Question

这是一个简单的代码示例来说明我的问题：

case class Record( key: String, value: Int )

object Job extends App
{
  val env = StreamExecutionEnvironment.getExecutionEnvironment
  val data = env.fromElements( Record("01",1), Record("02",2), Record("03",3), Record("04",4), Record("05",5) )
  val step1 = data.filter( record => record.value % 3 != 0  ) // introduces some data loss
  val step2 = data.map( r => Record( r.key, r.value * 2 ) )
  val step3 = data.map( r => Record( r.key, r.value * 3 ) )
  val merged = step1.union( step2, step3 )
  val keyed = merged.keyBy(0)
  val windowed = keyed.countWindow( 3 )
  val summed = windowed.sum( 1 )
  summed.print()
  env.execute("test")
}

这会产生以下结果：

Record(01,6)
Record(02,12)
Record(04,24)
Record(05,30)

正如预期的那样，没有为键“03”产生结果，因为计数窗口需要3个元素，并且流中只有两个元素。

我想要的是某种具有超时的计数窗口，以便在一定的超时后，如果未达到计数窗口所期望的元素数量，则会使用现有元素生成部分结果。

有了这种行为，在我的例子中，当达到超时时会产生一个Record（03,15）。

Answer 1

您也可以使用自定义窗口Trigger执行此操作，该窗口在达到计数或超时到期时触发 - 有效地混合内置CountTrigger和EventTimeTrigger

Answer 2

我认为您可以使用 ProcessFunction

来实现此用例

您拥有计数属性和 windowEnd 属性。使用它可以决定何时收集数据。

public class TimeCountWindowProcessFunction extends ProcessFunction {

    protected long windowStart;
    protected long windowEnd;
    protected long count;
    private ValueState<CountPojo> state;

    public TimeCountWindowProcessFunction(long windowSize, long count) {

    this.windowSize = windowSize;
    this.count = count;

    }

@Override
    public void open(Configuration parameters) {

    TypeInformation<CountPojo> typeInformation = TypeInformation.of(new TypeHint<CountPojo>() {
    });
    ValueStateDescriptor<CountPojo> descriptor = new ValueStateDescriptor("test", typeInformation);

    state = getRuntimeContext().getState(descriptor);
}


    @Override
    public void processElement(CountPojo input, Context ctx, Collector<CountPojo> out)
            throws Exception {

    long timestamp = ctx.timestamp();
        windowStart = timestamp - (timestamp % windowSize);
        windowEnd = windowStart + windowSize;

        // retrieve the current count
        CountPojo current = (CountPojo) state.value();

        if (current == null) {

            current = new CountPojo();
        current.count = 1;

            ctx.timerService().registerEventTimeTimer(windowEnd);
        } else {

            current.count += 1;
        }

        if(current.count >= count) {
        out.collect(current);
    }

        // set the state's timestamp to the record's assigned event time timestamp
        current.setLastModified(ctx.timestamp());

        // write the state back
        state.update(current);
    }


    @Override
    public void onTimer(long timestamp, OnTimerContext ctx, Collector<CountPojo> out)
            throws Exception {


        if (windowEnd == timestamp) {

            out.collect(state.value());
        }

        state.clear();
    }
}

我希望这会对你有所帮助。

Answer 3

我遵循了David和NIrav的方法，结果如下。

1）使用自定义触发器：

这里我颠倒了我的初始逻辑。我没有使用“计数窗口”，而是使用一个“时间窗口”，其持续时间对应于超时，然后是一个触发器，当所有元素都被处理后触发。

case class Record( key: String, value: Int )

object Job extends App
{
  val env = StreamExecutionEnvironment.getExecutionEnvironment
  val data = env.fromElements( Record("01",1), Record("02",2), Record("03",3), Record("04",4), Record("05",5) )
  val step1 = data.filter( record => record.value % 3 != 0  ) // introduces some data loss
  val step2 = data.map( r => Record( r.key, r.value * 2 ) )
  val step3 = data.map( r => Record( r.key, r.value * 3 ) )
  val merged = step1.union( step2, step3 )
  val keyed = merged.keyBy(0)
  val windowed = keyed.timeWindow( Time.milliseconds( 50 ) )
  val triggered = windowed.trigger( new CountTriggerWithTimeout( 3, env.getStreamTimeCharacteristic ) )
  val summed = triggered.sum( 1 )
  summed.print()
  env.execute("test")
}

这是触发器代码：

import org.apache.flink.annotation.PublicEvolving
import org.apache.flink.api.common.functions.ReduceFunction
import org.apache.flink.api.common.functions.RuntimeContext
import org.apache.flink.api.common.state.ReducingState
import org.apache.flink.api.common.state.ReducingStateDescriptor
import org.apache.flink.api.common.typeutils.base.LongSerializer
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.windowing.triggers._
import org.apache.flink.streaming.api.windowing.triggers.Trigger.TriggerContext
import org.apache.flink.streaming.api.windowing.windows.TimeWindow

/**
 * A trigger that fires when the count of elements in a pane reaches the given count or a 
 * timeout is reached whatever happens first.
 */
class CountTriggerWithTimeout[W <: TimeWindow](maxCount: Long, timeCharacteristic: TimeCharacteristic) extends Trigger[Object,W] 
{
  private val countState: ReducingStateDescriptor[java.lang.Long] = new ReducingStateDescriptor[java.lang.Long]( "count", new Sum(), LongSerializer.INSTANCE)

  override def onElement(element: Object, timestamp: Long, window: W, ctx: TriggerContext): TriggerResult = 
  {
      val count: ReducingState[java.lang.Long] = ctx.getPartitionedState(countState)
      count.add( 1L )
      if ( count.get >= maxCount || timestamp >= window.getEnd ) TriggerResult.FIRE_AND_PURGE else TriggerResult.CONTINUE
  }

  override def onProcessingTime(time: Long, window: W, ctx: TriggerContext): TriggerResult = 
  {
      if (timeCharacteristic == TimeCharacteristic.EventTime) TriggerResult.CONTINUE else
      {
          if ( time >= window.getEnd ) TriggerResult.CONTINUE else TriggerResult.FIRE_AND_PURGE
      }
  }

  override def onEventTime(time: Long, window: W, ctx: TriggerContext): TriggerResult = 
  {
      if (timeCharacteristic == TimeCharacteristic.ProcessingTime) TriggerResult.CONTINUE else
      {
          if ( time >= window.getEnd ) TriggerResult.CONTINUE else TriggerResult.FIRE_AND_PURGE
      }
  }

  override def clear(window: W, ctx: TriggerContext): Unit = 
  {
          ctx.getPartitionedState( countState ).clear
    }

    class Sum extends ReduceFunction[java.lang.Long] 
  {
        def reduce(value1: java.lang.Long, value2: java.lang.Long): java.lang.Long = value1 + value2
  }
}

2）使用流程功能：

case class Record( key: String, value: Int )

object Job extends App
{
  val env = StreamExecutionEnvironment.getExecutionEnvironment
  env.setStreamTimeCharacteristic( TimeCharacteristic.IngestionTime )
  val data = env.fromElements( Record("01",1), Record("02",2), Record("03",3), Record("04",4), Record("05",5) )
  val step1 = data.filter( record => record.value % 3 != 0  ) // introduces some data loss
  val step2 = data.map( r => Record( r.key, r.value * 2 ) )
  val step3 = data.map( r => Record( r.key, r.value * 3 ) )
  val merged = step1.union( step2, step3 )
  val keyed = merged.keyBy(0)
  val processed = keyed.process( new TimeCountWindowProcessFunction( 3, 100 ) )
  processed.print()
  env.execute("test")
}

使用所有逻辑（即窗口化，触发和求和）进入函数：

import org.apache.flink.streaming.api.functions._
import org.apache.flink.util._
import org.apache.flink.api.common.state._

case class Status( count: Int, key: String, value: Long )

class TimeCountWindowProcessFunction( count: Long, windowSize: Long ) extends ProcessFunction[Record,Record] 
{
    lazy val state: ValueState[Status] = getRuntimeContext
      .getState(new ValueStateDescriptor[Status]("state", classOf[Status]))

    override def processElement( input: Record, ctx: ProcessFunction[Record,Record]#Context, out: Collector[Record] ): Unit =
    {
        val updated: Status = Option( state.value ) match {
            case None => {
                ctx.timerService().registerEventTimeTimer( ctx.timestamp + windowSize )
                Status( 1, input.key, input.value )
            }
            case Some( current ) => Status( current.count + 1, input.key, input.value + current.value )    
        }
        if ( updated.count == count ) 
        {
            out.collect( Record( input.key, updated.value ) )
            state.clear
        }
        else
        {
            state.update( updated )  
        }        
    }

    override def onTimer( timestamp: Long, ctx: ProcessFunction[Record,Record]#OnTimerContext, out: Collector[Record] ): Unit =
    {
        Option( state.value ) match {
            case None => // ignore
            case Some( status ) => {
                out.collect( Record( status.key, status.value ) )
                state.clear
            }
        }
    }
}

Apache Flink：计数窗口超时

3 个答案: