDetail]] = {
    val path: String = "file:///Users/name/flinkTest/excluded"
    val textInputFormat = new TextInputFormat(new Path(path))
    env
      .readFile(
        textInputFormat,
        path,
        FileProcessingMode.PROCESS_CONTINUOUSLY,
        1000
      )
      .map(jsonString => {
        val excludedCars: Array[ExcludedCarDetail] = (new Gson).fromJson(jsonString, classOf[Array[ExcludedCarDetail]])
        excludedCars
      })
  }
}

case class ExcludedCarDetail(
  @(SerializedName @scala.annotation.meta.field)("city") cityId: String,
  @(SerializedName @scala.annotation.meta.field)("plate") plateNumber: String
)

广播状态解决方案：

object Cars {

  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    val excludedCarsState: MapStateDescriptor[Int, List[String]] = new MapStateDescriptor("excludedCars", classOf[Int], classOf[List[String]])
    val excludedCarDetails: DataStream[ExcludedCarDetail] = getExcludedCarsStream(env)
    val excludedCarBroadcast: BroadcastStream[ExcludedCarDetail] = excludedCarDetails.broadcast(excludedCarsState)


    val carsStream: DataStream[CarDetail] = getMainCarsStream(env)

    val bs = carsStream
      .keyBy(_.cityId)
      .connect(excludedCarBroadcast)
      .process(new CarsStateLogic(excludedCarsState))

    bs.print()

    env.execute("Scala SocketTextStreamWordCount Example")
  }

  private def getExcludedCarsStream(env: StreamExecutionEnvironment): DataStream[ExcludedCarDetail] = {
    val cars: ListBuffer[ExcludedCarDetail] = ListBuffer()
    for(i <- 0 until 3) {
      val cityId = i+1
      val plateNumber = "Plate"+(i+1)
      cars += ExcludedCarDetail(cityId, plateNumber) // Basically exclude cars with plate1 in city1, plate2 in city2, plate3 in city3
    }
    env.fromCollection(cars.toList)
  }

  private def getMainCarsStream(env: StreamExecutionEnvironment): DataStream[CarDetail] = {
    val cars: ListBuffer[CarDetail] = ListBuffer()
    for(i <- 0 until 10) {
      val cityId = i+1
      val plateNumber = "Plate"+(i+1)
      val name = "Name"+(i+1)
      cars += CarDetail(cityId, plateNumber, name)
    }
    env.fromCollection(cars.toList)
  }
}

case class ExcludedCarDetail(cityId: Int, plateNumber: String)
case class CarDetail(cityId: Int, plateNumber: String, name: String)

class CarsStateLogic(excludedCarsState: MapStateDescriptor[Int, List[String]]) extends KeyedBroadcastProcessFunction[String, CarDetail, ExcludedCarDetail, CarDetail] {
  override def processElement(car: CarDetail, ctx: KeyedBroadcastProcessFunction[String, CarDetail, ExcludedCarDetail, CarDetail]#ReadOnlyContext, out: Collector[CarDetail]): Unit = {
    val state = ctx.getBroadcastState(excludedCarsState)

    if(state.contains(car.cityId)) {
      val cityState = state.get(car.cityId)
      if(cityState.indexOf(car.plateNumber) < 0) { // not excluded
        out.collect(car)
      }
    } else {
      out.collect(car)
    }
  }

  override def processBroadcastElement(value: ExcludedCarDetail, ctx: KeyedBroadcastProcessFunction[String, CarDetail, ExcludedCarDetail, CarDetail]#Context, out: Collector[CarDetail]): Unit = {
    val state = ctx.getBroadcastState(excludedCarsState)
    val newStateForKey = if(state.contains(value.cityId)) {
      value.plateNumber :: state.get(value.cityId)
    } else {
      List(value.plateNumber)
    }
    ctx.getBroadcastState(excludedCarsState).put(value.cityId, newStateForKey)
    println("BroadCast element: CityId:"+ value.cityId+ ", State: "+state.get(value.cityId))
  }
}

但我现在碰到了冷启动问题。在处理主数据之前，确保广播状态可用的可靠方法是什么。

Answer 1

如果排除的cars数据集很小，那么您可以按原样广播它（不按城市分组）。如果它很大，那么您可以按城市键（与car流相同），并将这两个流连接起来，以便每个子任务只获得所有被排除的car和常规car数据的分区集。

注意，您存在冷启动问题，即您希望在处理任何常规汽车数据之前首先处理当前所有排除的汽车数据，这样，在接收排除的汽车数据之前，您就不会从正在处理的汽车数据中得到误报。