当processElement依赖于广播的数据时，如何在flink中单元测试BroadcastProcessFunction

Question

我用BroadcastProcessFunction实现了一个flink流。从processBroadcastElement获取模型，并将其应用于processElement中的事件。

我没有找到对流进行单元测试的方法，因为我没有找到确保在第一个事件之前调度模型的解决方案。我想说有两种方法可以实现这一点：
1。找到一个解决方案，首先在流中推送模型。在流的执行之前，使用模型填充广播状态，以便恢复流

我可能错过了什么，但我没有找到一个简单的方法来做到这一点。

下面是我的问题的一个简单单元测试：

import org.apache.flink.api.common.state.MapStateDescriptor
import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction
import org.apache.flink.streaming.api.functions.sink.SinkFunction
import org.apache.flink.streaming.api.scala._
import org.apache.flink.util.Collector
import org.scalatest.Matchers._
import org.scalatest.{BeforeAndAfter, FunSuite}

import scala.collection.mutable


class BroadCastProcessor extends BroadcastProcessFunction[Int, (Int, String), String] {

  import BroadCastProcessor._

  override def processElement(value: Int,
                              ctx: BroadcastProcessFunction[Int, (Int, String), String]#ReadOnlyContext,
                              out: Collector[String]): Unit = {
    val broadcastState = ctx.getBroadcastState(broadcastStateDescriptor)

    if (broadcastState.contains(value)) {
      out.collect(broadcastState.get(value))
    }
  }

  override def processBroadcastElement(value: (Int, String),
                                       ctx: BroadcastProcessFunction[Int, (Int, String), String]#Context,
                                       out: Collector[String]): Unit = {
    ctx.getBroadcastState(broadcastStateDescriptor).put(value._1, value._2)
  }
}

object BroadCastProcessor {
  val broadcastStateDescriptor: MapStateDescriptor[Int, String] = new MapStateDescriptor[Int, String]("int_to_string", classOf[Int], classOf[String])
}

class CollectSink extends SinkFunction[String] {

  import CollectSink._

  override def invoke(value: String): Unit = {
    values += value
  }
}

object CollectSink { // must be static
  val values: mutable.MutableList[String] = mutable.MutableList[String]()
}

class BroadCastProcessTest extends FunSuite with BeforeAndAfter {

  before {
    CollectSink.values.clear()
  }

  test("add_elem_to_broadcast_and_process_should_apply_broadcast_rule") {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)

    val dataToProcessStream = env.fromElements(1)

    val ruleToBroadcastStream = env.fromElements(1 -> "1", 2 -> "2", 3 -> "3")

    val broadcastStream = ruleToBroadcastStream.broadcast(BroadCastProcessor.broadcastStateDescriptor)

    dataToProcessStream
      .connect(broadcastStream)
      .process(new BroadCastProcessor)
      .addSink(new CollectSink())

    // execute
    env.execute()

    CollectSink.values should contain("1")
  }
}

由于David Anderson的更新，我选择了缓冲区解决方案。我为同步定义了一个进程函数：

class SynchronizeModelAndEvent(modelNumberToWaitFor: Int) extends CoProcessFunction[Int, (Int, String), Int] {
  val eventBuffer: mutable.MutableList[Int] = mutable.MutableList[Int]()
  var modelEventsNumber = 0

  override def processElement1(value: Int, ctx: CoProcessFunction[Int, (Int, String), Int]#Context, out: Collector[Int]): Unit = {
    if (modelEventsNumber < modelNumberToWaitFor) {
      eventBuffer += value
      return
    }
    out.collect(value)
  }

  override def processElement2(value: (Int, String), ctx: CoProcessFunction[Int, (Int, String), Int]#Context, out: Collector[Int]): Unit = {
    modelEventsNumber += 1

    if (modelEventsNumber >= modelNumberToWaitFor) {
      eventBuffer.foreach(event => out.collect(event))
    }
  }
}

因此，我需要将其添加到我的流中：

dataToProcessStream
  .connect(ruleToBroadcastStream)
  .process(new SynchronizeModelAndEvent(3))
  .connect(broadcastStream)
  .process(new BroadCastProcessor)
  .addSink(new CollectSink())

谢谢

Answer 1

感谢David Anderson和Matthieu，我编写了这个通用的CoFlatMap函数，可以在事件流上产生请求的延迟：

import org.apache.flink.streaming.api.functions.co.CoProcessFunction
import org.apache.flink.util.Collector

import scala.collection.mutable

class SynchronizeEventsWithRules[A,B](rulesToWait: Int) extends CoProcessFunction[A, B, A] {
  val eventBuffer: mutable.MutableList[A] = mutable.MutableList[A]()
  var processedRules = 0
  override def processElement1(value: A, ctx: CoProcessFunction[A, B, A]#Context, out: Collector[A]): Unit = {
    if (processedRules < rulesToWait) {
      println("1 item buffered")
      println(rulesToWait+"--"+processedRules)
      eventBuffer += value
      return
    }
    eventBuffer.clear()
    println("send input to output without buffering:")
    out.collect(value)
  }

  override def processElement2(value: B, ctx: CoProcessFunction[A, B, A]#Context, out: Collector[A]): Unit = {
    processedRules += 1
    println("1 rule processed processedRules:"+processedRules)
    if (processedRules >= rulesToWait && eventBuffer.length>0) {
      println("send buffered data to output")
      eventBuffer.foreach(event => out.collect(event))
      eventBuffer.clear()
    }
  }
}

但不幸的是，它对我的情况一点帮助都没有，因为测试的主题是一个KeyedBroadcastProcessFunction，它使事件数据的延迟变得无关紧要，因为我试图应用一个平面图，使规则流n倍大，n是CPU的数量。所以我将确保生成的事件流将始终与规则流同步，并且将在那之后到达，但它也没有帮助。

毕竟，我找到了这个简单的解决方案，当然它不是确定性的，但由于并行性和并发性的性质，问题本身也不是确定性的。如果我们把delayMilis设置得足够大(

val delayMilis = 100
val synchronizedInput = inputEventStream.map(x=>{
      Thread.sleep(delayMilis)
      x
    }).keyBy(_.someKey)

您还可以将映射函数更改为this以仅在第一个元素上应用延迟：

package util

import org.apache.flink.api.common.functions.MapFunction

class DelayEvents[T](delayMilis: Int) extends MapFunction[T,T] {
  var delayed = false
  override def map(value: T): T = {
        if (!delayed) {
          delayed=true
          Thread.sleep(delayMilis)
        }
        value
  }
} 
val delayMilis = 100
val synchronizedInput = inputEventStream.map(new DelayEvents(100)).keyBy(_.someKey)

Answer 2

要做到这一点，没有简单的方法。您可以让processElement缓冲其所有输入，直到processBroadcastElement接收到模型为止。或者在没有事件流量的情况下运行一次作业，并在模型广播后获取一个保存点。然后将该保存点恢复到同一作业中，但其事件输入已连接。

顺便说一下，在Flink社区中，您正在寻找的功能通常被称为“辅助输入”。