当进程元素依赖于广播数据时，如何在 flink 中对广播进程函数进行单元测试

Question

我用 BroadcastProcessFunction 实现了一个 flink 流。从processBroadcastElement中，我得到了我的模型，并将其应用于processElement中的事件。

我没有找到对我的流进行单元测试的方法，因为我找不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
将模型推送到流中 2. 用模型填充广播状态，以便恢复流的执行

我可能错过了一些东西，但我还没有找到一个简单的方法来做到这一点。

这是我的问题的简单单元测试：

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")
  }
}

感谢大卫安德森
的更新我选择了缓冲溶液。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))
    }
  }
}

所以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

没有一个简单的方法可以做到这一点。你可以让processElement缓冲它的所有输入，直到processBroadcastElement接收到模型。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保存点。然后将该保存点还原到同一作业中，但已连接其事件输入。

顺便说一下，你正在寻找的能力在 Flink 社区中通常被称为"侧输入"。

Answer 2

感谢David Anderson和Matthieu，我编写了这个通用的CoFlatMap函数，该函数在事件流上发出请求的延迟：

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，它在我的情况下根本没有帮助，因为被测试的对象是一个KeyedBroadcastProcessFunction，这使得事件数据的延迟无关紧要，因此我试图应用一个平面图，使规则流大 n 倍，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，并将在此之后到达但这也无济于事。

毕竟，我得出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。如果我们设置的 delayMilis 足够大 (>100(，结果将是确定性的：

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此函数，以仅对第一个元素应用延迟：

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)