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")
}
这将产生以下结果:
Record(01,6)
Record(02,12)
Record(04,24)
Record(05,30)
正如预期的那样,键“03”不会产生任何结果,因为计数窗口需要3个元素,并且流中仅存在2个元素。
我想要的是一种带有超时的计数窗口,以便在一定的超时后,如果未达到计数窗口期望的元素数量,则现有元素会产生部分结果。
有了这种行为,在我的示例中,当达到超时时,将生成Record(03,15)。
最佳答案
我已经遵循了David和NIrav的方法,这是结果。
1)使用自定义触发器:
在这里,我颠倒了最初的逻辑。我没有使用“时间窗口”,而是使用了一个与超时相对应的持续时间的“时间窗口”,然后是在处理完所有元素后触发的触发器。
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)使用过程功能:
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")
}
在所有逻辑(即窗口化,触发和求和)进入函数后:
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
}
}
}
}