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/*
* Copyright (C) 2015-2020 Lightbend Inc.
*/
package akka.stream.scaladsl
import akka.NotUsed
import akka.event.Logging
import akka.pattern.BackoffSupervisor
import akka.stream.Attributes.Attribute
import akka.stream._
import akka.stream.impl.fusing.GraphStages.SimpleLinearGraphStage
import akka.stream.scaladsl.RestartWithBackoffFlow.Delay
import akka.stream.stage._
import scala.concurrent.duration._
import akka.stream.Attributes.LogLevels
import akka.util.OptionVal
/**
* A RestartFlow wraps a [[Flow]] that gets restarted when it completes or fails.
*
* They are useful for graphs that need to run for longer than the [[Flow]] can necessarily guarantee it will, for
* example, for [[Flow]] streams that depend on a remote server that may crash or become partitioned. The
* RestartFlow ensures that the graph can continue running while the [[Flow]] restarts.
*/
object RestartFlow {
/**
* Wrap the given [[Flow]] with a [[Flow]] that will restart it when it fails or complete using an exponential
* backoff.
*
* This [[Flow]] will not cancel, complete or emit a failure, until the opposite end of it has been cancelled or
* completed. Any termination by the [[Flow]] before that time will be handled by restarting it. Any termination
* signals sent to this [[Flow]] however will terminate the wrapped [[Flow]], if it's running, and then the [[Flow]]
* will be allowed to terminate without being restarted.
*
* The restart process is inherently lossy, since there is no coordination between cancelling and the sending of
* messages. A termination signal from either end of the wrapped [[Flow]] will cause the other end to be terminated,
* and any in transit messages will be lost. During backoff, this [[Flow]] will backpressure.
*
* This uses the same exponential backoff algorithm as [[akka.pattern.Backoff]].
*
* @param minBackoff minimum (initial) duration until the child actor will
* started again, if it is terminated
* @param maxBackoff the exponential back-off is capped to this duration
* @param randomFactor after calculation of the exponential back-off an additional
* random delay based on this factor is added, e.g. `0.2` adds up to `20%` delay.
* In order to skip this additional delay pass in `0`.
* @param flowFactory A factory for producing the [[Flow]] to wrap.
*/
def withBackoff[In, Out](minBackoff: FiniteDuration, maxBackoff: FiniteDuration, randomFactor: Double)(
flowFactory: () => Flow[In, Out, _]): Flow[In, Out, NotUsed] = {
Flow.fromGraph(
new RestartWithBackoffFlow(
flowFactory,
minBackoff,
maxBackoff,
randomFactor,
onlyOnFailures = false,
Int.MaxValue))
}
/**
* Wrap the given [[Flow]] with a [[Flow]] that will restart it when it fails or complete using an exponential
* backoff.
*
* This [[Flow]] will not cancel, complete or emit a failure, until the opposite end of it has been cancelled or
* completed. Any termination by the [[Flow]] before that time will be handled by restarting it as long as maxRestarts
* is not reached. Any termination signals sent to this [[Flow]] however will terminate the wrapped [[Flow]], if it's
* running, and then the [[Flow]] will be allowed to terminate without being restarted.
*
* The restart process is inherently lossy, since there is no coordination between cancelling and the sending of
* messages. A termination signal from either end of the wrapped [[Flow]] will cause the other end to be terminated,
* and any in transit messages will be lost. During backoff, this [[Flow]] will backpressure.
*
* This uses the same exponential backoff algorithm as [[akka.pattern.Backoff]].
*
* @param minBackoff minimum (initial) duration until the child actor will
* started again, if it is terminated
* @param maxBackoff the exponential back-off is capped to this duration
* @param randomFactor after calculation of the exponential back-off an additional
* random delay based on this factor is added, e.g. `0.2` adds up to `20%` delay.
* In order to skip this additional delay pass in `0`.
* @param maxRestarts the amount of restarts is capped to this amount within a time frame of minBackoff.
* Passing `0` will cause no restarts and a negative number will not cap the amount of restarts.
* @param flowFactory A factory for producing the [[Flow]] to wrap.
*/
def withBackoff[In, Out](
minBackoff: FiniteDuration,
maxBackoff: FiniteDuration,
randomFactor: Double,
maxRestarts: Int)(flowFactory: () => Flow[In, Out, _]): Flow[In, Out, NotUsed] = {
Flow.fromGraph(
new RestartWithBackoffFlow(
flowFactory,
minBackoff,
maxBackoff,
randomFactor,
onlyOnFailures = false,
maxRestarts))
}
/**
* Wrap the given [[Flow]] with a [[Flow]] that will restart it when it fails using an exponential
* backoff. Notice that this [[Flow]] will not restart on completion of the wrapped flow.
*
* This [[Flow]] will not emit any failure
* The failures by the wrapped [[Flow]] will be handled by
* restarting the wrapping [[Flow]] as long as maxRestarts is not reached.
* Any termination signals sent to this [[Flow]] however will terminate the wrapped [[Flow]], if it's
* running, and then the [[Flow]] will be allowed to terminate without being restarted.
*
* The restart process is inherently lossy, since there is no coordination between cancelling and the sending of
* messages. A termination signal from either end of the wrapped [[Flow]] will cause the other end to be terminated,
* and any in transit messages will be lost. During backoff, this [[Flow]] will backpressure.
*
* This uses the same exponential backoff algorithm as [[akka.pattern.Backoff]].
*
* @param minBackoff minimum (initial) duration until the child actor will
* started again, if it is terminated
* @param maxBackoff the exponential back-off is capped to this duration
* @param randomFactor after calculation of the exponential back-off an additional
* random delay based on this factor is added, e.g. `0.2` adds up to `20%` delay.
* In order to skip this additional delay pass in `0`.
* @param maxRestarts the amount of restarts is capped to this amount within a time frame of minBackoff.
* Passing `0` will cause no restarts and a negative number will not cap the amount of restarts.
* @param flowFactory A factory for producing the [[Flow]] to wrap.
*/
def onFailuresWithBackoff[In, Out](
minBackoff: FiniteDuration,
maxBackoff: FiniteDuration,
randomFactor: Double,
maxRestarts: Int)(flowFactory: () => Flow[In, Out, _]): Flow[In, Out, NotUsed] = {
Flow.fromGraph(
new RestartWithBackoffFlow(flowFactory, minBackoff, maxBackoff, randomFactor, onlyOnFailures = true, maxRestarts))
}
}
private final class RestartWithBackoffFlow[In, Out](
flowFactory: () => Flow[In, Out, _],
minBackoff: FiniteDuration,
maxBackoff: FiniteDuration,
randomFactor: Double,
onlyOnFailures: Boolean,
maxRestarts: Int)
extends GraphStage[FlowShape[In, Out]] { self =>
val in = Inlet[In]("RestartWithBackoffFlow.in")
val out = Outlet[Out]("RestartWithBackoffFlow.out")
override def shape = FlowShape(in, out)
override def createLogic(inheritedAttributes: Attributes) =
new RestartWithBackoffLogic(
"Flow",
shape,
inheritedAttributes,
minBackoff,
maxBackoff,
randomFactor,
onlyOnFailures,
maxRestarts) {
val delay = inheritedAttributes.get[Delay](Delay(50.millis)).duration
var activeOutIn: Option[(SubSourceOutlet[In], SubSinkInlet[Out])] = None
override protected def logSource = self.getClass
override protected def startGraph() = {
val sourceOut: SubSourceOutlet[In] = createSubOutlet(in)
val sinkIn: SubSinkInlet[Out] = createSubInlet(out)
Source
.fromGraph(sourceOut.source)
// Temp fix while waiting cause of cancellation. See #23909
.via(RestartWithBackoffFlow.delayCancellation[In](delay))
.via(flowFactory())
.runWith(sinkIn.sink)(subFusingMaterializer)
if (isAvailable(out)) {
sinkIn.pull()
}
activeOutIn = Some((sourceOut, sinkIn))
}
override protected def backoff() = {
setHandler(in, new InHandler {
override def onPush() = ()
})
setHandler(out, new OutHandler {
override def onPull() = ()
})
// We need to ensure that the other end of the sub flow is also completed, so that we don't
// receive any callbacks from it.
activeOutIn.foreach {
case (sourceOut, sinkIn) =>
if (!sourceOut.isClosed) {
sourceOut.complete()
}
if (!sinkIn.isClosed) {
sinkIn.cancel()
}
activeOutIn = None
}
}
backoff()
}
}
/**
* Shared logic for all restart with backoff logics.
*/
private abstract class RestartWithBackoffLogic[S <: Shape](
name: String,
shape: S,
inheritedAttributes: Attributes,
minBackoff: FiniteDuration,
maxBackoff: FiniteDuration,
randomFactor: Double,
onlyOnFailures: Boolean,
maxRestarts: Int)
extends TimerGraphStageLogicWithLogging(shape) {
var restartCount = 0
var resetDeadline = minBackoff.fromNow
// This is effectively only used for flows, if either the main inlet or outlet of this stage finishes, then we
// don't want to restart the sub inlet when it finishes, we just finish normally.
var finishing = false
override protected def logSource: Class[_] = classOf[RestartWithBackoffLogic[_]]
protected def startGraph(): Unit
protected def backoff(): Unit
private def loggingEnabled = inheritedAttributes.get[LogLevels] match {
case Some(levels) => levels.onFailure != LogLevels.Off
case None => true
}
/**
* @param out The permanent outlet
* @return A sub sink inlet that's sink is attached to the wrapped operator
*/
protected final def createSubInlet[T](out: Outlet[T]): SubSinkInlet[T] = {
val sinkIn = new SubSinkInlet[T](s"RestartWithBackoff$name.subIn")
sinkIn.setHandler(new InHandler {
override def onPush() = push(out, sinkIn.grab())
override def onUpstreamFinish() = {
if (finishing || maxRestartsReached() || onlyOnFailures) {
complete(out)
} else {
scheduleRestartTimer()
}
}
/*
* Upstream in this context is the wrapped stage.
*/
override def onUpstreamFailure(ex: Throwable) = {
if (finishing || maxRestartsReached()) {
fail(out, ex)
} else {
if (loggingEnabled)
log.warning("Restarting graph due to failure. stack_trace: {}", Logging.stackTraceFor(ex))
scheduleRestartTimer()
}
}
})
setHandler(out, new OutHandler {
override def onPull() = sinkIn.pull()
override def onDownstreamFinish(cause: Throwable) = {
finishing = true
sinkIn.cancel(cause)
}
})
sinkIn
}
/**
* @param in The permanent inlet for this operator
* @return Temporary SubSourceOutlet for this "restart"
*/
protected final def createSubOutlet[T](in: Inlet[T]): SubSourceOutlet[T] = {
val sourceOut = new SubSourceOutlet[T](s"RestartWithBackoff$name.subOut")
sourceOut.setHandler(new OutHandler {
override def onPull() =
if (isAvailable(in)) {
sourceOut.push(grab(in))
} else {
if (!hasBeenPulled(in)) {
pull(in)
}
}
/*
* Downstream in this context is the wrapped stage.
*
* Can either be a failure or a cancel in the wrapped state.
* onlyOnFailures is thus racy so a delay to cancellation is added in the case of a flow.
*/
override def onDownstreamFinish(cause: Throwable) = {
if (finishing || maxRestartsReached() || onlyOnFailures) {
cancel(in, cause)
} else {
scheduleRestartTimer()
}
}
})
setHandler(
in,
new InHandler {
override def onPush() = if (sourceOut.isAvailable) {
sourceOut.push(grab(in))
}
override def onUpstreamFinish() = {
finishing = true
sourceOut.complete()
}
override def onUpstreamFailure(ex: Throwable) = {
finishing = true
sourceOut.fail(ex)
}
})
sourceOut
}
protected final def maxRestartsReached(): Boolean = {
// Check if the last start attempt was more than the minimum backoff
if (resetDeadline.isOverdue()) {
log.debug("Last restart attempt was more than {} ago, resetting restart count", minBackoff)
restartCount = 0
}
restartCount == maxRestarts
}
// Set a timer to restart after the calculated delay
protected final def scheduleRestartTimer(): Unit = {
val restartDelay = BackoffSupervisor.calculateDelay(restartCount, minBackoff, maxBackoff, randomFactor)
log.debug("Restarting graph in {}", restartDelay)
scheduleOnce("RestartTimer", restartDelay)
restartCount += 1
// And while we wait, we go into backoff mode
backoff()
}
// Invoked when the backoff timer ticks
override protected def onTimer(timerKey: Any) = {
startGraph()
resetDeadline = minBackoff.fromNow
}
// When the stage starts, start the source
override def preStart() = startGraph()
}
object RestartWithBackoffFlow {
/**
* Temporary attribute that can override the time a [[RestartWithBackoffFlow]] waits
* for a failure before cancelling.
*
* See https://github.com/akka/akka/issues/24529
*
* Will be removed if/when cancellation can include a cause.
*/
case class Delay(duration: FiniteDuration) extends Attribute
/**
* Returns a flow that is almost identity but delays propagation of cancellation from downstream to upstream.
*
* Once the down stream is finish calls to onPush are ignored.
*/
private def delayCancellation[T](duration: FiniteDuration): Flow[T, T, NotUsed] =
Flow.fromGraph(new DelayCancellationStage(duration))
private final class DelayCancellationStage[T](delay: FiniteDuration) extends SimpleLinearGraphStage[T] {
override def createLogic(inheritedAttributes: Attributes): GraphStageLogic =
new TimerGraphStageLogic(shape) with InHandler with OutHandler with StageLogging {
override protected def logSource: Class[_] = classOf[DelayCancellationStage[_]]
private var cause: OptionVal[Throwable] = OptionVal.None
setHandlers(in, out, this)
def onPush(): Unit = push(out, grab(in))
def onPull(): Unit = pull(in)
override def onDownstreamFinish(cause: Throwable): Unit = {
this.cause = OptionVal.Some(cause)
scheduleOnce("CompleteState", delay)
setHandler(in, new InHandler {
def onPush(): Unit = {}
})
}
override protected def onTimer(timerKey: Any): Unit = {
log.debug(s"Stage was canceled after delay of $delay")
cause match {
case OptionVal.Some(ex) =>
cancelStage(ex)
case OptionVal.None =>
throw new IllegalStateException("Timer hitting without first getting a cancel cannot happen")
}
}
}
}
}
