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/*
* Copyright (C) 2014-2020 Lightbend Inc.
*/
package akka.stream.scaladsl
import java.util.concurrent.CompletionStage
import akka.actor.{ ActorRef, Cancellable }
import akka.annotation.InternalApi
import akka.stream.impl.Stages.DefaultAttributes
import akka.stream.impl.fusing.GraphStages
import akka.stream.impl.fusing.GraphStages._
import akka.stream.impl.{ PublisherSource, _ }
import akka.stream.{ Outlet, SourceShape, _ }
import akka.util.ConstantFun
import akka.{ Done, NotUsed }
import org.reactivestreams.{ Publisher, Subscriber }
import scala.annotation.tailrec
import scala.annotation.unchecked.uncheckedVariance
import scala.collection.immutable
import scala.concurrent.duration.FiniteDuration
import scala.concurrent.{ Future, Promise }
import akka.stream.stage.GraphStageWithMaterializedValue
import scala.compat.java8.FutureConverters._
/**
* A `Source` is a set of stream processing steps that has one open output. It can comprise
* any number of internal sources and transformations that are wired together, or it can be
* an “atomic” source, e.g. from a collection or a file. Materialization turns a Source into
* a Reactive Streams `Publisher` (at least conceptually).
*/
final class Source[+Out, +Mat](
override val traversalBuilder: LinearTraversalBuilder,
override val shape: SourceShape[Out])
extends FlowOpsMat[Out, Mat]
with Graph[SourceShape[Out], Mat] {
override type Repr[+O] = Source[O, Mat @uncheckedVariance]
override type ReprMat[+O, +M] = Source[O, M]
override type Closed = RunnableGraph[Mat @uncheckedVariance]
override type ClosedMat[+M] = RunnableGraph[M]
override def toString: String = s"Source($shape)"
override def via[T, Mat2](flow: Graph[FlowShape[Out, T], Mat2]): Repr[T] = viaMat(flow)(Keep.left)
override def viaMat[T, Mat2, Mat3](flow: Graph[FlowShape[Out, T], Mat2])(
combine: (Mat, Mat2) => Mat3): Source[T, Mat3] = {
if (flow.traversalBuilder eq Flow.identityTraversalBuilder)
if (combine == Keep.left)
//optimization by returning this
this.asInstanceOf[Source[T, Mat3]] //Mat == Mat3, due to Keep.left
else if (combine == Keep.right || combine == Keep.none) // Mat3 = NotUsed
//optimization with LinearTraversalBuilder.empty()
new Source[T, Mat3](
traversalBuilder.append(LinearTraversalBuilder.empty(), flow.shape, combine),
SourceShape(shape.out).asInstanceOf[SourceShape[T]])
else
new Source[T, Mat3](
traversalBuilder.append(flow.traversalBuilder, flow.shape, combine),
SourceShape(flow.shape.out))
else
new Source[T, Mat3](
traversalBuilder.append(flow.traversalBuilder, flow.shape, combine),
SourceShape(flow.shape.out))
}
/**
* Connect this [[akka.stream.scaladsl.Source]] to a [[akka.stream.scaladsl.Sink]],
* concatenating the processing steps of both.
*/
def to[Mat2](sink: Graph[SinkShape[Out], Mat2]): RunnableGraph[Mat] = toMat(sink)(Keep.left)
/**
* Connect this [[akka.stream.scaladsl.Source]] to a [[akka.stream.scaladsl.Sink]],
* concatenating the processing steps of both.
*/
def toMat[Mat2, Mat3](sink: Graph[SinkShape[Out], Mat2])(combine: (Mat, Mat2) => Mat3): RunnableGraph[Mat3] = {
RunnableGraph(traversalBuilder.append(sink.traversalBuilder, sink.shape, combine))
}
/**
* Transform only the materialized value of this Source, leaving all other properties as they were.
*/
override def mapMaterializedValue[Mat2](f: Mat => Mat2): ReprMat[Out, Mat2] =
new Source[Out, Mat2](traversalBuilder.transformMat(f.asInstanceOf[Any => Any]), shape)
/**
* Materializes this Source, immediately returning (1) its materialized value, and (2) a new Source
* that can be used to consume elements from the newly materialized Source.
*/
def preMaterialize()(implicit materializer: Materializer): (Mat, ReprMat[Out, NotUsed]) = {
val (mat, pub) = toMat(Sink.asPublisher(fanout = true))(Keep.both).run()
(mat, Source.fromPublisher(pub))
}
/**
* Connect this `Source` to a `Sink` and run it. The returned value is the materialized value
* of the `Sink`, e.g. the `Publisher` of a [[akka.stream.scaladsl.Sink#publisher]].
*
* Note that the `ActorSystem` can be used as the implicit `materializer` parameter to use the
* [[akka.stream.SystemMaterializer]] for running the stream.
*/
def runWith[Mat2](sink: Graph[SinkShape[Out], Mat2])(implicit materializer: Materializer): Mat2 =
toMat(sink)(Keep.right).run()
/**
* Shortcut for running this `Source` with a fold function.
* The given function is invoked for every received element, giving it its previous
* output (or the given `zero` value) and the element as input.
* The returned [[scala.concurrent.Future]] will be completed with value of the final
* function evaluation when the input stream ends, or completed with `Failure`
* if there is a failure signaled in the stream.
*
* Note that the `ActorSystem` can be used as the implicit `materializer` parameter to use the
* [[akka.stream.SystemMaterializer]] for running the stream.
*/
def runFold[U](zero: U)(f: (U, Out) => U)(implicit materializer: Materializer): Future[U] =
runWith(Sink.fold(zero)(f))
/**
* Shortcut for running this `Source` with a foldAsync function.
* The given function is invoked for every received element, giving it its previous
* output (or the given `zero` value) and the element as input.
* The returned [[scala.concurrent.Future]] will be completed with value of the final
* function evaluation when the input stream ends, or completed with `Failure`
* if there is a failure signaled in the stream.
*
* Note that the `ActorSystem` can be used as the implicit `materializer` parameter to use the
* [[akka.stream.SystemMaterializer]] for running the stream.
*/
def runFoldAsync[U](zero: U)(f: (U, Out) => Future[U])(implicit materializer: Materializer): Future[U] =
runWith(Sink.foldAsync(zero)(f))
/**
* Shortcut for running this `Source` with a reduce function.
* The given function is invoked for every received element, giving it its previous
* output (from the second element) and the element as input.
* The returned [[scala.concurrent.Future]] will be completed with value of the final
* function evaluation when the input stream ends, or completed with `Failure`
* if there is a failure signaled in the stream.
*
* If the stream is empty (i.e. completes before signalling any elements),
* the reduce operator will fail its downstream with a [[NoSuchElementException]],
* which is semantically in-line with that Scala's standard library collections
* do in such situations.
*
* Note that the `ActorSystem` can be used as the implicit `materializer` parameter to use the
* [[akka.stream.SystemMaterializer]] for running the stream.
*/
def runReduce[U >: Out](f: (U, U) => U)(implicit materializer: Materializer): Future[U] =
runWith(Sink.reduce(f))
/**
* Shortcut for running this `Source` with a foreach procedure. The given procedure is invoked
* for each received element.
* The returned [[scala.concurrent.Future]] will be completed with `Success` when reaching the
* normal end of the stream, or completed with `Failure` if there is a failure signaled in
* the stream.
*
* Note that the `ActorSystem` can be used as the implicit `materializer` parameter to use the
* [[akka.stream.SystemMaterializer]] for running the stream.
*/
// FIXME: Out => Unit should stay, right??
def runForeach(f: Out => Unit)(implicit materializer: Materializer): Future[Done] = runWith(Sink.foreach(f))
/**
* Replace the attributes of this [[Source]] with the given ones. If this Source is a composite
* of multiple graphs, new attributes on the composite will be less specific than attributes
* set directly on the individual graphs of the composite.
*/
override def withAttributes(attr: Attributes): Repr[Out] =
new Source(traversalBuilder.setAttributes(attr), shape)
/**
* Add the given attributes to this Source. If the specific attribute was already on this source
* it will replace the previous value. If this Source is a composite
* of multiple graphs, the added attributes will be on the composite and therefore less specific than attributes
* set directly on the individual graphs of the composite.
*/
override def addAttributes(attr: Attributes): Repr[Out] = withAttributes(traversalBuilder.attributes and attr)
/**
* Add a ``name`` attribute to this Source.
*/
override def named(name: String): Repr[Out] = addAttributes(Attributes.name(name))
/**
* Put an asynchronous boundary around this `Source`
*/
override def async: Repr[Out] = super.async.asInstanceOf[Repr[Out]]
/**
* Put an asynchronous boundary around this `Graph`
*
* @param dispatcher Run the graph on this dispatcher
*/
override def async(dispatcher: String): Repr[Out] =
super.async(dispatcher).asInstanceOf[Repr[Out]]
/**
* Put an asynchronous boundary around this `Graph`
*
* @param dispatcher Run the graph on this dispatcher
* @param inputBufferSize Set the input buffer to this size for the graph
*/
override def async(dispatcher: String, inputBufferSize: Int): Repr[Out] =
super.async(dispatcher, inputBufferSize).asInstanceOf[Repr[Out]]
/**
* Converts this Scala DSL element to it's Java DSL counterpart.
*/
def asJava: javadsl.Source[Out @uncheckedVariance, Mat @uncheckedVariance] = new javadsl.Source(this)
/**
* Combines several sources with fan-in strategy like `Merge` or `Concat` and returns `Source`.
*/
@deprecated("Use `Source.combine` on companion object instead", "2.5.5")
def combine[T, U](first: Source[T, _], second: Source[T, _], rest: Source[T, _]*)(
strategy: Int => Graph[UniformFanInShape[T, U], NotUsed]): Source[U, NotUsed] =
Source.fromGraph(GraphDSL.create() { implicit b =>
import GraphDSL.Implicits._
val c = b.add(strategy(rest.size + 2))
first ~> c.in(0)
second ~> c.in(1)
@tailrec def combineRest(idx: Int, i: Iterator[Source[T, _]]): SourceShape[U] =
if (i.hasNext) {
i.next() ~> c.in(idx)
combineRest(idx + 1, i)
} else SourceShape(c.out)
combineRest(2, rest.iterator)
})
def asSourceWithContext[Ctx](f: Out => Ctx): SourceWithContext[Out, Ctx, Mat] =
new SourceWithContext(this.map(e => (e, f(e))))
}
object Source {
/** INTERNAL API */
def shape[T](name: String): SourceShape[T] = SourceShape(Outlet(name + ".out"))
/**
* Helper to create [[Source]] from `Publisher`.
*
* Construct a transformation starting with given publisher. The transformation steps
* are executed by a series of [[org.reactivestreams.Processor]] instances
* that mediate the flow of elements downstream and the propagation of
* back-pressure upstream.
*/
def fromPublisher[T](publisher: Publisher[T]): Source[T, NotUsed] =
fromGraph(new PublisherSource(publisher, DefaultAttributes.publisherSource, shape("PublisherSource")))
/**
* Helper to create [[Source]] from `Iterator`.
* Example usage: `Source.fromIterator(() => Iterator.from(0))`
*
* Start a new `Source` from the given function that produces anIterator.
* The produced stream of elements will continue until the iterator runs empty
* or fails during evaluation of the `next()` method.
* Elements are pulled out of the iterator in accordance with the demand coming
* from the downstream transformation steps.
*/
def fromIterator[T](f: () => Iterator[T]): Source[T, NotUsed] =
apply(new immutable.Iterable[T] {
override def iterator: Iterator[T] = f()
override def toString: String = "() => Iterator"
})
/**
* Creates [[Source]] that will continually produce given elements in specified order.
*
* Starts a new 'cycled' `Source` from the given elements. The producer stream of elements
* will continue infinitely by repeating the sequence of elements provided by function parameter.
*/
def cycle[T](f: () => Iterator[T]): Source[T, NotUsed] = {
val iterator = Iterator.continually {
val i = f(); if (i.isEmpty) throw new IllegalArgumentException("empty iterator") else i
}.flatten
fromIterator(() => iterator).withAttributes(DefaultAttributes.cycledSource)
}
/**
* A graph with the shape of a source logically is a source, this method makes
* it so also in type.
*/
def fromGraph[T, M](g: Graph[SourceShape[T], M]): Source[T, M] = g match {
case s: Source[T, M] => s
case s: javadsl.Source[T, M] => s.asScala
case g: GraphStageWithMaterializedValue[SourceShape[T], M] =>
// move these from the stage itself to make the returned source
// behave as it is the stage with regards to attributes
val attrs = g.traversalBuilder.attributes
val noAttrStage = g.withAttributes(Attributes.none)
new Source(
LinearTraversalBuilder.fromBuilder(noAttrStage.traversalBuilder, noAttrStage.shape, Keep.right),
noAttrStage.shape).withAttributes(attrs)
case other =>
// composite source shaped graph
new Source(LinearTraversalBuilder.fromBuilder(other.traversalBuilder, other.shape, Keep.right), other.shape)
}
/**
* Defers the creation of a [[Source]] until materialization. The `factory` function
* exposes [[Materializer]] which is going to be used during materialization and
* [[Attributes]] of the [[Source]] returned by this method.
*/
def fromMaterializer[T, M](factory: (Materializer, Attributes) => Source[T, M]): Source[T, Future[M]] =
Source.fromGraph(new SetupSourceStage(factory))
/**
* Defers the creation of a [[Source]] until materialization. The `factory` function
* exposes [[ActorMaterializer]] which is going to be used during materialization and
* [[Attributes]] of the [[Source]] returned by this method.
*/
@deprecated("Use 'fromMaterializer' instead", "2.6.0")
def setup[T, M](factory: (ActorMaterializer, Attributes) => Source[T, M]): Source[T, Future[M]] =
Source.fromGraph(new SetupSourceStage((materializer, attributes) =>
factory(ActorMaterializerHelper.downcast(materializer), attributes)))
/**
* Helper to create [[Source]] from `Iterable`.
* Example usage: `Source(Seq(1,2,3))`
*
* Starts a new `Source` from the given `Iterable`. This is like starting from an
* Iterator, but every Subscriber directly attached to the Publisher of this
* stream will see an individual flow of elements (always starting from the
* beginning) regardless of when they subscribed.
*/
def apply[T](iterable: immutable.Iterable[T]): Source[T, NotUsed] =
single(iterable).mapConcat(ConstantFun.scalaIdentityFunction).withAttributes(DefaultAttributes.iterableSource)
/**
* Starts a new `Source` from the given `Future`. The stream will consist of
* one element when the `Future` is completed with a successful value, which
* may happen before or after materializing the `Flow`.
* The stream terminates with a failure if the `Future` is completed with a failure.
*/
@deprecated("Use 'Source.future' instead", "2.6.0")
def fromFuture[T](future: Future[T]): Source[T, NotUsed] =
fromGraph(new FutureSource(future))
/**
* Starts a new `Source` from the given `Future`. The stream will consist of
* one element when the `Future` is completed with a successful value, which
* may happen before or after materializing the `Flow`.
* The stream terminates with a failure if the `Future` is completed with a failure.
*/
@deprecated("Use 'Source.completionStage' instead", "2.6.0")
def fromCompletionStage[T](future: CompletionStage[T]): Source[T, NotUsed] =
fromGraph(new FutureSource(future.toScala))
/**
* Streams the elements of the given future source once it successfully completes.
* If the [[Future]] fails the stream is failed with the exception from the future. If downstream cancels before the
* stream completes the materialized `Future` will be failed with a [[StreamDetachedException]]
*/
@deprecated("Use 'Source.futureSource' (potentially together with `Source.fromGraph`) instead", "2.6.0")
def fromFutureSource[T, M](future: Future[Graph[SourceShape[T], M]]): Source[T, Future[M]] =
fromGraph(new FutureFlattenSource(future))
/**
* Streams the elements of an asynchronous source once its given `completion` operator completes.
* If the [[CompletionStage]] fails the stream is failed with the exception from the future.
* If downstream cancels before the stream completes the materialized `Future` will be failed
* with a [[StreamDetachedException]]
*/
@deprecated("Use scala-compat CompletionStage to future converter and 'Source.futureSource' instead", "2.6.0")
def fromSourceCompletionStage[T, M](
completion: CompletionStage[_ <: Graph[SourceShape[T], M]]): Source[T, CompletionStage[M]] =
fromFutureSource(completion.toScala).mapMaterializedValue(_.toJava)
/**
* Elements are emitted periodically with the specified interval.
* The tick element will be delivered to downstream consumers that has requested any elements.
* If a consumer has not requested any elements at the point in time when the tick
* element is produced it will not receive that tick element later. It will
* receive new tick elements as soon as it has requested more elements.
*/
def tick[T](initialDelay: FiniteDuration, interval: FiniteDuration, tick: T): Source[T, Cancellable] =
fromGraph(new TickSource[T](initialDelay, interval, tick))
/**
* Create a `Source` with one element.
* Every connected `Sink` of this stream will see an individual stream consisting of one element.
*/
def single[T](element: T): Source[T, NotUsed] =
fromGraph(new GraphStages.SingleSource(element))
/**
* Create a `Source` that will continually emit the given element.
*/
def repeat[T](element: T): Source[T, NotUsed] = {
val next = Some((element, element))
unfold(element)(_ => next).withAttributes(DefaultAttributes.repeat)
}
/**
* Create a `Source` that will unfold a value of type `S` into
* a pair of the next state `S` and output elements of type `E`.
*
* For example, all the Fibonacci numbers under 10M:
*
* {{{
* Source.unfold(0 -> 1) {
* case (a, _) if a > 10000000 => None
* case (a, b) => Some((b -> (a + b)) -> a)
* }
* }}}
*/
def unfold[S, E](s: S)(f: S => Option[(S, E)]): Source[E, NotUsed] =
Source.fromGraph(new Unfold(s, f))
/**
* Same as [[unfold]], but uses an async function to generate the next state-element tuple.
*
* async fibonacci example:
*
* {{{
* Source.unfoldAsync(0 -> 1) {
* case (a, _) if a > 10000000 => Future.successful(None)
* case (a, b) => Future{
* Thread.sleep(1000)
* Some((b -> (a + b)) -> a)
* }
* }
* }}}
*/
def unfoldAsync[S, E](s: S)(f: S => Future[Option[(S, E)]]): Source[E, NotUsed] =
Source.fromGraph(new UnfoldAsync(s, f))
/**
* A `Source` with no elements, i.e. an empty stream that is completed immediately for every connected `Sink`.
*/
def empty[T]: Source[T, NotUsed] = _empty
private[this] val _empty: Source[Nothing, NotUsed] =
Source.fromGraph(EmptySource)
/**
* Create a `Source` which materializes a [[scala.concurrent.Promise]] which controls what element
* will be emitted by the Source.
* If the materialized promise is completed with a Some, that value will be produced downstream,
* followed by completion.
* If the materialized promise is completed with a None, no value will be produced downstream and completion will
* be signalled immediately.
* If the materialized promise is completed with a failure, then the source will fail with that error.
* If the downstream of this source cancels or fails before the promise has been completed, then the promise will be completed
* with None.
*/
def maybe[T]: Source[T, Promise[Option[T]]] =
Source.fromGraph(MaybeSource.asInstanceOf[Graph[SourceShape[T], Promise[Option[T]]]])
/**
* Create a `Source` that immediately ends the stream with the `cause` error to every connected `Sink`.
*/
def failed[T](cause: Throwable): Source[T, NotUsed] =
Source.fromGraph(new FailedSource[T](cause))
/**
* Creates a `Source` that is not materialized until there is downstream demand, when the source gets materialized
* the materialized future is completed with its value, if downstream cancels or fails without any demand the
* create factory is never called and the materialized `Future` is failed.
*/
@deprecated("Use 'Source.lazySource' instead", "2.6.0")
def lazily[T, M](create: () => Source[T, M]): Source[T, Future[M]] =
Source.fromGraph(new LazySource[T, M](create))
/**
* Creates a `Source` from supplied future factory that is not called until downstream demand. When source gets
* materialized the materialized future is completed with the value from the factory. If downstream cancels or fails
* without any demand the create factory is never called and the materialized `Future` is failed.
*
* @see [[Source.lazily]]
*/
@deprecated("Use 'Source.lazyFuture' instead", "2.6.0")
def lazilyAsync[T](create: () => Future[T]): Source[T, Future[NotUsed]] =
lazily(() => fromFuture(create()))
/**
* Emits a single value when the given `Future` is successfully completed and then completes the stream.
* The stream fails if the `Future` is completed with a failure.
*/
def future[T](futureElement: Future[T]): Source[T, NotUsed] =
fromGraph(new FutureSource[T](futureElement))
/**
* Emits a single value when the given `CompletionStage` is successfully completed and then completes the stream.
* If the `CompletionStage` is completed with a failure the stream is failed.
*
* Here for Java interoperability, the normal use from Scala should be [[Source.future]]
*/
def completionStage[T](completionStage: CompletionStage[T]): Source[T, NotUsed] =
future(completionStage.toScala)
/**
* Turn a `Future[Source]` into a source that will emit the values of the source when the future completes successfully.
* If the `Future` is completed with a failure the stream is failed.
*/
def futureSource[T, M](futureSource: Future[Source[T, M]]): Source[T, Future[M]] =
fromGraph(new FutureFlattenSource(futureSource))
/**
* Defers invoking the `create` function to create a single element until there is downstream demand.
*
* If the `create` function fails when invoked the stream is failed.
*
* Note that asynchronous boundaries (and other operators) in the stream may do pre-fetching which counter acts
* the laziness and will trigger the factory immediately.
*/
def lazySingle[T](create: () => T): Source[T, NotUsed] =
lazySource(() => single(create())).mapMaterializedValue(_ => NotUsed)
/**
* Defers invoking the `create` function to create a future element until there is downstream demand.
*
* The returned future element will be emitted downstream when it completes, or fail the stream if the future
* is failed or the `create` function itself fails.
*
* Note that asynchronous boundaries (and other operators) in the stream may do pre-fetching which counter acts
* the laziness and will trigger the factory immediately.
*/
def lazyFuture[T](create: () => Future[T]): Source[T, NotUsed] =
lazySource { () =>
val f = create()
future(f)
}.mapMaterializedValue(_ => NotUsed)
/**
* Defers invoking the `create` function to create a future source until there is downstream demand.
*
* The returned source will emit downstream and behave just like it was the outer source. Downstream completes
* when the created source completes and fails when the created source fails.
*
* Note that asynchronous boundaries (and other operators) in the stream may do pre-fetching which counter acts
* the laziness and will trigger the factory immediately.
*
* The materialized future value is completed with the materialized value of the created source when
* it has been materialized. If the function throws or the source materialization fails the future materialized value
* is failed with the thrown exception.
*
* If downstream cancels or fails before the function is invoked the materialized value
* is failed with a [[akka.stream.NeverMaterializedException]]
*/
def lazySource[T, M](create: () => Source[T, M]): Source[T, Future[M]] =
fromGraph(new LazySource(create))
/**
* Defers invoking the `create` function to create a future source until there is downstream demand.
*
* The returned future source will emit downstream and behave just like it was the outer source when the future completes
* successfully. Downstream completes when the created source completes and fails when the created source fails.
* If the future or the `create` function fails the stream is failed.
*
* Note that asynchronous boundaries (and other operators) in the stream may do pre-fetching which counter acts
* the laziness and triggers the factory immediately.
*
* The materialized future value is completed with the materialized value of the created source when
* it has been materialized. If the function throws or the source materialization fails the future materialized value
* is failed with the thrown exception.
*
* If downstream cancels or fails before the function is invoked the materialized value
* is failed with a [[akka.stream.NeverMaterializedException]]
*/
def lazyFutureSource[T, M](create: () => Future[Source[T, M]]): Source[T, Future[M]] =
lazySource(() => futureSource(create())).mapMaterializedValue(_.flatten)
/**
* Creates a `Source` that is materialized as a [[org.reactivestreams.Subscriber]]
*/
def asSubscriber[T]: Source[T, Subscriber[T]] =
fromGraph(new SubscriberSource[T](DefaultAttributes.subscriberSource, shape("SubscriberSource")))
/**
* Creates a `Source` that is materialized as an [[akka.actor.ActorRef]].
* Messages sent to this actor will be emitted to the stream if there is demand from downstream,
* otherwise they will be buffered until request for demand is received.
*
* Depending on the defined [[akka.stream.OverflowStrategy]] it might drop elements if
* there is no space available in the buffer.
*
* The strategy [[akka.stream.OverflowStrategy.backpressure]] is not supported, and an
* IllegalArgument("Backpressure overflowStrategy not supported") will be thrown if it is passed as argument.
*
* The buffer can be disabled by using `bufferSize` of 0 and then received messages are dropped if there is no demand
* from downstream. When `bufferSize` is 0 the `overflowStrategy` does not matter. An async boundary is added after
* this Source; as such, it is never safe to assume the downstream will always generate demand.
*
* The stream can be completed successfully by sending the actor reference a message that is matched by
* `completionMatcher` in which case already buffered elements will be signaled before signaling
* completion.
*
* The stream can be completed with failure by sending a message that is matched by `failureMatcher`. The extracted
* [[Throwable]] will be used to fail the stream. In case the Actor is still draining its internal buffer (after having received
* a message matched by `completionMatcher`) before signaling completion and it receives a message matched by `failureMatcher`,
* the failure will be signaled downstream immediately (instead of the completion signal).
*
* Note that terminating the actor without first completing it, either with a success or a
* failure, will prevent the actor triggering downstream completion and the stream will continue
* to run even though the source actor is dead. Therefore you should **not** attempt to
* manually terminate the actor such as with a [[akka.actor.PoisonPill]].
*
* The actor will be stopped when the stream is completed, failed or canceled from downstream,
* i.e. you can watch it to get notified when that happens.
*
* See also [[akka.stream.scaladsl.Source.queue]].
*
* @param completionMatcher catches the completion message to end the stream
* @param failureMatcher catches the failure message to fail the stream
* @param bufferSize The size of the buffer in element count
* @param overflowStrategy Strategy that is used when incoming elements cannot fit inside the buffer
*/
def actorRef[T](
completionMatcher: PartialFunction[Any, CompletionStrategy],
failureMatcher: PartialFunction[Any, Throwable],
bufferSize: Int,
overflowStrategy: OverflowStrategy): Source[T, ActorRef] = {
require(bufferSize >= 0, "bufferSize must be greater than or equal to 0")
require(!overflowStrategy.isBackpressure, "Backpressure overflowStrategy not supported")
Source
.fromGraph(new ActorRefSource(bufferSize, overflowStrategy, completionMatcher, failureMatcher))
.withAttributes(DefaultAttributes.actorRefSource)
}
/**
* Creates a `Source` that is materialized as an [[akka.actor.ActorRef]].
* Messages sent to this actor will be emitted to the stream if there is demand from downstream,
* otherwise they will be buffered until request for demand is received.
*
* Depending on the defined [[akka.stream.OverflowStrategy]] it might drop elements if
* there is no space available in the buffer.
*
* The strategy [[akka.stream.OverflowStrategy.backpressure]] is not supported, and an
* IllegalArgument("Backpressure overflowStrategy not supported") will be thrown if it is passed as argument.
*
* The buffer can be disabled by using `bufferSize` of 0 and then received messages are dropped if there is no demand
* from downstream. When `bufferSize` is 0 the `overflowStrategy` does not matter. An async boundary is added after
* this Source; as such, it is never safe to assume the downstream will always generate demand.
*
* The stream can be completed successfully by sending the actor reference a [[akka.actor.Status.Success]].
* If the content is [[akka.stream.CompletionStrategy.immediately]] the completion will be signaled immediately.
* Otherwise, if the content is [[akka.stream.CompletionStrategy.draining]] (or anything else)
* already buffered elements will be sent out before signaling completion.
* Sending [[akka.actor.PoisonPill]] will signal completion immediately but this behavior is deprecated and scheduled to be removed.
* Using [[akka.actor.ActorSystem.stop]] to stop the actor and complete the stream is *not supported*.
*
* The stream can be completed with failure by sending a [[akka.actor.Status.Failure]] to the
* actor reference. In case the Actor is still draining its internal buffer (after having received
* a [[akka.actor.Status.Success]]) before signaling completion and it receives a [[akka.actor.Status.Failure]],
* the failure will be signaled downstream immediately (instead of the completion signal).
*
* The actor will be stopped when the stream is canceled from downstream,
* i.e. you can watch it to get notified when that happens.
*
* See also [[akka.stream.scaladsl.Source.queue]].
*
*
* @param bufferSize The size of the buffer in element count
* @param overflowStrategy Strategy that is used when incoming elements cannot fit inside the buffer
*/
@deprecated("Use variant accepting completion and failure matchers instead", "2.6.0")
def actorRef[T](bufferSize: Int, overflowStrategy: OverflowStrategy): Source[T, ActorRef] =
actorRef({
case akka.actor.Status.Success(s: CompletionStrategy) => s
case akka.actor.Status.Success(_) => CompletionStrategy.Draining
case akka.actor.Status.Success => CompletionStrategy.Draining
}, { case akka.actor.Status.Failure(cause) => cause }, bufferSize, overflowStrategy)
/**
* INTERNAL API
*/
@InternalApi private[akka] def actorRefWithAck[T](
ackTo: Option[ActorRef],
ackMessage: Any,
completionMatcher: PartialFunction[Any, CompletionStrategy],
failureMatcher: PartialFunction[Any, Throwable]): Source[T, ActorRef] = {
Source.fromGraph(new ActorRefBackpressureSource(ackTo, ackMessage, completionMatcher, failureMatcher))
}
/**
* Creates a `Source` that is materialized as an [[akka.actor.ActorRef]].
* Messages sent to this actor will be emitted to the stream if there is demand from downstream,
* and a new message will only be accepted after the previous messages has been consumed and acknowledged back.
* The stream will complete with failure if a message is sent before the acknowledgement has been replied back.
*
* The stream can be completed with failure by sending a message that is matched by `failureMatcher`. The extracted
* [[Throwable]] will be used to fail the stream. In case the Actor is still draining its internal buffer (after having received
* a message matched by `completionMatcher`) before signaling completion and it receives a message matched by `failureMatcher`,
* the failure will be signaled downstream immediately (instead of the completion signal).
*
* The actor will be stopped when the stream is completed, failed or canceled from downstream,
* i.e. you can watch it to get notified when that happens.
*/
def actorRefWithBackpressure[T](
ackMessage: Any,
completionMatcher: PartialFunction[Any, CompletionStrategy],
failureMatcher: PartialFunction[Any, Throwable]): Source[T, ActorRef] = {
Source.fromGraph(new ActorRefBackpressureSource(None, ackMessage, completionMatcher, failureMatcher))
}
/**
* Creates a `Source` that is materialized as an [[akka.actor.ActorRef]].
* Messages sent to this actor will be emitted to the stream if there is demand from downstream,
* and a new message will only be accepted after the previous messages has been consumed and acknowledged back.
* The stream will complete with failure if a message is sent before the acknowledgement has been replied back.
*
* The stream can be completed successfully by sending the actor reference a [[akka.actor.Status.Success]].
* If the content is [[akka.stream.CompletionStrategy.immediately]] the completion will be signaled immediately,
* otherwise if the content is [[akka.stream.CompletionStrategy.draining]] (or anything else)
* already buffered element will be signaled before signaling completion.
*
* The stream can be completed with failure by sending a [[akka.actor.Status.Failure]] to the
* actor reference. In case the Actor is still draining its internal buffer (after having received
* a [[akka.actor.Status.Success]]) before signaling completion and it receives a [[akka.actor.Status.Failure]],
* the failure will be signaled downstream immediately (instead of the completion signal).
*
* The actor will be stopped when the stream is completed, failed or canceled from downstream,
* i.e. you can watch it to get notified when that happens.
*/
@deprecated("Use actorRefWithBackpressure accepting completion and failure matchers instead", "2.6.0")
def actorRefWithAck[T](ackMessage: Any): Source[T, ActorRef] =
actorRefWithAck(None, ackMessage, {
case akka.actor.Status.Success(s: CompletionStrategy) => s
case akka.actor.Status.Success(_) => CompletionStrategy.Draining
case akka.actor.Status.Success => CompletionStrategy.Draining
}, { case akka.actor.Status.Failure(cause) => cause })
/**
* Combines several sources with fan-in strategy like [[Merge]] or [[Concat]] into a single [[Source]].
*/
def combine[T, U](first: Source[T, _], second: Source[T, _], rest: Source[T, _]*)(
strategy: Int => Graph[UniformFanInShape[T, U], NotUsed]): Source[U, NotUsed] =
Source.fromGraph(GraphDSL.create() { implicit b =>
import GraphDSL.Implicits._
val c = b.add(strategy(rest.size + 2))
first ~> c.in(0)
second ~> c.in(1)
@tailrec def combineRest(idx: Int, i: Iterator[Source[T, _]]): SourceShape[U] =
if (i.hasNext) {
i.next() ~> c.in(idx)
combineRest(idx + 1, i)
} else SourceShape(c.out)
combineRest(2, rest.iterator)
})
/**
* Combines several sources with fan-in strategy like [[Merge]] or [[Concat]] into a single [[Source]] with a materialized value.
*/
def combineMat[T, U, M1, M2, M](first: Source[T, M1], second: Source[T, M2])(
strategy: Int => Graph[UniformFanInShape[T, U], NotUsed])(matF: (M1, M2) => M): Source[U, M] = {
val secondPartiallyCombined = GraphDSL.create(second) { implicit b => secondShape =>
import GraphDSL.Implicits._
val c = b.add(strategy(2))
secondShape ~> c.in(1)
FlowShape(c.in(0), c.out)
}
first.viaMat(secondPartiallyCombined)(matF)
}
/**
* Combine the elements of multiple streams into a stream of sequences.
*/
def zipN[T](sources: immutable.Seq[Source[T, _]]): Source[immutable.Seq[T], NotUsed] =
zipWithN(ConstantFun.scalaIdentityFunction[immutable.Seq[T]])(sources).addAttributes(DefaultAttributes.zipN)
/*
* Combine the elements of multiple streams into a stream of sequences using a combiner function.
*/
def zipWithN[T, O](zipper: immutable.Seq[T] => O)(sources: immutable.Seq[Source[T, _]]): Source[O, NotUsed] = {
val source = sources match {
case immutable.Seq() => empty[O]
case immutable.Seq(source) => source.map(t => zipper(immutable.Seq(t))).mapMaterializedValue(_ => NotUsed)
case s1 +: s2 +: ss => combine(s1, s2, ss: _*)(ZipWithN(zipper))
}
source.addAttributes(DefaultAttributes.zipWithN)
}
/**
* Creates a `Source` that is materialized as an [[akka.stream.scaladsl.SourceQueueWithComplete]].
* You can push elements to the queue and they will be emitted to the stream if there is demand from downstream,
* otherwise they will be buffered until request for demand is received. Elements in the buffer will be discarded
* if downstream is terminated.
*
* Depending on the defined [[akka.stream.OverflowStrategy]] it might drop elements if
* there is no space available in the buffer.
*
* Acknowledgement mechanism is available.
* [[akka.stream.scaladsl.SourceQueueWithComplete.offer]] returns `Future[QueueOfferResult]` which completes with
* `QueueOfferResult.Enqueued` if element was added to buffer or sent downstream. It completes with
* `QueueOfferResult.Dropped` if element was dropped. Can also complete with `QueueOfferResult.Failure` -
* when stream failed or `QueueOfferResult.QueueClosed` when downstream is completed.
*
* The strategy [[akka.stream.OverflowStrategy.backpressure]] will not complete last `offer():Future`
* call when buffer is full.
*
* You can watch accessibility of stream with [[akka.stream.scaladsl.SourceQueueWithComplete.watchCompletion]].
* It returns future that completes with success when the operator is completed or fails when the stream is failed.
*
* The buffer can be disabled by using `bufferSize` of 0 and then received message will wait
* for downstream demand unless there is another message waiting for downstream demand, in that case
* offer result will be completed according to the overflow strategy.
*
* @param bufferSize size of buffer in element count
* @param overflowStrategy Strategy that is used when incoming elements cannot fit inside the buffer
*/
def queue[T](bufferSize: Int, overflowStrategy: OverflowStrategy): Source[T, SourceQueueWithComplete[T]] =
Source.fromGraph(new QueueSource(bufferSize, overflowStrategy).withAttributes(DefaultAttributes.queueSource))
/**
* Start a new `Source` from some resource which can be opened, read and closed.
* Interaction with resource happens in a blocking way.
*
* Example:
* {{{
* Source.unfoldResource(
* () => new BufferedReader(new FileReader("...")),
* reader => Option(reader.readLine()),
* reader => reader.close())
* }}}
*
* You can use the supervision strategy to handle exceptions for `read` function. All exceptions thrown by `create`
* or `close` will fail the stream.
*
* `Restart` supervision strategy will close and create blocking IO again. Default strategy is `Stop` which means
* that stream will be terminated on error in `read` function by default.
*
* You can configure the default dispatcher for this Source by changing the `akka.stream.materializer.blocking-io-dispatcher` or
* set it for a given Source by using [[ActorAttributes]].
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* @param create - function that is called on stream start and creates/opens resource.
* @param read - function that reads data from opened resource. It is called each time backpressure signal
* is received. Stream calls close and completes when `read` returns None.
* @param close - function that closes resource
*/
def unfoldResource[T, S](create: () => S, read: (S) => Option[T], close: (S) => Unit): Source[T, NotUsed] =
Source.fromGraph(new UnfoldResourceSource(create, read, close))
/**
* Start a new `Source` from some resource which can be opened, read and closed.
* It's similar to `unfoldResource` but takes functions that return `Futures` instead of plain values.
*
* You can use the supervision strategy to handle exceptions for `read` function or failures of produced `Futures`.
* All exceptions thrown by `create` or `close` as well as fails of returned futures will fail the stream.
*
* `Restart` supervision strategy will close and create resource. Default strategy is `Stop` which means
* that stream will be terminated on error in `read` function (or future) by default.
*
* You can configure the default dispatcher for this Source by changing the `akka.stream.materializer.blocking-io-dispatcher` or
* set it for a given Source by using [[ActorAttributes]].
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* @param create - function that is called on stream start and creates/opens resource.
* @param read - function that reads data from opened resource. It is called each time backpressure signal
* is received. Stream calls close and completes when `Future` from read function returns None.
* @param close - function that closes resource
*/
def unfoldResourceAsync[T, S](
create: () => Future[S],
read: (S) => Future[Option[T]],
close: (S) => Future[Done]): Source[T, NotUsed] =
Source.fromGraph(new UnfoldResourceSourceAsync(create, read, close))
}
