1828 lines
79 KiB
Scala
1828 lines
79 KiB
Scala
/**
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* Copyright (C) 2014-2016 Lightbend Inc. <http://www.lightbend.com>
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*/
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package akka.stream.javadsl
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import akka.{ NotUsed, Done }
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import akka.event.LoggingAdapter
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import akka.japi.{ function, Pair }
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import akka.stream.impl.{ ConstantFun, StreamLayout }
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import akka.stream.{ scaladsl, _ }
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import akka.stream.stage.Stage
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import org.reactivestreams.Processor
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import scala.annotation.unchecked.uncheckedVariance
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import scala.concurrent.duration.FiniteDuration
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import akka.japi.Util
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import java.util.Comparator
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import java.util.concurrent.CompletionStage
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import scala.compat.java8.FutureConverters._
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object Flow {
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private[this] val _identity = new javadsl.Flow(scaladsl.Flow[Any])
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/** Create a `Flow` which can process elements of type `T`. */
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def create[T](): javadsl.Flow[T, T, NotUsed] = fromGraph(scaladsl.Flow[T])
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def fromProcessor[I, O](processorFactory: function.Creator[Processor[I, O]]): javadsl.Flow[I, O, NotUsed] =
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new Flow(scaladsl.Flow.fromProcessor(() ⇒ processorFactory.create()))
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def fromProcessorMat[I, O, Mat](processorFactory: function.Creator[Pair[Processor[I, O], Mat]]): javadsl.Flow[I, O, Mat] =
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new Flow(scaladsl.Flow.fromProcessorMat { () ⇒
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val javaPair = processorFactory.create()
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(javaPair.first, javaPair.second)
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})
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/**
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* Creates a [Flow] which will use the given function to transform its inputs to outputs. It is equivalent
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* to `Flow.create[T].map(f)`
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*/
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def fromFunction[I, O](f: function.Function[I, O]): javadsl.Flow[I, O, NotUsed] =
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Flow.create[I]().map(f)
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/** Create a `Flow` which can process elements of type `T`. */
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def of[T](clazz: Class[T]): javadsl.Flow[T, T, NotUsed] = create[T]()
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/**
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* A graph with the shape of a flow logically is a flow, this method makes it so also in type.
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*/
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def fromGraph[I, O, M](g: Graph[FlowShape[I, O], M]): Flow[I, O, M] =
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g match {
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case f: Flow[I, O, M] ⇒ f
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case f: scaladsl.Flow[I, O, M] if f.isIdentity ⇒ _identity.asInstanceOf[Flow[I, O, M]]
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case other ⇒ new Flow(scaladsl.Flow.fromGraph(other))
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}
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/**
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* Helper to create `Flow` from a `Sink`and a `Source`.
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*/
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def fromSinkAndSource[I, O](sink: Graph[SinkShape[I], _], source: Graph[SourceShape[O], _]): Flow[I, O, NotUsed] =
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new Flow(scaladsl.Flow.fromSinkAndSourceMat(sink, source)(scaladsl.Keep.none))
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/**
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* Helper to create `Flow` from a `Sink`and a `Source`.
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*/
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def fromSinkAndSourceMat[I, O, M1, M2, M](
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sink: Graph[SinkShape[I], M1], source: Graph[SourceShape[O], M2],
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combine: function.Function2[M1, M2, M]): Flow[I, O, M] =
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new Flow(scaladsl.Flow.fromSinkAndSourceMat(sink, source)(combinerToScala(combine)))
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}
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/** Create a `Flow` which can process elements of type `T`. */
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final class Flow[-In, +Out, +Mat](delegate: scaladsl.Flow[In, Out, Mat]) extends Graph[FlowShape[In, Out], Mat] {
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import scala.collection.JavaConverters._
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override def shape: FlowShape[In, Out] = delegate.shape
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private[stream] def module: StreamLayout.Module = delegate.module
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/** Converts this Flow to its Scala DSL counterpart */
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def asScala: scaladsl.Flow[In, Out, Mat] = delegate
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/**
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* Transform only the materialized value of this Flow, leaving all other properties as they were.
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*/
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def mapMaterializedValue[Mat2](f: function.Function[Mat, Mat2]): Flow[In, Out, Mat2] =
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new Flow(delegate.mapMaterializedValue(f.apply _))
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/**
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* Transform this [[Flow]] by appending the given processing steps.
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* {{{
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* +----------------------------+
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* | Resulting Flow |
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* | |
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* | +------+ +------+ |
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* | | | | | |
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* In ~~> | this | ~Out~> | flow | ~~> T
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* | | | | | |
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* | +------+ +------+ |
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* +----------------------------+
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* }}}
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* The materialized value of the combined [[Flow]] will be the materialized
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* value of the current flow (ignoring the other Flow’s value), use
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* `viaMat` if a different strategy is needed.
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*/
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def via[T, M](flow: Graph[FlowShape[Out, T], M]): javadsl.Flow[In, T, Mat] =
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new Flow(delegate.via(flow))
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/**
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* Transform this [[Flow]] by appending the given processing steps.
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* {{{
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* +----------------------------+
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* | Resulting Flow |
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* | |
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* | +------+ +------+ |
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* | | | | | |
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* In ~~> | this | ~Out~> | flow | ~~> T
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* | | | | | |
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* | +------+ +------+ |
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* +----------------------------+
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* }}}
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* The `combine` function is used to compose the materialized values of this flow and that
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* flow into the materialized value of the resulting Flow.
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*/
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def viaMat[T, M, M2](flow: Graph[FlowShape[Out, T], M], combine: function.Function2[Mat, M, M2]): javadsl.Flow[In, T, M2] =
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new Flow(delegate.viaMat(flow)(combinerToScala(combine)))
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/**
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* Connect this [[Flow]] to a [[Sink]], concatenating the processing steps of both.
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* {{{
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* +----------------------------+
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* | Resulting Sink |
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* | |
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* | +------+ +------+ |
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* | | | | | |
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* In ~~> | flow | ~Out~> | sink | |
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* | | | | | |
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* | +------+ +------+ |
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* +----------------------------+
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* }}}
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* The materialized value of the combined [[Sink]] will be the materialized
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* value of the current flow (ignoring the given Sink’s value), use
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* `toMat` if a different strategy is needed.
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*/
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def to(sink: Graph[SinkShape[Out], _]): javadsl.Sink[In, Mat] =
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new Sink(delegate.to(sink))
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/**
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* Connect this [[Flow]] to a [[Sink]], concatenating the processing steps of both.
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* {{{
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* +----------------------------+
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* | Resulting Sink |
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* | |
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* | +------+ +------+ |
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* | | | | | |
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* In ~~> | flow | ~Out~> | sink | |
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* | | | | | |
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* | +------+ +------+ |
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* +----------------------------+
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* }}}
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* The `combine` function is used to compose the materialized values of this flow and that
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* Sink into the materialized value of the resulting Sink.
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*/
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def toMat[M, M2](sink: Graph[SinkShape[Out], M], combine: function.Function2[Mat, M, M2]): javadsl.Sink[In, M2] =
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new Sink(delegate.toMat(sink)(combinerToScala(combine)))
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/**
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* Join this [[Flow]] to another [[Flow]], by cross connecting the inputs and outputs, creating a [[RunnableGraph]].
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* {{{
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* +------+ +-------+
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* | | ~Out~> | |
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* | this | | other |
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* | | <~In~ | |
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* +------+ +-------+
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* }}}
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* The materialized value of the combined [[Flow]] will be the materialized
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* value of the current flow (ignoring the other Flow’s value), use
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* `joinMat` if a different strategy is needed.
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*/
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def join[M](flow: Graph[FlowShape[Out, In], M]): javadsl.RunnableGraph[Mat] =
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RunnableGraph.fromGraph(delegate.join(flow))
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/**
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* Join this [[Flow]] to another [[Flow]], by cross connecting the inputs and outputs, creating a [[RunnableGraph]]
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* {{{
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* +------+ +-------+
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* | | ~Out~> | |
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* | this | | other |
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* | | <~In~ | |
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* +------+ +-------+
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* }}}
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* The `combine` function is used to compose the materialized values of this flow and that
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* Flow into the materialized value of the resulting Flow.
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*/
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def joinMat[M, M2](flow: Graph[FlowShape[Out, In], M], combine: function.Function2[Mat, M, M2]): javadsl.RunnableGraph[M2] =
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RunnableGraph.fromGraph(delegate.joinMat(flow)(combinerToScala(combine)))
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/**
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* Join this [[Flow]] to a [[BidiFlow]] to close off the “top” of the protocol stack:
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* {{{
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* +---------------------------+
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* | Resulting Flow |
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* | |
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* | +------+ +------+ |
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* | | | ~Out~> | | ~~> O2
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* | | flow | | bidi | |
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* | | | <~In~ | | <~~ I2
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* | +------+ +------+ |
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* +---------------------------+
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* }}}
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* The materialized value of the combined [[Flow]] will be the materialized
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* value of the current flow (ignoring the [[BidiFlow]]’s value), use
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* [[Flow#joinMat[I2* joinMat]] if a different strategy is needed.
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*/
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def join[I2, O2, Mat2](bidi: Graph[BidiShape[Out, O2, I2, In], Mat2]): Flow[I2, O2, Mat] =
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new Flow(delegate.join(bidi))
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/**
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* Join this [[Flow]] to a [[BidiFlow]] to close off the “top” of the protocol stack:
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* {{{
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* +---------------------------+
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* | Resulting Flow |
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* | |
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* | +------+ +------+ |
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* | | | ~Out~> | | ~~> O2
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* | | flow | | bidi | |
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* | | | <~In~ | | <~~ I2
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* | +------+ +------+ |
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* +---------------------------+
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* }}}
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* The `combine` function is used to compose the materialized values of this flow and that
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* [[BidiFlow]] into the materialized value of the resulting [[Flow]].
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*/
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def joinMat[I2, O2, Mat2, M](bidi: Graph[BidiShape[Out, O2, I2, In], Mat2], combine: function.Function2[Mat, Mat2, M]): Flow[I2, O2, M] =
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new Flow(delegate.joinMat(bidi)(combinerToScala(combine)))
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/**
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* Connect the `Source` to this `Flow` and then connect it to the `Sink` and run it.
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*
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* The returned tuple contains the materialized values of the `Source` and `Sink`,
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* e.g. the `Subscriber` of a `Source.asSubscriber` and `Publisher` of a `Sink.asPublisher`.
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*
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* @tparam T materialized type of given Source
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* @tparam U materialized type of given Sink
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*/
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def runWith[T, U](source: Graph[SourceShape[In], T], sink: Graph[SinkShape[Out], U], materializer: Materializer): akka.japi.Pair[T, U] = {
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val (som, sim) = delegate.runWith(source, sink)(materializer)
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akka.japi.Pair(som, sim)
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}
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/**
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* Transform this stream by applying the given function to each of the elements
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* as they pass through this processing step.
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*
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* '''Emits when''' the mapping function returns an element
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*
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* '''Backpressures when''' downstream backpressures
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*
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* '''Completes when''' upstream completes
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*
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* '''Cancels when''' downstream cancels
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*/
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def map[T](f: function.Function[Out, T]): javadsl.Flow[In, T, Mat] =
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new Flow(delegate.map(f.apply))
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/**
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* Transform each input element into an `Iterable` of output elements that is
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* then flattened into the output stream.
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*
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* Make sure that the `Iterable` is immutable or at least not modified after
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* being used as an output sequence. Otherwise the stream may fail with
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* `ConcurrentModificationException` or other more subtle errors may occur.
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*
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* The returned `Iterable` MUST NOT contain `null` values,
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* as they are illegal as stream elements - according to the Reactive Streams specification.
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*
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* '''Emits when''' the mapping function returns an element or there are still remaining elements
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* from the previously calculated collection
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*
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* '''Backpressures when''' downstream backpressures or there are still remaining elements from the
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* previously calculated collection
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*
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* '''Completes when''' upstream completes and all remaining elements have been emitted
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*
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* '''Cancels when''' downstream cancels
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*/
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def mapConcat[T](f: function.Function[Out, java.lang.Iterable[T]]): javadsl.Flow[In, T, Mat] =
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new Flow(delegate.mapConcat { elem ⇒ Util.immutableSeq(f(elem)) })
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/**
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* Transform each input element into an `Iterable` of output elements that is
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* then flattened into the output stream. The transformation is meant to be stateful,
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* which is enabled by creating the transformation function anew for every materialization —
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* the returned function will typically close over mutable objects to store state between
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* invocations. For the stateless variant see [[#mapConcat]].
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*
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* Make sure that the `Iterable` is immutable or at least not modified after
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* being used as an output sequence. Otherwise the stream may fail with
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* `ConcurrentModificationException` or other more subtle errors may occur.
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*
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* The returned `Iterable` MUST NOT contain `null` values,
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* as they are illegal as stream elements - according to the Reactive Streams specification.
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*
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* '''Emits when''' the mapping function returns an element or there are still remaining elements
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* from the previously calculated collection
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*
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* '''Backpressures when''' downstream backpressures or there are still remaining elements from the
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* previously calculated collection
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*
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* '''Completes when''' upstream completes and all remaining elements has been emitted
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*
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* '''Cancels when''' downstream cancels
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*/
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def statefulMapConcat[T](f: function.Creator[function.Function[Out, java.lang.Iterable[T]]]): javadsl.Flow[In, T, Mat] =
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new Flow(delegate.statefulMapConcat { () ⇒
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val fun = f.create()
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elem ⇒ Util.immutableSeq(fun(elem))
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})
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/**
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* Transform this stream by applying the given function to each of the elements
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* as they pass through this processing step. The function returns a `CompletionStage` and the
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* value of that future will be emitted downstream. As many futures as requested elements by
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* downstream may run in parallel and may complete in any order, but the elements that
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* are emitted downstream are in the same order as received from upstream.
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*
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* If the function `f` throws an exception or if the `CompletionStage` is completed
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* with failure and the supervision decision is [[akka.stream.Supervision#stop]]
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* the stream will be completed with failure.
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*
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* If the function `f` throws an exception or if the `CompletionStage` is completed
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* with failure and the supervision decision is [[akka.stream.Supervision#resume]] or
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* [[akka.stream.Supervision#restart]] the element is dropped and the stream continues.
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*
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* The function `f` is always invoked on the elements in the order they arrive.
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*
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* '''Emits when''' the CompletionStage returned by the provided function finishes for the next element in sequence
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*
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* '''Backpressures when''' the number of futures reaches the configured parallelism and the downstream
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* backpressures or the first future is not completed
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*
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* '''Completes when''' upstream completes and all futures have been completed and all elements have been emitted
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*
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* '''Cancels when''' downstream cancels
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*
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* @see [[#mapAsyncUnordered]]
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*/
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def mapAsync[T](parallelism: Int, f: function.Function[Out, CompletionStage[T]]): javadsl.Flow[In, T, Mat] =
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new Flow(delegate.mapAsync(parallelism)(x ⇒ f(x).toScala))
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/**
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* Transform this stream by applying the given function to each of the elements
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* as they pass through this processing step. The function returns a `CompletionStage` and the
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* value of that future will be emitted downstream. As many futures as requested elements by
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* downstream may run in parallel and each processed element will be emitted downstream
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* as soon as it is ready, i.e. it is possible that the elements are not emitted downstream
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* in the same order as received from upstream.
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*
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* If the function `f` throws an exception or if the `CompletionStage` is completed
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* with failure and the supervision decision is [[akka.stream.Supervision#stop]]
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* the stream will be completed with failure.
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*
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* If the function `f` throws an exception or if the `CompletionStage` is completed
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* with failure and the supervision decision is [[akka.stream.Supervision#resume]] or
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* [[akka.stream.Supervision#restart]] the element is dropped and the stream continues.
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*
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* The function `f` is always invoked on the elements in the order they arrive (even though the result of the futures
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* returned by `f` might be emitted in a different order).
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*
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* '''Emits when''' any of the CompletionStages returned by the provided function complete
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*
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* '''Backpressures when''' the number of futures reaches the configured parallelism and the downstream backpressures
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*
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* '''Completes when''' upstream completes and all futures have been completed and all elements have been emitted
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*
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* '''Cancels when''' downstream cancels
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*
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* @see [[#mapAsync]]
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*/
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def mapAsyncUnordered[T](parallelism: Int, f: function.Function[Out, CompletionStage[T]]): javadsl.Flow[In, T, Mat] =
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new Flow(delegate.mapAsyncUnordered(parallelism)(x ⇒ f(x).toScala))
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/**
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* Only pass on those elements that satisfy the given predicate.
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*
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* '''Emits when''' the given predicate returns true for the element
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*
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* '''Backpressures when''' the given predicate returns true for the element and downstream backpressures
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*
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* '''Completes when''' upstream completes
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*
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* '''Cancels when''' downstream cancels
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*
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*/
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def filter(p: function.Predicate[Out]): javadsl.Flow[In, Out, Mat] =
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new Flow(delegate.filter(p.test))
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/**
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* Only pass on those elements that NOT satisfy the given predicate.
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*
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* '''Emits when''' the given predicate returns false for the element
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*
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* '''Backpressures when''' the given predicate returns false for the element and downstream backpressures
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*
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* '''Completes when''' upstream completes
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*
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* '''Cancels when''' downstream cancels
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*/
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def filterNot(p: function.Predicate[Out]): javadsl.Flow[In, Out, Mat] =
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new Flow(delegate.filterNot(p.test))
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/**
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* Transform this stream by applying the given partial function to each of the elements
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* on which the function is defined as they pass through this processing step.
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* Non-matching elements are filtered out.
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*
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* '''Emits when''' the provided partial function is defined for the element
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*
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* '''Backpressures when''' the partial function is defined for the element and downstream backpressures
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*
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* '''Completes when''' upstream completes
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*
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* '''Cancels when''' downstream cancels
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*/
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def collect[T](pf: PartialFunction[Out, T]): javadsl.Flow[In, T, Mat] =
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new Flow(delegate.collect(pf))
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/**
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* Chunk up this stream into groups of the given size, with the last group
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* possibly smaller than requested due to end-of-stream.
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*
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* `n` must be positive, otherwise IllegalArgumentException is thrown.
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*
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* '''Emits when''' the specified number of elements has been accumulated or upstream completed
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*
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* '''Backpressures when''' a group has been assembled and downstream backpressures
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*
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* '''Completes when''' upstream completes
|
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*
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* '''Cancels when''' downstream cancels
|
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*/
|
||
def grouped(n: Int): javadsl.Flow[In, java.util.List[Out @uncheckedVariance], Mat] =
|
||
new Flow(delegate.grouped(n).map(_.asJava)) // TODO optimize to one step
|
||
|
||
/**
|
||
* Ensure stream boundedness by limiting the number of elements from upstream.
|
||
* If the number of incoming elements exceeds max, it will signal
|
||
* upstream failure `StreamLimitException` downstream.
|
||
*
|
||
* Due to input buffering some elements may have been
|
||
* requested from upstream publishers that will then not be processed downstream
|
||
* of this step.
|
||
*
|
||
* The stream will be completed without producing any elements if `n` is zero
|
||
* or negative.
|
||
*
|
||
* '''Emits when''' the specified number of elements to take has not yet been reached
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' the defined number of elements has been taken or upstream completes
|
||
*
|
||
* '''Errors when''' the total number of incoming element exceeds max
|
||
*
|
||
* '''Cancels when''' the defined number of elements has been taken or downstream cancels
|
||
*
|
||
* See also [[Flow.take]], [[Flow.takeWithin]], [[Flow.takeWhile]]
|
||
*/
|
||
def limit(n: Long): javadsl.Flow[In, Out, Mat] = new Flow(delegate.limit(n))
|
||
|
||
/**
|
||
* Ensure stream boundedness by evaluating the cost of incoming elements
|
||
* using a cost function. Exactly how many elements will be allowed to travel downstream depends on the
|
||
* evaluated cost of each element. If the accumulated cost exceeds max, it will signal
|
||
* upstream failure `StreamLimitException` downstream.
|
||
*
|
||
* Due to input buffering some elements may have been
|
||
* requested from upstream publishers that will then not be processed downstream
|
||
* of this step.
|
||
*
|
||
* The stream will be completed without producing any elements if `n` is zero
|
||
* or negative.
|
||
*
|
||
* '''Emits when''' the specified number of elements to take has not yet been reached
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' the defined number of elements has been taken or upstream completes
|
||
*
|
||
* '''Errors when''' when the accumulated cost exceeds max
|
||
*
|
||
* '''Cancels when''' the defined number of elements has been taken or downstream cancels
|
||
*
|
||
* See also [[Flow.take]], [[Flow.takeWithin]], [[Flow.takeWhile]]
|
||
*/
|
||
def limitWeighted(n: Long)(costFn: function.Function[Out, Long]): javadsl.Flow[In, Out, Mat] = {
|
||
new Flow(delegate.limitWeighted(n)(costFn.apply))
|
||
}
|
||
|
||
/**
|
||
* Apply a sliding window over the stream and return the windows as groups of elements, with the last group
|
||
* possibly smaller than requested due to end-of-stream.
|
||
*
|
||
* `n` must be positive, otherwise IllegalArgumentException is thrown.
|
||
* `step` must be positive, otherwise IllegalArgumentException is thrown.
|
||
*
|
||
* '''Emits when''' enough elements have been collected within the window or upstream completed
|
||
*
|
||
* '''Backpressures when''' a window has been assembled and downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def sliding(n: Int, step: Int = 1): javadsl.Flow[In, java.util.List[Out @uncheckedVariance], Mat] =
|
||
new Flow(delegate.sliding(n, step).map(_.asJava)) // TODO optimize to one step
|
||
|
||
/**
|
||
* Similar to `fold` but is not a terminal operation,
|
||
* emits its current value which starts at `zero` and then
|
||
* applies the current and next value to the given function `f`,
|
||
* emitting the next current value.
|
||
*
|
||
* If the function `f` throws an exception and the supervision decision is
|
||
* [[akka.stream.Supervision#restart]] current value starts at `zero` again
|
||
* the stream will continue.
|
||
*
|
||
* '''Emits when''' the function scanning the element returns a new element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def scan[T](zero: T)(f: function.Function2[T, Out, T]): javadsl.Flow[In, T, Mat] =
|
||
new Flow(delegate.scan(zero)(f.apply))
|
||
|
||
/**
|
||
* Similar to `scan` but only emits its result when the upstream completes,
|
||
* after which it also completes. Applies the given function `f` towards its current and next value,
|
||
* yielding the next current value.
|
||
*
|
||
* If the function `f` throws an exception and the supervision decision is
|
||
* [[akka.stream.Supervision#restart]] current value starts at `zero` again
|
||
* the stream will continue.
|
||
*
|
||
* '''Emits when''' upstream completes
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def fold[T](zero: T)(f: function.Function2[T, Out, T]): javadsl.Flow[In, T, Mat] =
|
||
new Flow(delegate.fold(zero)(f.apply))
|
||
|
||
/**
|
||
* Similar to `fold` but uses first element as zero element.
|
||
* Applies the given function towards its current and next value,
|
||
* yielding the next current value.
|
||
*
|
||
* '''Emits when''' upstream completes
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def reduce(f: function.Function2[Out, Out, Out @uncheckedVariance]): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.reduce(f.apply))
|
||
|
||
/**
|
||
* Intersperses stream with provided element, similar to how [[scala.collection.immutable.List.mkString]]
|
||
* injects a separator between a List's elements.
|
||
*
|
||
* Additionally can inject start and end marker elements to stream.
|
||
*
|
||
* Examples:
|
||
*
|
||
* {{{
|
||
* Source<Integer, ?> nums = Source.from(Arrays.asList(0, 1, 2, 3));
|
||
* nums.intersperse(","); // 1 , 2 , 3
|
||
* nums.intersperse("[", ",", "]"); // [ 1 , 2 , 3 ]
|
||
* }}}
|
||
*
|
||
* In case you want to only prepend or only append an element (yet still use the `intercept` feature
|
||
* to inject a separator between elements, you may want to use the following pattern instead of the 3-argument
|
||
* version of intersperse (See [[Source.concat]] for semantics details):
|
||
*
|
||
* {{{
|
||
* Source.single(">> ").concat(flow.intersperse(","))
|
||
* flow.intersperse(",").concat(Source.single("END"))
|
||
* }}}
|
||
*
|
||
* '''Emits when''' upstream emits (or before with the `start` element if provided)
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def intersperse[T >: Out](start: T, inject: T, end: T): javadsl.Flow[In, T, Mat] =
|
||
new Flow(delegate.intersperse(start, inject, end))
|
||
|
||
/**
|
||
* Intersperses stream with provided element, similar to how [[scala.collection.immutable.List.mkString]]
|
||
* injects a separator between a List's elements.
|
||
*
|
||
* Additionally can inject start and end marker elements to stream.
|
||
*
|
||
* Examples:
|
||
*
|
||
* {{{
|
||
* Source<Integer, ?> nums = Source.from(Arrays.asList(0, 1, 2, 3));
|
||
* nums.intersperse(","); // 1 , 2 , 3
|
||
* nums.intersperse("[", ",", "]"); // [ 1 , 2 , 3 ]
|
||
* }}}
|
||
*
|
||
* '''Emits when''' upstream emits (or before with the `start` element if provided)
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def intersperse[T >: Out](inject: T): javadsl.Flow[In, T, Mat] =
|
||
new Flow(delegate.intersperse(inject))
|
||
|
||
/**
|
||
* Chunk up this stream into groups of elements received within a time window,
|
||
* or limited by the given number of elements, whatever happens first.
|
||
* Empty groups will not be emitted if no elements are received from upstream.
|
||
* The last group before end-of-stream will contain the buffered elements
|
||
* since the previously emitted group.
|
||
*
|
||
* '''Emits when''' the configured time elapses since the last group has been emitted
|
||
*
|
||
* '''Backpressures when''' the configured time elapses since the last group has been emitted
|
||
*
|
||
* '''Completes when''' upstream completes (emits last group)
|
||
*
|
||
* '''Cancels when''' downstream completes
|
||
*
|
||
* `n` must be positive, and `d` must be greater than 0 seconds, otherwise
|
||
* IllegalArgumentException is thrown.
|
||
*/
|
||
def groupedWithin(n: Int, d: FiniteDuration): javadsl.Flow[In, java.util.List[Out @uncheckedVariance], Mat] =
|
||
new Flow(delegate.groupedWithin(n, d).map(_.asJava)) // TODO optimize to one step
|
||
|
||
/**
|
||
* Shifts elements emission in time by a specified amount. It allows to store elements
|
||
* in internal buffer while waiting for next element to be emitted. Depending on the defined
|
||
* [[akka.stream.DelayOverflowStrategy]] it might drop elements or backpressure the upstream if
|
||
* there is no space available in the buffer.
|
||
*
|
||
* Delay precision is 10ms to avoid unnecessary timer scheduling cycles
|
||
*
|
||
* Internal buffer has default capacity 16. You can set buffer size by calling `withAttributes(inputBuffer)`
|
||
*
|
||
* '''Emits when''' there is a pending element in the buffer and configured time for this element elapsed
|
||
* * EmitEarly - strategy do not wait to emit element if buffer is full
|
||
*
|
||
* '''Backpressures when''' depending on OverflowStrategy
|
||
* * Backpressure - backpressures when buffer is full
|
||
* * DropHead, DropTail, DropBuffer - never backpressures
|
||
* * Fail - fails the stream if buffer gets full
|
||
*
|
||
* '''Completes when''' upstream completes and buffered elements have been drained
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*
|
||
* @param of time to shift all messages
|
||
* @param strategy Strategy that is used when incoming elements cannot fit inside the buffer
|
||
*/
|
||
def delay(of: FiniteDuration, strategy: DelayOverflowStrategy): Flow[In, Out, Mat] =
|
||
new Flow(delegate.delay(of, strategy))
|
||
|
||
/**
|
||
* Discard the given number of elements at the beginning of the stream.
|
||
* No elements will be dropped if `n` is zero or negative.
|
||
*
|
||
* '''Emits when''' the specified number of elements has been dropped already
|
||
*
|
||
* '''Backpressures when''' the specified number of elements has been dropped and downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def drop(n: Long): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.drop(n))
|
||
|
||
/**
|
||
* Discard the elements received within the given duration at beginning of the stream.
|
||
*
|
||
* '''Emits when''' the specified time elapsed and a new upstream element arrives
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def dropWithin(d: FiniteDuration): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.dropWithin(d))
|
||
|
||
/**
|
||
* Terminate processing (and cancel the upstream publisher) after predicate
|
||
* returns false for the first time. Due to input buffering some elements may have been
|
||
* requested from upstream publishers that will then not be processed downstream
|
||
* of this step.
|
||
*
|
||
* The stream will be completed without producing any elements if predicate is false for
|
||
* the first stream element.
|
||
*
|
||
* '''Emits when''' the predicate is true
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' predicate returned false or upstream completes
|
||
*
|
||
* '''Cancels when''' predicate returned false or downstream cancels
|
||
*
|
||
* See also [[Flow.limit]], [[Flow.limitWeighted]]
|
||
*/
|
||
def takeWhile(p: function.Predicate[Out]): javadsl.Flow[In, Out, Mat] = new Flow(delegate.takeWhile(p.test))
|
||
|
||
/**
|
||
* Discard elements at the beginning of the stream while predicate is true.
|
||
* All elements will be taken after predicate returns false first time.
|
||
*
|
||
* '''Emits when''' predicate returned false and for all following stream elements
|
||
*
|
||
* '''Backpressures when''' predicate returned false and downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def dropWhile(p: function.Predicate[Out]): javadsl.Flow[In, Out, Mat] = new Flow(delegate.dropWhile(p.test))
|
||
|
||
/**
|
||
* Recover allows to send last element on failure and gracefully complete the stream
|
||
* Since the underlying failure signal onError arrives out-of-band, it might jump over existing elements.
|
||
* This stage can recover the failure signal, but not the skipped elements, which will be dropped.
|
||
*
|
||
* '''Emits when''' element is available from the upstream or upstream is failed and pf returns an element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes or upstream failed with exception pf can handle
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def recover[T >: Out](pf: PartialFunction[Throwable, T]): javadsl.Flow[In, T, Mat] =
|
||
new Flow(delegate.recover(pf))
|
||
|
||
/**
|
||
* RecoverWith allows to switch to alternative Source on flow failure. It will stay in effect after
|
||
* a failure has been recovered so that each time there is a failure it is fed into the `pf` and a new
|
||
* Source may be materialized.
|
||
*
|
||
* Since the underlying failure signal onError arrives out-of-band, it might jump over existing elements.
|
||
* This stage can recover the failure signal, but not the skipped elements, which will be dropped.
|
||
*
|
||
* '''Emits when''' element is available from the upstream or upstream is failed and element is available
|
||
* from alternative Source
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes or upstream failed with exception pf can handle
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*
|
||
*/
|
||
def recoverWith[T >: Out](pf: PartialFunction[Throwable, _ <: Graph[SourceShape[T], NotUsed]]): javadsl.Flow[In, T, Mat @uncheckedVariance] =
|
||
new Flow(delegate.recoverWith(pf))
|
||
|
||
/**
|
||
* Terminate processing (and cancel the upstream publisher) after the given
|
||
* number of elements. Due to input buffering some elements may have been
|
||
* requested from upstream publishers that will then not be processed downstream
|
||
* of this step.
|
||
*
|
||
* The stream will be completed without producing any elements if `n` is zero
|
||
* or negative.
|
||
*
|
||
* '''Emits when''' the specified number of elements to take has not yet been reached
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' the defined number of elements has been taken or upstream completes
|
||
*
|
||
* '''Cancels when''' the defined number of elements has been taken or downstream cancels
|
||
*
|
||
* See also [[Flow.limit]], [[Flow.limitWeighted]]
|
||
*/
|
||
def take(n: Long): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.take(n))
|
||
|
||
/**
|
||
* Terminate processing (and cancel the upstream publisher) after the given
|
||
* duration. Due to input buffering some elements may have been
|
||
* requested from upstream publishers that will then not be processed downstream
|
||
* of this step.
|
||
*
|
||
* Note that this can be combined with [[#take]] to limit the number of elements
|
||
* within the duration.
|
||
*
|
||
* '''Emits when''' an upstream element arrives
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes or timer fires
|
||
*
|
||
* '''Cancels when''' downstream cancels or timer fires
|
||
*
|
||
* See also [[Flow.limit]], [[Flow.limitWeighted]]
|
||
*/
|
||
def takeWithin(d: FiniteDuration): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.takeWithin(d))
|
||
|
||
/**
|
||
* Allows a faster upstream to progress independently of a slower subscriber by conflating elements into a summary
|
||
* until the subscriber is ready to accept them. For example a conflate step might average incoming numbers if the
|
||
* upstream publisher is faster.
|
||
*
|
||
* This version of conflate allows to derive a seed from the first element and change the aggregated type to be
|
||
* different than the input type. See [[Flow.conflate]] for a simpler version that does not change types.
|
||
*
|
||
* This element only rolls up elements if the upstream is faster, but if the downstream is faster it will not
|
||
* duplicate elements.
|
||
*
|
||
* '''Emits when''' downstream stops backpressuring and there is a conflated element available
|
||
*
|
||
* '''Backpressures when''' never
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*
|
||
* see also [[Flow.conflate]] [[Flow.batch]] [[Flow.batchWeighted]]
|
||
*
|
||
* @param seed Provides the first state for a conflated value using the first unconsumed element as a start
|
||
* @param aggregate Takes the currently aggregated value and the current pending element to produce a new aggregate
|
||
*
|
||
*/
|
||
def conflateWithSeed[S](seed: function.Function[Out, S], aggregate: function.Function2[S, Out, S]): javadsl.Flow[In, S, Mat] =
|
||
new Flow(delegate.conflateWithSeed(seed.apply)(aggregate.apply))
|
||
|
||
/**
|
||
* Allows a faster upstream to progress independently of a slower subscriber by conflating elements into a summary
|
||
* until the subscriber is ready to accept them. For example a conflate step might average incoming numbers if the
|
||
* upstream publisher is faster.
|
||
*
|
||
* This version of conflate does not change the output type of the stream. See [[Flow.conflateWithSeed]] for a
|
||
* more flexible version that can take a seed function and transform elements while rolling up.
|
||
*
|
||
* This element only rolls up elements if the upstream is faster, but if the downstream is faster it will not
|
||
* duplicate elements.
|
||
*
|
||
* '''Emits when''' downstream stops backpressuring and there is a conflated element available
|
||
*
|
||
* '''Backpressures when''' never
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*
|
||
* see also [[Flow.conflateWithSeed]] [[Flow.batch]] [[Flow.batchWeighted]]
|
||
*
|
||
* @param aggregate Takes the currently aggregated value and the current pending element to produce a new aggregate
|
||
*
|
||
*/
|
||
def conflate[O2 >: Out](aggregate: function.Function2[O2, O2, O2]): javadsl.Flow[In, O2, Mat] =
|
||
new Flow(delegate.conflate(aggregate.apply))
|
||
|
||
/**
|
||
* Allows a faster upstream to progress independently of a slower subscriber by aggregating elements into batches
|
||
* until the subscriber is ready to accept them. For example a batch step might store received elements in
|
||
* an array up to the allowed max limit if the upstream publisher is faster.
|
||
*
|
||
* This element only rolls up elements if the upstream is faster, but if the downstream is faster it will not
|
||
* duplicate elements.
|
||
*
|
||
* '''Emits when''' downstream stops backpressuring and there is an aggregated element available
|
||
*
|
||
* '''Backpressures when''' there are `max` batched elements and 1 pending element and downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes and there is no batched/pending element waiting
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*
|
||
* See also [[Flow.conflate]], [[Flow.batchWeighted]]
|
||
*
|
||
* @param max maximum number of elements to batch before backpressuring upstream (must be positive non-zero)
|
||
* @param seed Provides the first state for a batched value using the first unconsumed element as a start
|
||
* @param aggregate Takes the currently batched value and the current pending element to produce a new aggregate
|
||
*/
|
||
def batch[S](max: Long, seed: function.Function[Out, S], aggregate: function.Function2[S, Out, S]): javadsl.Flow[In, S, Mat] =
|
||
new Flow(delegate.batch(max, seed.apply)(aggregate.apply))
|
||
|
||
/**
|
||
* Allows a faster upstream to progress independently of a slower subscriber by aggregating elements into batches
|
||
* until the subscriber is ready to accept them. For example a batch step might concatenate `ByteString`
|
||
* elements up to the allowed max limit if the upstream publisher is faster.
|
||
*
|
||
* This element only rolls up elements if the upstream is faster, but if the downstream is faster it will not
|
||
* duplicate elements.
|
||
*
|
||
* Batching will apply for all elements, even if a single element cost is greater than the total allowed limit.
|
||
* In this case, previous batched elements will be emitted, then the "heavy" element will be emitted (after
|
||
* being applied with the `seed` function) without batching further elements with it, and then the rest of the
|
||
* incoming elements are batched.
|
||
*
|
||
* '''Emits when''' downstream stops backpressuring and there is a batched element available
|
||
*
|
||
* '''Backpressures when''' there are `max` weighted batched elements + 1 pending element and downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes and there is no batched/pending element waiting
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*
|
||
* See also [[Flow.conflate]], [[Flow.batch]]
|
||
*
|
||
* @param max maximum weight of elements to batch before backpressuring upstream (must be positive non-zero)
|
||
* @param costFn a function to compute a single element weight
|
||
* @param seed Provides the first state for a batched value using the first unconsumed element as a start
|
||
* @param aggregate Takes the currently batched value and the current pending element to produce a new batch
|
||
*/
|
||
def batchWeighted[S](max: Long, costFn: function.Function[Out, Long], seed: function.Function[Out, S], aggregate: function.Function2[S, Out, S]): javadsl.Flow[In, S, Mat] =
|
||
new Flow(delegate.batchWeighted(max, costFn.apply, seed.apply)(aggregate.apply))
|
||
|
||
/**
|
||
* Allows a faster downstream to progress independently of a slower publisher by extrapolating elements from an older
|
||
* element until new element comes from the upstream. For example an expand step might repeat the last element for
|
||
* the subscriber until it receives an update from upstream.
|
||
*
|
||
* This element will never "drop" upstream elements as all elements go through at least one extrapolation step.
|
||
* This means that if the upstream is actually faster than the upstream it will be backpressured by the downstream
|
||
* subscriber.
|
||
*
|
||
* Expand does not support [[akka.stream.Supervision#restart]] and [[akka.stream.Supervision#resume]].
|
||
* Exceptions from the `seed` or `extrapolate` functions will complete the stream with failure.
|
||
*
|
||
* '''Emits when''' downstream stops backpressuring
|
||
*
|
||
* '''Backpressures when''' downstream backpressures or iterator runs empty
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*
|
||
* @param seed Provides the first state for extrapolation using the first unconsumed element
|
||
* @param extrapolate Takes the current extrapolation state to produce an output element and the next extrapolation
|
||
* state.
|
||
*/
|
||
def expand[U](extrapolate: function.Function[Out, java.util.Iterator[U]]): javadsl.Flow[In, U, Mat] =
|
||
new Flow(delegate.expand(in ⇒ extrapolate(in).asScala))
|
||
|
||
/**
|
||
* Adds a fixed size buffer in the flow that allows to store elements from a faster upstream until it becomes full.
|
||
* Depending on the defined [[akka.stream.OverflowStrategy]] it might drop elements or backpressure the upstream if
|
||
* there is no space available
|
||
*
|
||
* '''Emits when''' downstream stops backpressuring and there is a pending element in the buffer
|
||
*
|
||
* '''Backpressures when''' depending on OverflowStrategy
|
||
* * Backpressure - backpressures when buffer is full
|
||
* * DropHead, DropTail, DropBuffer - never backpressures
|
||
* * Fail - fails the stream if buffer gets full
|
||
*
|
||
* '''Completes when''' upstream completes and buffered elements have been drained
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*
|
||
* @param size The size of the buffer in element count
|
||
* @param overflowStrategy Strategy that is used when incoming elements cannot fit inside the buffer
|
||
*/
|
||
def buffer(size: Int, overflowStrategy: OverflowStrategy): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.buffer(size, overflowStrategy))
|
||
|
||
/**
|
||
* Generic transformation of a stream with a custom processing [[akka.stream.stage.Stage]].
|
||
* This operator makes it possible to extend the `Flow` API when there is no specialized
|
||
* operator that performs the transformation.
|
||
*/
|
||
@deprecated("Use via(GraphStage) instead.", "2.4.3")
|
||
def transform[U](mkStage: function.Creator[Stage[Out, U]]): javadsl.Flow[In, U, Mat] =
|
||
new Flow(delegate.transform(() ⇒ mkStage.create()))
|
||
|
||
/**
|
||
* Takes up to `n` elements from the stream (less than `n` if the upstream completes before emitting `n` elements)
|
||
* and returns a pair containing a strict sequence of the taken element
|
||
* and a stream representing the remaining elements. If ''n'' is zero or negative, then this will return a pair
|
||
* of an empty collection and a stream containing the whole upstream unchanged.
|
||
*
|
||
* In case of an upstream error, depending on the current state
|
||
* - the master stream signals the error if less than `n` elements have been seen, and therefore the substream
|
||
* has not yet been emitted
|
||
* - the tail substream signals the error after the prefix and tail has been emitted by the main stream
|
||
* (at that point the main stream has already completed)
|
||
*
|
||
* '''Emits when''' the configured number of prefix elements are available. Emits this prefix, and the rest
|
||
* as a substream
|
||
*
|
||
* '''Backpressures when''' downstream backpressures or substream backpressures
|
||
*
|
||
* '''Completes when''' prefix elements have been consumed and substream has been consumed
|
||
*
|
||
* '''Cancels when''' downstream cancels or substream cancels
|
||
*/
|
||
def prefixAndTail(n: Int): javadsl.Flow[In, akka.japi.Pair[java.util.List[Out @uncheckedVariance], javadsl.Source[Out @uncheckedVariance, NotUsed]], Mat] =
|
||
new Flow(delegate.prefixAndTail(n).map { case (taken, tail) ⇒ akka.japi.Pair(taken.asJava, tail.asJava) })
|
||
|
||
/**
|
||
* This operation demultiplexes the incoming stream into separate output
|
||
* streams, one for each element key. The key is computed for each element
|
||
* using the given function. When a new key is encountered for the first time
|
||
* a new substream is opened and subsequently fed with all elements belonging to
|
||
* that key.
|
||
*
|
||
* The object returned from this method is not a normal [[Flow]],
|
||
* it is a [[SubFlow]]. This means that after this combinator all transformations
|
||
* are applied to all encountered substreams in the same fashion. Substream mode
|
||
* is exited either by closing the substream (i.e. connecting it to a [[Sink]])
|
||
* or by merging the substreams back together; see the `to` and `mergeBack` methods
|
||
* on [[SubFlow]] for more information.
|
||
*
|
||
* It is important to note that the substreams also propagate back-pressure as
|
||
* any other stream, which means that blocking one substream will block the `groupBy`
|
||
* operator itself—and thereby all substreams—once all internal or
|
||
* explicit buffers are filled.
|
||
*
|
||
* If the group by function `f` throws an exception and the supervision decision
|
||
* is [[akka.stream.Supervision#stop]] the stream and substreams will be completed
|
||
* with failure.
|
||
*
|
||
* If the group by function `f` throws an exception and the supervision decision
|
||
* is [[akka.stream.Supervision#resume]] or [[akka.stream.Supervision#restart]]
|
||
* the element is dropped and the stream and substreams continue.
|
||
*
|
||
* '''Emits when''' an element for which the grouping function returns a group that has not yet been created.
|
||
* Emits the new group
|
||
*
|
||
* '''Backpressures when''' there is an element pending for a group whose substream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels and all substreams cancel
|
||
*
|
||
* @param maxSubstreams configures the maximum number of substreams (keys)
|
||
* that are supported; if more distinct keys are encountered then the stream fails
|
||
*/
|
||
def groupBy[K](maxSubstreams: Int, f: function.Function[Out, K]): SubFlow[In, Out @uncheckedVariance, Mat] =
|
||
new SubFlow(delegate.groupBy(maxSubstreams, f.apply))
|
||
|
||
/**
|
||
* This operation applies the given predicate to all incoming elements and
|
||
* emits them to a stream of output streams, always beginning a new one with
|
||
* the current element if the given predicate returns true for it. This means
|
||
* that for the following series of predicate values, three substreams will
|
||
* be produced with lengths 1, 2, and 3:
|
||
*
|
||
* {{{
|
||
* false, // element goes into first substream
|
||
* true, false, // elements go into second substream
|
||
* true, false, false // elements go into third substream
|
||
* }}}
|
||
*
|
||
* In case the *first* element of the stream matches the predicate, the first
|
||
* substream emitted by splitWhen will start from that element. For example:
|
||
*
|
||
* {{{
|
||
* true, false, false // first substream starts from the split-by element
|
||
* true, false // subsequent substreams operate the same way
|
||
* }}}
|
||
*
|
||
* The object returned from this method is not a normal [[Flow]],
|
||
* it is a [[SubFlow]]. This means that after this combinator all transformations
|
||
* are applied to all encountered substreams in the same fashion. Substream mode
|
||
* is exited either by closing the substream (i.e. connecting it to a [[Sink]])
|
||
* or by merging the substreams back together; see the `to` and `mergeBack` methods
|
||
* on [[SubFlow]] for more information.
|
||
*
|
||
* It is important to note that the substreams also propagate back-pressure as
|
||
* any other stream, which means that blocking one substream will block the `splitWhen`
|
||
* operator itself—and thereby all substreams—once all internal or
|
||
* explicit buffers are filled.
|
||
*
|
||
* If the split predicate `p` throws an exception and the supervision decision
|
||
* is [[akka.stream.Supervision#stop]] the stream and substreams will be completed
|
||
* with failure.
|
||
*
|
||
* If the split predicate `p` throws an exception and the supervision decision
|
||
* is [[akka.stream.Supervision#resume]] or [[akka.stream.Supervision#restart]]
|
||
* the element is dropped and the stream and substreams continue.
|
||
*
|
||
* '''Emits when''' an element for which the provided predicate is true, opening and emitting
|
||
* a new substream for subsequent element
|
||
*
|
||
* '''Backpressures when''' there is an element pending for the next substream, but the previous
|
||
* is not fully consumed yet, or the substream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels and substreams cancel on `SubstreamCancelStrategy.drain()`, downstream
|
||
* cancels or any substream cancels on `SubstreamCancelStrategy.propagate()`
|
||
*
|
||
* See also [[Flow.splitAfter]].
|
||
*/
|
||
def splitWhen(p: function.Predicate[Out]): SubFlow[In, Out, Mat] =
|
||
new SubFlow(delegate.splitWhen(p.test))
|
||
|
||
/**
|
||
* This operation applies the given predicate to all incoming elements and
|
||
* emits them to a stream of output streams, always beginning a new one with
|
||
* the current element if the given predicate returns true for it.
|
||
*
|
||
* @see [[#splitWhen]]
|
||
*/
|
||
def splitWhen(substreamCancelStrategy: SubstreamCancelStrategy)(p: function.Predicate[Out]): SubFlow[In, Out, Mat] =
|
||
new SubFlow(delegate.splitWhen(substreamCancelStrategy)(p.test))
|
||
|
||
/**
|
||
* This operation applies the given predicate to all incoming elements and
|
||
* emits them to a stream of output streams. It *ends* the current substream when the
|
||
* predicate is true. This means that for the following series of predicate values,
|
||
* three substreams will be produced with lengths 2, 2, and 3:
|
||
*
|
||
* {{{
|
||
* false, true, // elements go into first substream
|
||
* false, true, // elements go into second substream
|
||
* false, false, true // elements go into third substream
|
||
* }}}
|
||
*
|
||
* The object returned from this method is not a normal [[Flow]],
|
||
* it is a [[SubFlow]]. This means that after this combinator all transformations
|
||
* are applied to all encountered substreams in the same fashion. Substream mode
|
||
* is exited either by closing the substream (i.e. connecting it to a [[Sink]])
|
||
* or by merging the substreams back together; see the `to` and `mergeBack` methods
|
||
* on [[SubFlow]] for more information.
|
||
*
|
||
* It is important to note that the substreams also propagate back-pressure as
|
||
* any other stream, which means that blocking one substream will block the `splitAfter`
|
||
* operator itself—and thereby all substreams—once all internal or
|
||
* explicit buffers are filled.
|
||
*
|
||
* If the split predicate `p` throws an exception and the supervision decision
|
||
* is [[akka.stream.Supervision.Stop]] the stream and substreams will be completed
|
||
* with failure.
|
||
*
|
||
* If the split predicate `p` throws an exception and the supervision decision
|
||
* is [[akka.stream.Supervision.Resume]] or [[akka.stream.Supervision.Restart]]
|
||
* the element is dropped and the stream and substreams continue.
|
||
*
|
||
* '''Emits when''' an element passes through. When the provided predicate is true it emits the element
|
||
* and opens a new substream for subsequent element
|
||
*
|
||
* '''Backpressures when''' there is an element pending for the next substream, but the previous
|
||
* is not fully consumed yet, or the substream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels and substreams cancel on `SubstreamCancelStrategy.drain`, downstream
|
||
* cancels or any substream cancels on `SubstreamCancelStrategy.propagate`
|
||
*
|
||
* See also [[Flow.splitWhen]].
|
||
*/
|
||
def splitAfter[U >: Out](p: function.Predicate[Out]): SubFlow[In, Out, Mat] =
|
||
new SubFlow(delegate.splitAfter(p.test))
|
||
|
||
/**
|
||
* This operation applies the given predicate to all incoming elements and
|
||
* emits them to a stream of output streams. It *ends* the current substream when the
|
||
* predicate is true.
|
||
*
|
||
* @see [[#splitAfter]]
|
||
*/
|
||
def splitAfter(substreamCancelStrategy: SubstreamCancelStrategy)(p: function.Predicate[Out]): SubFlow[In, Out, Mat] =
|
||
new SubFlow(delegate.splitAfter(substreamCancelStrategy)(p.test))
|
||
|
||
/**
|
||
* Transform each input element into a `Source` of output elements that is
|
||
* then flattened into the output stream by concatenation,
|
||
* fully consuming one Source after the other.
|
||
*
|
||
* '''Emits when''' a currently consumed substream has an element available
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes and all consumed substreams complete
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def flatMapConcat[T, M](f: function.Function[Out, _ <: Graph[SourceShape[T], M]]): Flow[In, T, Mat] =
|
||
new Flow(delegate.flatMapConcat[T, M](x ⇒ f(x)))
|
||
|
||
/**
|
||
* Transform each input element into a `Source` of output elements that is
|
||
* then flattened into the output stream by merging, where at most `breadth`
|
||
* substreams are being consumed at any given time.
|
||
*
|
||
* '''Emits when''' a currently consumed substream has an element available
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes and all consumed substreams complete
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def flatMapMerge[T, M](breadth: Int, f: function.Function[Out, _ <: Graph[SourceShape[T], M]]): Flow[In, T, Mat] =
|
||
new Flow(delegate.flatMapMerge(breadth, o ⇒ f(o)))
|
||
|
||
/**
|
||
* Concatenate the given [[Source]] to this [[Flow]], meaning that once this
|
||
* Flow’s input is exhausted and all result elements have been generated,
|
||
* the Source’s elements will be produced.
|
||
*
|
||
* Note that the [[Source]] is materialized together with this Flow and just kept
|
||
* from producing elements by asserting back-pressure until its time comes.
|
||
*
|
||
* If this [[Flow]] gets upstream error - no elements from the given [[Source]] will be pulled.
|
||
*
|
||
* '''Emits when''' element is available from current stream or from the given [[Source]] when current is completed
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' given [[Source]] completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def concat[T >: Out, M](that: Graph[SourceShape[T], M]): javadsl.Flow[In, T, Mat] =
|
||
new Flow(delegate.concat(that))
|
||
|
||
/**
|
||
* Concatenate the given [[Source]] to this [[Flow]], meaning that once this
|
||
* Flow’s input is exhausted and all result elements have been generated,
|
||
* the Source’s elements will be produced.
|
||
*
|
||
* Note that the [[Source]] is materialized together with this Flow and just kept
|
||
* from producing elements by asserting back-pressure until its time comes.
|
||
*
|
||
* If this [[Flow]] gets upstream error - no elements from the given [[Source]] will be pulled.
|
||
*
|
||
* @see [[#concat]]
|
||
*/
|
||
def concatMat[T >: Out, M, M2](that: Graph[SourceShape[T], M], matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, T, M2] =
|
||
new Flow(delegate.concatMat(that)(combinerToScala(matF)))
|
||
|
||
/**
|
||
* Prepend the given [[Source]] to this [[Flow]], meaning that before elements
|
||
* are generated from this Flow, the Source's elements will be produced until it
|
||
* is exhausted, at which point Flow elements will start being produced.
|
||
*
|
||
* Note that this Flow will be materialized together with the [[Source]] and just kept
|
||
* from producing elements by asserting back-pressure until its time comes.
|
||
*
|
||
* If the given [[Source]] gets upstream error - no elements from this [[Flow]] will be pulled.
|
||
*
|
||
* '''Emits when''' element is available from the given [[Source]] or from current stream when the [[Source]] is completed
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' this [[Flow]] completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def prepend[T >: Out, M](that: Graph[SourceShape[T], M]): javadsl.Flow[In, T, Mat] =
|
||
new Flow(delegate.prepend(that))
|
||
|
||
/**
|
||
* Prepend the given [[Source]] to this [[Flow]], meaning that before elements
|
||
* are generated from this Flow, the Source's elements will be produced until it
|
||
* is exhausted, at which point Flow elements will start being produced.
|
||
*
|
||
* Note that this Flow will be materialized together with the [[Source]] and just kept
|
||
* from producing elements by asserting back-pressure until its time comes.
|
||
*
|
||
* If the given [[Source]] gets upstream error - no elements from this [[Flow]] will be pulled.
|
||
*
|
||
* @see [[#prepend]].
|
||
*/
|
||
def prependMat[T >: Out, M, M2](that: Graph[SourceShape[T], M], matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, T, M2] =
|
||
new Flow(delegate.prependMat(that)(combinerToScala(matF)))
|
||
|
||
/**
|
||
* Attaches the given [[Sink]] to this [[Flow]], meaning that elements that passes
|
||
* through will also be sent to the [[Sink]].
|
||
*
|
||
* '''Emits when''' element is available and demand exists both from the Sink and the downstream.
|
||
*
|
||
* '''Backpressures when''' downstream or Sink backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def alsoTo(that: Graph[SinkShape[Out], _]): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.alsoTo(that))
|
||
|
||
/**
|
||
* Attaches the given [[Sink]] to this [[Flow]], meaning that elements that passes
|
||
* through will also be sent to the [[Sink]].
|
||
*
|
||
* @see [[#alsoTo]]
|
||
*/
|
||
def alsoToMat[M2, M3](that: Graph[SinkShape[Out], M2],
|
||
matF: function.Function2[Mat, M2, M3]): javadsl.Flow[In, Out, M3] =
|
||
new Flow(delegate.alsoToMat(that)(combinerToScala(matF)))
|
||
|
||
/**
|
||
* Interleave is a deterministic merge of the given [[Source]] with elements of this [[Flow]].
|
||
* It first emits `segmentSize` number of elements from this flow to downstream, then - same amount for `that` source,
|
||
* then repeat process.
|
||
*
|
||
* Example:
|
||
* {{{
|
||
* Source<Integer, ?> src = Source.from(Arrays.asList(1, 2, 3))
|
||
* Flow<Integer, Integer, ?> flow = flow.interleave(Source.from(Arrays.asList(4, 5, 6, 7)), 2)
|
||
* src.via(flow) // 1, 2, 4, 5, 3, 6, 7
|
||
* }}}
|
||
*
|
||
* After one of upstreams is complete than all the rest elements will be emitted from the second one
|
||
*
|
||
* If this [[Flow]] or [[Source]] gets upstream error - stream completes with failure.
|
||
*
|
||
* '''Emits when''' element is available from the currently consumed upstream
|
||
*
|
||
* '''Backpressures when''' downstream backpressures. Signal to current
|
||
* upstream, switch to next upstream when received `segmentSize` elements
|
||
*
|
||
* '''Completes when''' the [[Flow]] and given [[Source]] completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def interleave[T >: Out](that: Graph[SourceShape[T], _], segmentSize: Int): javadsl.Flow[In, T, Mat] =
|
||
new Flow(delegate.interleave(that, segmentSize))
|
||
|
||
/**
|
||
* Interleave is a deterministic merge of the given [[Source]] with elements of this [[Flow]].
|
||
* It first emits `segmentSize` number of elements from this flow to downstream, then - same amount for `that` source,
|
||
* then repeat process.
|
||
*
|
||
* After one of upstreams is compete than all the rest elements will be emitted from the second one
|
||
*
|
||
* If this [[Flow]] or [[Source]] gets upstream error - stream completes with failure.
|
||
*
|
||
* @see [[#interleave]].
|
||
*/
|
||
def interleaveMat[T >: Out, M, M2](that: Graph[SourceShape[T], M], segmentSize: Int,
|
||
matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, T, M2] =
|
||
new Flow(delegate.interleaveMat(that, segmentSize)(combinerToScala(matF)))
|
||
|
||
/**
|
||
* Merge the given [[Source]] to this [[Flow]], taking elements as they arrive from input streams,
|
||
* picking randomly when several elements ready.
|
||
*
|
||
* '''Emits when''' one of the inputs has an element available
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' all upstreams complete
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def merge[T >: Out](that: Graph[SourceShape[T], _]): javadsl.Flow[In, T, Mat] =
|
||
merge(that, eagerComplete = false)
|
||
|
||
/**
|
||
* Merge the given [[Source]] to this [[Flow]], taking elements as they arrive from input streams,
|
||
* picking randomly when several elements ready.
|
||
*
|
||
* '''Emits when''' one of the inputs has an element available
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' all upstreams complete (eagerComplete=false) or one upstream completes (eagerComplete=true), default value is `false`
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def merge[T >: Out](that: Graph[SourceShape[T], _], eagerComplete: Boolean): javadsl.Flow[In, T, Mat] =
|
||
new Flow(delegate.merge(that, eagerComplete))
|
||
|
||
/**
|
||
* Merge the given [[Source]] to this [[Flow]], taking elements as they arrive from input streams,
|
||
* picking randomly when several elements ready.
|
||
*
|
||
* @see [[#merge]]
|
||
*/
|
||
def mergeMat[T >: Out, M, M2](that: Graph[SourceShape[T], M],
|
||
matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, T, M2] =
|
||
mergeMat(that, matF, eagerComplete = false)
|
||
|
||
/**
|
||
* Merge the given [[Source]] to this [[Flow]], taking elements as they arrive from input streams,
|
||
* picking randomly when several elements ready.
|
||
*
|
||
* @see [[#merge]]
|
||
*/
|
||
def mergeMat[T >: Out, M, M2](that: Graph[SourceShape[T], M],
|
||
matF: function.Function2[Mat, M, M2],
|
||
eagerComplete: Boolean): javadsl.Flow[In, T, M2] =
|
||
new Flow(delegate.mergeMat(that)(combinerToScala(matF)))
|
||
|
||
/**
|
||
* Merge the given [[Source]] to this [[Flow]], taking elements as they arrive from input streams,
|
||
* picking always the smallest of the available elements (waiting for one element from each side
|
||
* to be available). This means that possible contiguity of the input streams is not exploited to avoid
|
||
* waiting for elements, this merge will block when one of the inputs does not have more elements (and
|
||
* does not complete).
|
||
*
|
||
* '''Emits when''' all of the inputs have an element available
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' all upstreams complete
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def mergeSorted[U >: Out, M](that: Graph[SourceShape[U], M], comp: Comparator[U]): javadsl.Flow[In, U, Mat] =
|
||
new Flow(delegate.mergeSorted(that)(Ordering.comparatorToOrdering(comp)))
|
||
|
||
/**
|
||
* Merge the given [[Source]] to this [[Flow]], taking elements as they arrive from input streams,
|
||
* picking always the smallest of the available elements (waiting for one element from each side
|
||
* to be available). This means that possible contiguity of the input streams is not exploited to avoid
|
||
* waiting for elements, this merge will block when one of the inputs does not have more elements (and
|
||
* does not complete).
|
||
*
|
||
* @see [[#mergeSorted]].
|
||
*/
|
||
def mergeSortedMat[U >: Out, Mat2, Mat3](that: Graph[SourceShape[U], Mat2], comp: Comparator[U],
|
||
matF: function.Function2[Mat, Mat2, Mat3]): javadsl.Flow[In, U, Mat3] =
|
||
new Flow(delegate.mergeSortedMat(that)(combinerToScala(matF))(Ordering.comparatorToOrdering(comp)))
|
||
|
||
/**
|
||
* Combine the elements of current [[Flow]] and the given [[Source]] into a stream of tuples.
|
||
*
|
||
* '''Emits when''' all of the inputs have an element available
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' any upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def zip[T](source: Graph[SourceShape[T], _]): javadsl.Flow[In, Out @uncheckedVariance Pair T, Mat] =
|
||
zipMat(source, Keep.left)
|
||
|
||
/**
|
||
* Combine the elements of current [[Flow]] and the given [[Source]] into a stream of tuples.
|
||
*
|
||
* @see [[#zip]]
|
||
*/
|
||
def zipMat[T, M, M2](that: Graph[SourceShape[T], M],
|
||
matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, Out @uncheckedVariance Pair T, M2] =
|
||
this.viaMat(Flow.fromGraph(GraphDSL.create(that,
|
||
new function.Function2[GraphDSL.Builder[M], SourceShape[T], FlowShape[Out, Out @ uncheckedVariance Pair T]] {
|
||
def apply(b: GraphDSL.Builder[M], s: SourceShape[T]): FlowShape[Out, Out @uncheckedVariance Pair T] = {
|
||
val zip: FanInShape2[Out, T, Out Pair T] = b.add(Zip.create[Out, T])
|
||
b.from(s).toInlet(zip.in1)
|
||
FlowShape(zip.in0, zip.out)
|
||
}
|
||
})), matF)
|
||
|
||
/**
|
||
* Put together the elements of current [[Flow]] and the given [[Source]]
|
||
* into a stream of combined elements using a combiner function.
|
||
*
|
||
* '''Emits when''' all of the inputs have an element available
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' any upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def zipWith[Out2, Out3](that: Graph[SourceShape[Out2], _],
|
||
combine: function.Function2[Out, Out2, Out3]): javadsl.Flow[In, Out3, Mat] =
|
||
new Flow(delegate.zipWith[Out2, Out3](that)(combinerToScala(combine)))
|
||
|
||
/**
|
||
* Put together the elements of current [[Flow]] and the given [[Source]]
|
||
* into a stream of combined elements using a combiner function.
|
||
*
|
||
* @see [[#zipWith]]
|
||
*/
|
||
def zipWithMat[Out2, Out3, M, M2](that: Graph[SourceShape[Out2], M],
|
||
combine: function.Function2[Out, Out2, Out3],
|
||
matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, Out3, M2] =
|
||
new Flow(delegate.zipWithMat[Out2, Out3, M, M2](that)(combinerToScala(combine))(combinerToScala(matF)))
|
||
|
||
/**
|
||
* If the first element has not passed through this stage before the provided timeout, the stream is failed
|
||
* with a [[java.util.concurrent.TimeoutException]].
|
||
*
|
||
* '''Emits when''' upstream emits an element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes or fails if timeout elapses before first element arrives
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def initialTimeout(timeout: FiniteDuration): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.initialTimeout(timeout))
|
||
|
||
/**
|
||
* If the completion of the stream does not happen until the provided timeout, the stream is failed
|
||
* with a [[java.util.concurrent.TimeoutException]].
|
||
*
|
||
* '''Emits when''' upstream emits an element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes or fails if timeout elapses before upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def completionTimeout(timeout: FiniteDuration): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.completionTimeout(timeout))
|
||
|
||
/**
|
||
* If the time between two processed elements exceed the provided timeout, the stream is failed
|
||
* with a [[java.util.concurrent.TimeoutException]].
|
||
*
|
||
* '''Emits when''' upstream emits an element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes or fails if timeout elapses between two emitted elements
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def idleTimeout(timeout: FiniteDuration): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.idleTimeout(timeout))
|
||
|
||
/**
|
||
* Injects additional elements if the upstream does not emit for a configured amount of time. In other words, this
|
||
* stage attempts to maintains a base rate of emitted elements towards the downstream.
|
||
*
|
||
* If the downstream backpressures then no element is injected until downstream demand arrives. Injected elements
|
||
* do not accumulate during this period.
|
||
*
|
||
* Upstream elements are always preferred over injected elements.
|
||
*
|
||
* '''Emits when''' upstream emits an element or if the upstream was idle for the configured period
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def keepAlive[U >: Out](maxIdle: FiniteDuration, injectedElem: function.Creator[U]): javadsl.Flow[In, U, Mat] =
|
||
new Flow(delegate.keepAlive(maxIdle, () ⇒ injectedElem.create()))
|
||
|
||
/**
|
||
* Sends elements downstream with speed limited to `elements/per`. In other words, this stage set the maximum rate
|
||
* for emitting messages. This combinator works for streams where all elements have the same cost or length.
|
||
*
|
||
* Throttle implements the token bucket model. There is a bucket with a given token capacity (burst size or maximumBurst).
|
||
* Tokens drops into the bucket at a given rate and can be `spared` for later use up to bucket capacity
|
||
* to allow some burstiness. Whenever stream wants to send an element, it takes as many
|
||
* tokens from the bucket as number of elements. If there isn't any, throttle waits until the
|
||
* bucket accumulates enough tokens. Bucket is full when stream just materialized and started.
|
||
*
|
||
* Parameter `mode` manages behaviour when upstream is faster than throttle rate:
|
||
* - [[akka.stream.ThrottleMode.Shaping]] makes pauses before emitting messages to meet throttle rate
|
||
* - [[akka.stream.ThrottleMode.Enforcing]] fails with exception when upstream is faster than throttle rate
|
||
*
|
||
* '''Emits when''' upstream emits an element and configured time per each element elapsed
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def throttle(elements: Int, per: FiniteDuration, maximumBurst: Int,
|
||
mode: ThrottleMode): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.throttle(elements, per, maximumBurst, mode))
|
||
|
||
/**
|
||
* Sends elements downstream with speed limited to `cost/per`. Cost is
|
||
* calculating for each element individually by calling `calculateCost` function.
|
||
* This combinator works for streams when elements have different cost(length).
|
||
* Streams of `ByteString` for example.
|
||
*
|
||
* Throttle implements the token bucket model. There is a bucket with a given token capacity (burst size or maximumBurst).
|
||
* Tokens drops into the bucket at a given rate and can be `spared` for later use up to bucket capacity
|
||
* to allow some burstiness. Whenever stream wants to send an element, it takes as many
|
||
* tokens from the bucket as element cost. If there isn't any, throttle waits until the
|
||
* bucket accumulates enough tokens. Elements that costs more than the allowed burst will be delayed proportionally
|
||
* to their cost minus available tokens, meeting the target rate.
|
||
*
|
||
* Parameter `mode` manages behaviour when upstream is faster than throttle rate:
|
||
* - [[akka.stream.ThrottleMode.Shaping]] makes pauses before emitting messages to meet throttle rate
|
||
* - [[akka.stream.ThrottleMode.Enforcing]] fails with exception when upstream is faster than throttle rate. Enforcing
|
||
* cannot emit elements that cost more than the maximumBurst
|
||
*
|
||
* '''Emits when''' upstream emits an element and configured time per each element elapsed
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def throttle(cost: Int, per: FiniteDuration, maximumBurst: Int,
|
||
costCalculation: function.Function[Out, Integer], mode: ThrottleMode): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.throttle(cost, per, maximumBurst, costCalculation.apply, mode))
|
||
|
||
/**
|
||
* Detaches upstream demand from downstream demand without detaching the
|
||
* stream rates; in other words acts like a buffer of size 1.
|
||
*
|
||
* '''Emits when''' upstream emits an element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def detach: javadsl.Flow[In, Out, Mat] = new Flow(delegate.detach)
|
||
|
||
/**
|
||
* Materializes to `Future[Done]` that completes on getting termination message.
|
||
* The Future completes with success when received complete message from upstream or cancel
|
||
* from downstream. It fails with the same error when received error message from
|
||
* downstream.
|
||
*/
|
||
def watchTermination[M]()(matF: function.Function2[Mat, CompletionStage[Done], M]): javadsl.Flow[In, Out, M] =
|
||
new Flow(delegate.watchTermination()((left, right) ⇒ matF(left, right.toJava)))
|
||
|
||
/**
|
||
* Delays the initial element by the specified duration.
|
||
*
|
||
* '''Emits when''' upstream emits an element if the initial delay already elapsed
|
||
*
|
||
* '''Backpressures when''' downstream backpressures or initial delay not yet elapsed
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def initialDelay(delay: FiniteDuration): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.initialDelay(delay))
|
||
|
||
/**
|
||
* Change the attributes of this [[Source]] to the given ones and seal the list
|
||
* of attributes. This means that further calls will not be able to remove these
|
||
* attributes, but instead add new ones. Note that this
|
||
* operation has no effect on an empty Flow (because the attributes apply
|
||
* only to the contained processing stages).
|
||
*/
|
||
override def withAttributes(attr: Attributes): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.withAttributes(attr))
|
||
|
||
/**
|
||
* Add the given attributes to this Source. Further calls to `withAttributes`
|
||
* will not remove these attributes. Note that this
|
||
* operation has no effect on an empty Flow (because the attributes apply
|
||
* only to the contained processing stages).
|
||
*/
|
||
override def addAttributes(attr: Attributes): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.addAttributes(attr))
|
||
|
||
/**
|
||
* Add a ``name`` attribute to this Flow.
|
||
*/
|
||
override def named(name: String): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.named(name))
|
||
|
||
/**
|
||
* Put an asynchronous boundary around this `Flow`
|
||
*/
|
||
override def async: javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.async)
|
||
|
||
/**
|
||
* Logs elements flowing through the stream as well as completion and erroring.
|
||
*
|
||
* By default element and completion signals are logged on debug level, and errors are logged on Error level.
|
||
* This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow:
|
||
*
|
||
* The `extract` function will be applied to each element before logging, so it is possible to log only those fields
|
||
* of a complex object flowing through this element.
|
||
*
|
||
* Uses the given [[LoggingAdapter]] for logging.
|
||
*
|
||
* '''Emits when''' the mapping function returns an element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def log(name: String, extract: function.Function[Out, Any], log: LoggingAdapter): javadsl.Flow[In, Out, Mat] =
|
||
new Flow(delegate.log(name, e ⇒ extract.apply(e))(log))
|
||
|
||
/**
|
||
* Logs elements flowing through the stream as well as completion and erroring.
|
||
*
|
||
* By default element and completion signals are logged on debug level, and errors are logged on Error level.
|
||
* This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow:
|
||
*
|
||
* The `extract` function will be applied to each element before logging, so it is possible to log only those fields
|
||
* of a complex object flowing through this element.
|
||
*
|
||
* Uses an internally created [[LoggingAdapter]] which uses `akka.stream.Log` as it's source (use this class to configure slf4j loggers).
|
||
*
|
||
* '''Emits when''' the mapping function returns an element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def log(name: String, extract: function.Function[Out, Any]): javadsl.Flow[In, Out, Mat] =
|
||
this.log(name, extract, null)
|
||
|
||
/**
|
||
* Logs elements flowing through the stream as well as completion and erroring.
|
||
*
|
||
* By default element and completion signals are logged on debug level, and errors are logged on Error level.
|
||
* This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow:
|
||
*
|
||
* Uses the given [[LoggingAdapter]] for logging.
|
||
*
|
||
* '''Emits when''' the mapping function returns an element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def log(name: String, log: LoggingAdapter): javadsl.Flow[In, Out, Mat] =
|
||
this.log(name, ConstantFun.javaIdentityFunction[Out], log)
|
||
|
||
/**
|
||
* Logs elements flowing through the stream as well as completion and erroring.
|
||
*
|
||
* By default element and completion signals are logged on debug level, and errors are logged on Error level.
|
||
* This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow.
|
||
*
|
||
* Uses an internally created [[LoggingAdapter]] which uses `akka.stream.Log` as it's source (use this class to configure slf4j loggers).
|
||
*
|
||
* '''Emits when''' the mapping function returns an element
|
||
*
|
||
* '''Backpressures when''' downstream backpressures
|
||
*
|
||
* '''Completes when''' upstream completes
|
||
*
|
||
* '''Cancels when''' downstream cancels
|
||
*/
|
||
def log(name: String): javadsl.Flow[In, Out, Mat] =
|
||
this.log(name, ConstantFun.javaIdentityFunction[Out], null)
|
||
|
||
/**
|
||
* Converts this Flow to a [[RunnableGraph]] that materializes to a Reactive Streams [[org.reactivestreams.Processor]]
|
||
* which implements the operations encapsulated by this Flow. Every materialization results in a new Processor
|
||
* instance, i.e. the returned [[RunnableGraph]] is reusable.
|
||
*
|
||
* @return A [[RunnableGraph]] that materializes to a Processor when run() is called on it.
|
||
*/
|
||
def toProcessor: RunnableGraph[Processor[In @uncheckedVariance, Out @uncheckedVariance]] = {
|
||
RunnableGraph.fromGraph(delegate.toProcessor)
|
||
}
|
||
}
|
||
|
||
object RunnableGraph {
|
||
/**
|
||
* A graph with a closed shape is logically a runnable graph, this method makes
|
||
* it so also in type.
|
||
*/
|
||
def fromGraph[Mat](graph: Graph[ClosedShape, Mat]): RunnableGraph[Mat] =
|
||
graph match {
|
||
case r: RunnableGraph[Mat] ⇒ r
|
||
case other ⇒ new RunnableGraphAdapter[Mat](scaladsl.RunnableGraph.fromGraph(graph))
|
||
}
|
||
|
||
/** INTERNAL API */
|
||
private final class RunnableGraphAdapter[Mat](runnable: scaladsl.RunnableGraph[Mat]) extends RunnableGraph[Mat] {
|
||
def shape = ClosedShape
|
||
def module = runnable.module
|
||
override def mapMaterializedValue[Mat2](f: function.Function[Mat, Mat2]): RunnableGraphAdapter[Mat2] =
|
||
new RunnableGraphAdapter(runnable.mapMaterializedValue(f.apply _))
|
||
|
||
override def run(materializer: Materializer): Mat = runnable.run()(materializer)
|
||
|
||
override def withAttributes(attr: Attributes): RunnableGraphAdapter[Mat] = {
|
||
val newRunnable = runnable.withAttributes(attr)
|
||
if (newRunnable eq runnable) this
|
||
else new RunnableGraphAdapter(newRunnable)
|
||
}
|
||
|
||
override def named(name: String): RunnableGraphAdapter[Mat] = {
|
||
val newRunnable = runnable.named(name)
|
||
if (newRunnable eq runnable) this
|
||
else new RunnableGraphAdapter(newRunnable)
|
||
}
|
||
}
|
||
}
|
||
/**
|
||
* Java API
|
||
*
|
||
* Flow with attached input and output, can be executed.
|
||
*/
|
||
abstract class RunnableGraph[+Mat] extends Graph[ClosedShape, Mat] {
|
||
|
||
/**
|
||
* Run this flow and return the materialized values of the flow.
|
||
*/
|
||
def run(materializer: Materializer): Mat
|
||
/**
|
||
* Transform only the materialized value of this RunnableGraph, leaving all other properties as they were.
|
||
*/
|
||
def mapMaterializedValue[Mat2](f: function.Function[Mat, Mat2]): RunnableGraph[Mat2]
|
||
}
|