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pekko/akka-stream/src/main/scala/akka/stream/javadsl/Flow.scala
Konrad `ktoso` Malawski cceb184098 =str javadoc rewording on new wireTap method
2018-04-25 12:44:47 +09:00

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/**
* Copyright (C) 2014-2018 Lightbend Inc. <https://www.lightbend.com>
*/
package akka.stream.javadsl
import akka.util.{ ConstantFun, Timeout }
import akka.{ Done, NotUsed }
import akka.event.LoggingAdapter
import akka.japi.{ Pair, function }
import akka.stream._
import org.reactivestreams.Processor
import scala.concurrent.duration.FiniteDuration
import akka.japi.Util
import java.util.{ Comparator, Optional }
import java.util.concurrent.CompletionStage
import akka.util.JavaDurationConverters._
import akka.actor.ActorRef
import akka.dispatch.ExecutionContexts
import akka.stream.impl.fusing.LazyFlow
import scala.annotation.unchecked.uncheckedVariance
import scala.compat.java8.FutureConverters._
import scala.reflect.ClassTag
object Flow {
/** Create a `Flow` which can process elements of type `T`. */
def create[T](): javadsl.Flow[T, T, NotUsed] = fromGraph(scaladsl.Flow[T])
def fromProcessor[I, O](processorFactory: function.Creator[Processor[I, O]]): javadsl.Flow[I, O, NotUsed] =
new Flow(scaladsl.Flow.fromProcessor(() ⇒ processorFactory.create()))
def fromProcessorMat[I, O, Mat](processorFactory: function.Creator[Pair[Processor[I, O], Mat]]): javadsl.Flow[I, O, Mat] =
new Flow(scaladsl.Flow.fromProcessorMat { () ⇒
val javaPair = processorFactory.create()
(javaPair.first, javaPair.second)
})
/**
* Creates a [Flow] which will use the given function to transform its inputs to outputs. It is equivalent
* to `Flow.create[T].map(f)`
*/
def fromFunction[I, O](f: function.Function[I, O]): javadsl.Flow[I, O, NotUsed] =
Flow.create[I]().map(f)
/** Create a `Flow` which can process elements of type `T`. */
def of[T](clazz: Class[T]): javadsl.Flow[T, T, NotUsed] = create[T]()
/**
* A graph with the shape of a flow logically is a flow, this method makes it so also in type.
*/
def fromGraph[I, O, M](g: Graph[FlowShape[I, O], M]): Flow[I, O, M] =
g match {
case f: Flow[I, O, M] ⇒ f
case other ⇒ new Flow(scaladsl.Flow.fromGraph(other))
}
/**
* Creates a `Flow` from a `Sink` and a `Source` where the Flow's input
* will be sent to the Sink and the Flow's output will come from the Source.
*
* The resulting flow can be visualized as:
* {{{
* +----------------------------------------------+
* | Resulting Flow[I, O, NotUsed] |
* | |
* | +---------+ +-----------+ |
* | | | | | |
* I ~~> | Sink[I] | [no-connection!] | Source[O] | ~~> O
* | | | | | |
* | +---------+ +-----------+ |
* +----------------------------------------------+
* }}}
*
* The completion of the Sink and Source sides of a Flow constructed using
* this method are independent. So if the Sink receives a completion signal,
* the Source side will remain unaware of that. If you are looking to couple
* the termination signals of the two sides use `Flow.fromSinkAndSourceCoupled` instead.
*
* See also [[fromSinkAndSourceMat]] when access to materialized values of the parameters is needed.
*/
def fromSinkAndSource[I, O](sink: Graph[SinkShape[I], _], source: Graph[SourceShape[O], _]): Flow[I, O, NotUsed] =
new Flow(scaladsl.Flow.fromSinkAndSourceMat(sink, source)(scaladsl.Keep.none))
/**
* Creates a `Flow` from a `Sink` and a `Source` where the Flow's input
* will be sent to the Sink and the Flow's output will come from the Source.
*
* The resulting flow can be visualized as:
* {{{
* +-------------------------------------------------------+
* | Resulting Flow[I, O, M] |
* | |
* | +-------------+ +---------------+ |
* | | | | | |
* I ~~> | Sink[I, M1] | [no-connection!] | Source[O, M2] | ~~> O
* | | | | | |
* | +-------------+ +---------------+ |
* +------------------------------------------------------+
* }}}
*
* The completion of the Sink and Source sides of a Flow constructed using
* this method are independent. So if the Sink receives a completion signal,
* the Source side will remain unaware of that. If you are looking to couple
* the termination signals of the two sides use `Flow.fromSinkAndSourceCoupledMat` instead.
*
* The `combine` function is used to compose the materialized values of the `sink` and `source`
* into the materialized value of the resulting [[Flow]].
*/
def fromSinkAndSourceMat[I, O, M1, M2, M](
sink: Graph[SinkShape[I], M1], source: Graph[SourceShape[O], M2],
combine: function.Function2[M1, M2, M]): Flow[I, O, M] =
new Flow(scaladsl.Flow.fromSinkAndSourceMat(sink, source)(combinerToScala(combine)))
/**
* Allows coupling termination (cancellation, completion, erroring) of Sinks and Sources while creating a Flow from them.
* Similar to [[Flow.fromSinkAndSource]] however couples the termination of these two stages.
*
* The resulting flow can be visualized as:
* {{{
* +---------------------------------------------+
* | Resulting Flow[I, O, NotUsed] |
* | |
* | +---------+ +-----------+ |
* | | | | | |
* I ~~> | Sink[I] | ~~~(coupled)~~~ | Source[O] | ~~> O
* | | | | | |
* | +---------+ +-----------+ |
* +---------------------------------------------+
* }}}
*
* E.g. if the emitted [[Flow]] gets a cancellation, the [[Source]] of course is cancelled,
* however the Sink will also be completed. The table below illustrates the effects in detail:
*
* <table>
* <tr>
* <th>Returned Flow</th>
* <th>Sink (<code>in</code>)</th>
* <th>Source (<code>out</code>)</th>
* </tr>
* <tr>
* <td><i>cause:</i> upstream (sink-side) receives completion</td>
* <td><i>effect:</i> receives completion</td>
* <td><i>effect:</i> receives cancel</td>
* </tr>
* <tr>
* <td><i>cause:</i> upstream (sink-side) receives error</td>
* <td><i>effect:</i> receives error</td>
* <td><i>effect:</i> receives cancel</td>
* </tr>
* <tr>
* <td><i>cause:</i> downstream (source-side) receives cancel</td>
* <td><i>effect:</i> completes</td>
* <td><i>effect:</i> receives cancel</td>
* </tr>
* <tr>
* <td><i>effect:</i> cancels upstream, completes downstream</td>
* <td><i>effect:</i> completes</td>
* <td><i>cause:</i> signals complete</td>
* </tr>
* <tr>
* <td><i>effect:</i> cancels upstream, errors downstream</td>
* <td><i>effect:</i> receives error</td>
* <td><i>cause:</i> signals error or throws</td>
* </tr>
* <tr>
* <td><i>effect:</i> cancels upstream, completes downstream</td>
* <td><i>cause:</i> cancels</td>
* <td><i>effect:</i> receives cancel</td>
* </tr>
* </table>
*
* See also [[fromSinkAndSourceCoupledMat]] when access to materialized values of the parameters is needed.
*/
def fromSinkAndSourceCoupled[I, O](sink: Graph[SinkShape[I], _], source: Graph[SourceShape[O], _]): Flow[I, O, NotUsed] =
new Flow(scaladsl.Flow.fromSinkAndSourceCoupled(sink, source))
/**
* Allows coupling termination (cancellation, completion, erroring) of Sinks and Sources while creating a Flow from them.
* Similar to [[Flow.fromSinkAndSource]] however couples the termination of these two stages.
*
* The resulting flow can be visualized as:
* {{{
* +-----------------------------------------------------+
* | Resulting Flow[I, O, M] |
* | |
* | +-------------+ +---------------+ |
* | | | | | |
* I ~~> | Sink[I, M1] | ~~~(coupled)~~~ | Source[O, M2] | ~~> O
* | | | | | |
* | +-------------+ +---------------+ |
* +-----------------------------------------------------+
* }}}
*
* E.g. if the emitted [[Flow]] gets a cancellation, the [[Source]] of course is cancelled,
* however the Sink will also be completed. The table on [[Flow.fromSinkAndSourceCoupled]]
* illustrates the effects in detail.
*
* The `combine` function is used to compose the materialized values of the `sink` and `source`
* into the materialized value of the resulting [[Flow]].
*/
def fromSinkAndSourceCoupledMat[I, O, M1, M2, M](
sink: Graph[SinkShape[I], M1], source: Graph[SourceShape[O], M2],
combine: function.Function2[M1, M2, M]): Flow[I, O, M] =
new Flow(scaladsl.Flow.fromSinkAndSourceCoupledMat(sink, source)(combinerToScala(combine)))
/**
* Creates a real `Flow` upon receiving the first element. Internal `Flow` will not be created
* if there are no elements, because of completion, cancellation, or error.
*
* The materialized value of the `Flow` is the value that is created by the `fallback` function.
*
* '''Emits when''' the internal flow is successfully created and it emits
*
* '''Backpressures when''' the internal flow is successfully created and it backpressures
*
* '''Completes when''' upstream completes and all elements have been emitted from the internal flow
*
* '''Cancels when''' downstream cancels
*/
@Deprecated
@deprecated("Use lazyInitAsync instead. (lazyInitAsync returns a flow with a more useful materialized value.)", "2.5.12")
def lazyInit[I, O, M](flowFactory: function.Function[I, CompletionStage[Flow[I, O, M]]], fallback: function.Creator[M]): Flow[I, O, M] = {
import scala.compat.java8.FutureConverters._
val sflow = scaladsl.Flow
.fromGraph(new LazyFlow[I, O, M](t ⇒ flowFactory.apply(t).toScala.map(_.asScala)(ExecutionContexts.sameThreadExecutionContext)))
.mapMaterializedValue(_ ⇒ fallback.create())
new Flow(sflow)
}
/**
* Creates a real `Flow` upon receiving the first element. Internal `Flow` will not be created
* if there are no elements, because of completion, cancellation, or error.
*
* The materialized value of the `Flow` is a `Future[Option[M]]` that is completed with `Some(mat)` when the internal
* flow gets materialized or with `None` when there where no elements. If the flow materialization (including
* the call of the `flowFactory`) fails then the future is completed with a failure.
*
* '''Emits when''' the internal flow is successfully created and it emits
*
* '''Backpressures when''' the internal flow is successfully created and it backpressures
*
* '''Completes when''' upstream completes and all elements have been emitted from the internal flow
*
* '''Cancels when''' downstream cancels
*/
def lazyInitAsync[I, O, M](flowFactory: function.Creator[CompletionStage[Flow[I, O, M]]]): Flow[I, O, CompletionStage[Optional[M]]] = {
import scala.compat.java8.FutureConverters._
val sflow = scaladsl.Flow.lazyInitAsync(() ⇒ flowFactory.create().toScala.map(_.asScala)(ExecutionContexts.sameThreadExecutionContext))
.mapMaterializedValue(fut ⇒ fut.map(_.fold[Optional[M]](Optional.empty())(m ⇒ Optional.ofNullable(m)))(ExecutionContexts.sameThreadExecutionContext).toJava)
new Flow(sflow)
}
/**
* Upcast a stream of elements to a stream of supertypes of that element. Useful in combination with
* fan-in combinators where you do not want to pay the cost of casting each element in a `map`.
*
* @tparam SuperOut a supertype to the type of element flowing out of the flow
* @return A flow that accepts `In` and outputs elements of the super type
*/
def upcast[In, SuperOut, Out <: SuperOut, M](flow: Flow[In, Out, M]): Flow[In, SuperOut, M] =
flow.asInstanceOf[Flow[In, SuperOut, M]]
}
/** Create a `Flow` which can process elements of type `T`. */
final class Flow[In, Out, Mat](delegate: scaladsl.Flow[In, Out, Mat]) extends Graph[FlowShape[In, Out], Mat] {
import scala.collection.JavaConverters._
override def shape: FlowShape[In, Out] = delegate.shape
override def traversalBuilder = delegate.traversalBuilder
override def toString: String = delegate.toString
/** Converts this Flow to its Scala DSL counterpart */
def asScala: scaladsl.Flow[In, Out, Mat] = delegate
/**
* Transform only the materialized value of this Flow, leaving all other properties as they were.
*/
def mapMaterializedValue[Mat2](f: function.Function[Mat, Mat2]): Flow[In, Out, Mat2] =
new Flow(delegate.mapMaterializedValue(f.apply _))
/**
* Transform this [[Flow]] by appending the given processing steps.
* {{{
* +---------------------------------+
* | Resulting Flow[In, T, Mat] |
* | |
* | +------+ +------+ |
* | | | | | |
* In ~~> | this | ~~Out~~> | flow | ~~> T
* | | Mat| | M| |
* | +------+ +------+ |
* +---------------------------------+
* }}}
* The materialized value of the combined [[Flow]] will be the materialized
* value of the current flow (ignoring the other Flow’s value), use
* `viaMat` if a different strategy is needed.
*
* See also [[viaMat]] when access to materialized values of the parameter is needed.
*/
def via[T, M](flow: Graph[FlowShape[Out, T], M]): javadsl.Flow[In, T, Mat] =
new Flow(delegate.via(flow))
/**
* Transform this [[Flow]] by appending the given processing steps.
* {{{
* +---------------------------------+
* | Resulting Flow[In, T, M2] |
* | |
* | +------+ +------+ |
* | | | | | |
* In ~~> | this | ~~Out~~> | flow | ~~> T
* | | Mat| | M| |
* | +------+ +------+ |
* +---------------------------------+
* }}}
* The `combine` function is used to compose the materialized values of this flow and that
* flow into the materialized value of the resulting Flow.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*/
def viaMat[T, M, M2](flow: Graph[FlowShape[Out, T], M], combine: function.Function2[Mat, M, M2]): javadsl.Flow[In, T, M2] =
new Flow(delegate.viaMat(flow)(combinerToScala(combine)))
/**
* Connect this [[Flow]] to a [[Sink]], concatenating the processing steps of both.
* {{{
* +------------------------------+
* | Resulting Sink[In, Mat] |
* | |
* | +------+ +------+ |
* | | | | | |
* In ~~> | flow | ~~Out~~> | sink | |
* | | Mat| | M| |
* | +------+ +------+ |
* +------------------------------+
* }}}
* The materialized value of the combined [[Sink]] will be the materialized
* value of the current flow (ignoring the given Sink’s value), use
* `toMat` if a different strategy is needed.
*
* See also [[toMat]] when access to materialized values of the parameter is needed.
*/
def to(sink: Graph[SinkShape[Out], _]): javadsl.Sink[In, Mat] =
new Sink(delegate.to(sink))
/**
* Connect this [[Flow]] to a [[Sink]], concatenating the processing steps of both.
* {{{
* +----------------------------+
* | Resulting Sink[In, M2] |
* | |
* | +------+ +------+ |
* | | | | | |
* In ~~> | flow | ~Out~> | sink | |
* | | Mat| | M| |
* | +------+ +------+ |
* +----------------------------+
* }}}
* The `combine` function is used to compose the materialized values of this flow and that
* Sink into the materialized value of the resulting Sink.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*/
def toMat[M, M2](sink: Graph[SinkShape[Out], M], combine: function.Function2[Mat, M, M2]): javadsl.Sink[In, M2] =
new Sink(delegate.toMat(sink)(combinerToScala(combine)))
/**
* Join this [[Flow]] to another [[Flow]], by cross connecting the inputs and outputs, creating a [[RunnableGraph]].
* {{{
* +------+ +-------+
* | | ~Out~> | |
* | this | | other |
* | | <~In~ | |
* +------+ +-------+
* }}}
* The materialized value of the combined [[Flow]] will be the materialized
* value of the current flow (ignoring the other Flow’s value), use
* `joinMat` if a different strategy is needed.
*
* See also [[joinMat]] when access to materialized values of the parameter is needed.
*/
def join[M](flow: Graph[FlowShape[Out, In], M]): javadsl.RunnableGraph[Mat] =
RunnableGraph.fromGraph(delegate.join(flow))
/**
* Join this [[Flow]] to another [[Flow]], by cross connecting the inputs and outputs, creating a [[RunnableGraph]]
* {{{
* +------+ +-------+
* | | ~Out~> | |
* | this | | other |
* | | <~In~ | |
* +------+ +-------+
* }}}
* The `combine` function is used to compose the materialized values of this flow and that
* Flow into the materialized value of the resulting Flow.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*/
def joinMat[M, M2](flow: Graph[FlowShape[Out, In], M], combine: function.Function2[Mat, M, M2]): javadsl.RunnableGraph[M2] =
RunnableGraph.fromGraph(delegate.joinMat(flow)(combinerToScala(combine)))
/**
* Join this [[Flow]] to a [[BidiFlow]] to close off the “top” of the protocol stack:
* {{{
* +---------------------------+
* | Resulting Flow |
* | |
* | +------+ +------+ |
* | | | ~Out~> | | ~~> O2
* | | flow | | bidi | |
* | | | <~In~ | | <~~ I2
* | +------+ +------+ |
* +---------------------------+
* }}}
* The materialized value of the combined [[Flow]] will be the materialized
* value of the current flow (ignoring the [[BidiFlow]]’s value), use
* [[Flow#joinMat[I2* joinMat]] if a different strategy is needed.
*/
def join[I2, O2, Mat2](bidi: Graph[BidiShape[Out, O2, I2, In], Mat2]): Flow[I2, O2, Mat] =
new Flow(delegate.join(bidi))
/**
* Join this [[Flow]] to a [[BidiFlow]] to close off the “top” of the protocol stack:
* {{{
* +---------------------------+
* | Resulting Flow |
* | |
* | +------+ +------+ |
* | | | ~Out~> | | ~~> O2
* | | flow | | bidi | |
* | | | <~In~ | | <~~ I2
* | +------+ +------+ |
* +---------------------------+
* }}}
* The `combine` function is used to compose the materialized values of this flow and that
* [[BidiFlow]] into the materialized value of the resulting [[Flow]].
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* See also [[viaMat]] when access to materialized values of the parameter is needed.
*/
def joinMat[I2, O2, Mat2, M](bidi: Graph[BidiShape[Out, O2, I2, In], Mat2], combine: function.Function2[Mat, Mat2, M]): Flow[I2, O2, M] =
new Flow(delegate.joinMat(bidi)(combinerToScala(combine)))
/**
* Connect the `Source` to this `Flow` and then connect it to the `Sink` and run it.
*
* The returned tuple contains the materialized values of the `Source` and `Sink`,
* e.g. the `Subscriber` of a `Source.asSubscriber` and `Publisher` of a `Sink.asPublisher`.
*
* @tparam T materialized type of given Source
* @tparam U materialized type of given Sink
*/
def runWith[T, U](source: Graph[SourceShape[In], T], sink: Graph[SinkShape[Out], U], materializer: Materializer): akka.japi.Pair[T, U] = {
val (som, sim) = delegate.runWith(source, sink)(materializer)
akka.japi.Pair(som, sim)
}
/**
* Transform this stream by applying the given function to each of the elements
* as they pass through this processing step.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the mapping function returns an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def map[T](f: function.Function[Out, T]): javadsl.Flow[In, T, Mat] =
new Flow(delegate.map(f.apply))
/**
* This is a simplified version of `wireTap(Sink)` that takes only a simple procedure.
* Elements will be passed into this "side channel" function, and any of its results will be ignored.
*
* This operation is useful for inspecting the passed through element, usually by means of side-effecting
* operations (such as `println`, or emitting metrics), for each element without having to modify it.
*
* For logging signals (elements, completion, error) consider using the [[log]] stage instead,
* along with appropriate `ActorAttributes.logLevels`.
*
* '''Emits when''' upstream emits an element; the same element will be passed to the attached function,
* as well as to the downstream stage
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
*/
def wireTap(f: function.Procedure[Out]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.wireTap(f(_)))
/**
* Transform each input element into an `Iterable` of output elements that is
* then flattened into the output stream.
*
* Make sure that the `Iterable` is immutable or at least not modified after
* being used as an output sequence. Otherwise the stream may fail with
* `ConcurrentModificationException` or other more subtle errors may occur.
*
* The returned `Iterable` MUST NOT contain `null` values,
* as they are illegal as stream elements - according to the Reactive Streams specification.
*
* '''Emits when''' the mapping function returns an element or there are still remaining elements
* from the previously calculated collection
*
* '''Backpressures when''' downstream backpressures or there are still remaining elements from the
* previously calculated collection
*
* '''Completes when''' upstream completes and all remaining elements have been emitted
*
* '''Cancels when''' downstream cancels
*/
def mapConcat[T](f: function.Function[Out, java.lang.Iterable[T]]): javadsl.Flow[In, T, Mat] =
new Flow(delegate.mapConcat { elem ⇒ Util.immutableSeq(f(elem)) })
/**
* Transform each input element into an `Iterable` of output elements that is
* then flattened into the output stream. The transformation is meant to be stateful,
* which is enabled by creating the transformation function anew for every materialization —
* the returned function will typically close over mutable objects to store state between
* invocations. For the stateless variant see [[#mapConcat]].
*
* Make sure that the `Iterable` is immutable or at least not modified after
* being used as an output sequence. Otherwise the stream may fail with
* `ConcurrentModificationException` or other more subtle errors may occur.
*
* The returned `Iterable` MUST NOT contain `null` values,
* as they are illegal as stream elements - according to the Reactive Streams specification.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the mapping function returns an element or there are still remaining elements
* from the previously calculated collection
*
* '''Backpressures when''' downstream backpressures or there are still remaining elements from the
* previously calculated collection
*
* '''Completes when''' upstream completes and all remaining elements has been emitted
*
* '''Cancels when''' downstream cancels
*/
def statefulMapConcat[T](f: function.Creator[function.Function[Out, java.lang.Iterable[T]]]): javadsl.Flow[In, T, Mat] =
new Flow(delegate.statefulMapConcat { () ⇒
val fun = f.create()
elem ⇒ Util.immutableSeq(fun(elem))
})
/**
* Transform this stream by applying the given function to each of the elements
* as they pass through this processing step. The function returns a `CompletionStage` and the
* value of that future will be emitted downstream. The number of CompletionStages
* that shall run in parallel is given as the first argument to ``mapAsync``.
* These CompletionStages may complete in any order, but the elements that
* are emitted downstream are in the same order as received from upstream.
*
* If the function `f` throws an exception or if the `CompletionStage` is completed
* with failure and the supervision decision is [[akka.stream.Supervision#stop]]
* the stream will be completed with failure.
*
* If the function `f` throws an exception or if the `CompletionStage` is completed
* with failure and the supervision decision is [[akka.stream.Supervision#resume]] or
* [[akka.stream.Supervision#restart]] the element is dropped and the stream continues.
*
* The function `f` is always invoked on the elements in the order they arrive.
*
* '''Emits when''' the CompletionStage returned by the provided function finishes for the next element in sequence
*
* '''Backpressures when''' the number of CompletionStages reaches the configured parallelism and the downstream
* backpressures or the first future is not completed
*
* '''Completes when''' upstream completes and all CompletionStages have been completed and all elements have been emitted
*
* '''Cancels when''' downstream cancels
*
* @see [[#mapAsyncUnordered]]
*/
def mapAsync[T](parallelism: Int, f: function.Function[Out, CompletionStage[T]]): javadsl.Flow[In, T, Mat] =
new Flow(delegate.mapAsync(parallelism)(x ⇒ f(x).toScala))
/**
* Transform this stream by applying the given function to each of the elements
* as they pass through this processing step. The function returns a `CompletionStage` and the
* value of that future will be emitted downstream. The number of CompletionStages
* that shall run in parallel is given as the first argument to ``mapAsyncUnordered``.
* Each processed element will be emitted downstream as soon as it is ready, i.e. it is possible
* that the elements are not emitted downstream in the same order as received from upstream.
*
* If the function `f` throws an exception or if the `CompletionStage` is completed
* with failure and the supervision decision is [[akka.stream.Supervision#stop]]
* the stream will be completed with failure.
*
* If the function `f` throws an exception or if the `CompletionStage` is completed
* with failure and the supervision decision is [[akka.stream.Supervision#resume]] or
* [[akka.stream.Supervision#restart]] the element is dropped and the stream continues.
*
* The function `f` is always invoked on the elements in the order they arrive (even though the result of the futures
* returned by `f` might be emitted in a different order).
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' any of the CompletionStages returned by the provided function complete
*
* '''Backpressures when''' the number of CompletionStages reaches the configured parallelism and the downstream backpressures
*
* '''Completes when''' upstream completes and all CompletionStages have been completed and all elements have been emitted
*
* '''Cancels when''' downstream cancels
*
* @see [[#mapAsync]]
*/
def mapAsyncUnordered[T](parallelism: Int, f: function.Function[Out, CompletionStage[T]]): javadsl.Flow[In, T, Mat] =
new Flow(delegate.mapAsyncUnordered(parallelism)(x ⇒ f(x).toScala))
/**
* Use the `ask` pattern to send a request-reply message to the target `ref` actor.
* If any of the asks times out it will fail the stream with a [[akka.pattern.AskTimeoutException]].
*
* The `mapTo` class parameter is used to cast the incoming responses to the expected response type.
*
* Similar to the plain ask pattern, the target actor is allowed to reply with `akka.util.Status`.
* An `akka.util.Status#Failure` will cause the stage to fail with the cause carried in the `Failure` message.
*
* Defaults to parallelism of 2 messages in flight, since while one ask message may be being worked on, the second one
* still be in the mailbox, so defaulting to sending the second one a bit earlier than when first ask has replied maintains
* a slightly healthier throughput.
*
* The stage fails with an [[akka.stream.WatchedActorTerminatedException]] if the target actor is terminated.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' any of the CompletionStages returned by the provided function complete
*
* '''Backpressures when''' the number of futures reaches the configured parallelism and the downstream backpressures
*
* '''Completes when''' upstream completes and all futures have been completed and all elements have been emitted
*
* '''Fails when''' the passed in actor terminates, or a timeout is exceeded in any of the asks performed
*
* '''Cancels when''' downstream cancels
*/
def ask[S](ref: ActorRef, mapTo: Class[S], timeout: Timeout): javadsl.Flow[In, S, Mat] =
ask(2, ref, mapTo, timeout)
/**
* Use the `ask` pattern to send a request-reply message to the target `ref` actor.
* If any of the asks times out it will fail the stream with a [[akka.pattern.AskTimeoutException]].
*
* The `mapTo` class parameter is used to cast the incoming responses to the expected response type.
*
* Similar to the plain ask pattern, the target actor is allowed to reply with `akka.util.Status`.
* An `akka.util.Status#Failure` will cause the stage to fail with the cause carried in the `Failure` message.
*
* Parallelism limits the number of how many asks can be "in flight" at the same time.
* Please note that the elements emitted by this stage are in-order with regards to the asks being issued
* (i.e. same behaviour as mapAsync).
*
* The stage fails with an [[akka.stream.WatchedActorTerminatedException]] if the target actor is terminated.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' any of the CompletionStages returned by the provided function complete
*
* '''Backpressures when''' the number of futures reaches the configured parallelism and the downstream backpressures
*
* '''Completes when''' upstream completes and all futures have been completed and all elements have been emitted
*
* '''Fails when''' the passed in actor terminates, or a timeout is exceeded in any of the asks performed
*
* '''Cancels when''' downstream cancels
*/
def ask[S](parallelism: Int, ref: ActorRef, mapTo: Class[S], timeout: Timeout): javadsl.Flow[In, S, Mat] =
new Flow(delegate.ask[S](parallelism)(ref)(timeout, ClassTag(mapTo)))
/**
* The stage fails with an [[akka.stream.WatchedActorTerminatedException]] if the target actor is terminated.
*
* '''Emits when''' upstream emits
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Fails when''' the watched actor terminates
*
* '''Cancels when''' downstream cancels
*/
def watch(ref: ActorRef): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.watch(ref))
/**
* Only pass on those elements that satisfy the given predicate.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the given predicate returns true for the element
*
* '''Backpressures when''' the given predicate returns true for the element and downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
*/
def filter(p: function.Predicate[Out]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.filter(p.test))
/**
* Only pass on those elements that NOT satisfy the given predicate.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the given predicate returns false for the element
*
* '''Backpressures when''' the given predicate returns false for the element and downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def filterNot(p: function.Predicate[Out]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.filterNot(p.test))
/**
* Transform this stream by applying the given partial function to each of the elements
* on which the function is defined as they pass through this processing step.
* Non-matching elements are filtered out.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the provided partial function is defined for the element
*
* '''Backpressures when''' the partial function is defined for the element and downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def collect[T](pf: PartialFunction[Out, T]): javadsl.Flow[In, T, Mat] =
new Flow(delegate.collect(pf))
/**
* Transform this stream by testing the type of each of the elements
* on which the element is an instance of the provided type as they pass through this processing step.
* Non-matching elements are filtered out.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the element is an instance of the provided type
*
* '''Backpressures when''' the element is an instance of the provided type and downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def collectType[T](clazz: Class[T]): javadsl.Flow[In, T, Mat] =
new Flow(delegate.collectType[T](ClassTag[T](clazz)))
/**
* Chunk up this stream into groups of the given size, with the last group
* possibly smaller than requested due to end-of-stream.
*
* `n` must be positive, otherwise IllegalArgumentException is thrown.
*
* '''Emits when''' the specified number of elements has been accumulated or upstream completed
*
* '''Backpressures when''' a group has been assembled and downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def grouped(n: Int): javadsl.Flow[In, java.util.List[Out], 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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''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, java.lang.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], 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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''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 with a asynchronous function,
* emits its current value which starts at `zero` and then
* applies the current and next value to the given function `f`,
* emitting a `Future` that resolves to 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.
*
* If the function `f` throws an exception and the supervision decision is
* [[akka.stream.Supervision.Resume]] current value starts at the previous
* current value, or zero when it doesn't have one, and the stream will continue.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the future returned by f` completes
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes and the last future returned by `f` completes
*
* '''Cancels when''' downstream cancels
*
* See also [[FlowOps.scan]]
*/
def scanAsync[T](zero: T)(f: function.Function2[T, Out, CompletionStage[T]]): javadsl.Flow[In, T, Mat] =
new Flow(delegate.scanAsync(zero) { (out, in) ⇒ f(out, in).toScala })
/**
* 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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* 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 with an asynchronous function.
* Applies the given function towards its current and next value,
* yielding the next current value.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* If the function `f` returns a failure 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 foldAsync[T](zero: T)(f: function.Function2[T, Out, CompletionStage[T]]): javadsl.Flow[In, T, Mat] = new Flow(delegate.foldAsync(zero) { (out, in) ⇒ f(out, in).toScala })
/**
* 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.
*
* If the stream is empty (i.e. completes before signalling any elements),
* the reduce stage will fail its downstream with a [[NoSuchElementException]],
* which is semantically in-line with that Scala's standard library collections
* do in such situations.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' upstream completes
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def reduce(f: function.Function2[Out, Out, Out]): 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(start: Out, inject: Out, end: Out): javadsl.Flow[In, Out, 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(inject: Out): javadsl.Flow[In, Out, 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 or `n` elements is buffered
*
* '''Backpressures when''' downstream backpressures, and there are `n+1` buffered elements
*
* '''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.
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def groupedWithin(n: Int, d: FiniteDuration): javadsl.Flow[In, java.util.List[Out], Mat] =
new Flow(delegate.groupedWithin(n, d).map(_.asJava)) // TODO optimize to one step
/**
* 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 or `n` elements is buffered
*
* '''Backpressures when''' downstream backpressures, and there are `n+1` buffered elements
*
* '''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: java.time.Duration): javadsl.Flow[In, java.util.List[Out], Mat] =
groupedWithin(n, d.asScala)
/**
* Chunk up this stream into groups of elements received within a time window,
* or limited by the weight of the 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 or weight limit reached
*
* '''Backpressures when''' downstream backpressures, and buffered group (+ pending element) weighs more than `maxWeight`
*
* '''Completes when''' upstream completes (emits last group)
*
* '''Cancels when''' downstream completes
*
* `maxWeight` must be positive, and `d` must be greater than 0 seconds, otherwise
* IllegalArgumentException is thrown.
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def groupedWeightedWithin(maxWeight: Long, costFn: function.Function[Out, java.lang.Long], d: FiniteDuration): javadsl.Flow[In, java.util.List[Out], Mat] =
new Flow(delegate.groupedWeightedWithin(maxWeight, d)(costFn.apply).map(_.asJava))
/**
* Chunk up this stream into groups of elements received within a time window,
* or limited by the weight of the 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 or weight limit reached
*
* '''Backpressures when''' downstream backpressures, and buffered group (+ pending element) weighs more than `maxWeight`
*
* '''Completes when''' upstream completes (emits last group)
*
* '''Cancels when''' downstream completes
*
* `maxWeight` must be positive, and `d` must be greater than 0 seconds, otherwise
* IllegalArgumentException is thrown.
*/
def groupedWeightedWithin(maxWeight: Long, costFn: function.Function[Out, java.lang.Long], d: java.time.Duration): javadsl.Flow[In, java.util.List[Out], Mat] =
groupedWeightedWithin(maxWeight, costFn, d.asScala)
/**
* 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 `addAttributes(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
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def delay(of: FiniteDuration, strategy: DelayOverflowStrategy): Flow[In, Out, Mat] =
new Flow(delegate.delay(of, strategy))
/**
* 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 `addAttributes(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: java.time.Duration, strategy: DelayOverflowStrategy): Flow[In, Out, Mat] =
delay(of.asScala, 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
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def dropWithin(d: FiniteDuration): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.dropWithin(d))
/**
* 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: java.time.Duration): javadsl.Flow[In, Out, Mat] =
dropWithin(d.asScala)
/**
* Terminate processing (and cancel the upstream publisher) after predicate
* returns false for the first time, including the first failed element iff inclusive is true
* 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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the predicate is true
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' predicate returned false (or 1 after predicate returns false if `inclusive` or upstream completes
*
* '''Cancels when''' predicate returned false or downstream cancels
*
* See also [[Flow.limit]], [[Flow.limitWeighted]]
*/
def takeWhile(p: function.Predicate[Out], inclusive: Boolean = false): javadsl.Flow[In, Out, Mat] = new Flow(delegate.takeWhile(p.test, inclusive))
/**
* Terminate processing (and cancel the upstream publisher) after predicate
* returns false for the first time, including the first failed element iff inclusive is true
* 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 1 after predicate returns false if `inclusive` 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] = takeWhile(p, false)
/**
* Discard elements at the beginning of the stream while predicate is true.
* All elements will be taken after predicate returns false first time.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''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.
*
* Throwing an exception inside `recover` _will_ be logged on ERROR level automatically.
*
* '''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(pf: PartialFunction[Throwable, Out]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.recover(pf))
/**
* While similar to [[recover]] this stage can be used to transform an error signal to a different one *without* logging
* it as an error in the process. So in that sense it is NOT exactly equivalent to `recover(t => throw t2)` since recover
* would log the `t2` error.
*
* 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.
*
* Similarily to [[recover]] throwing an exception inside `mapError` _will_ be logged.
*
* '''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 mapError(pf: PartialFunction[Throwable, Throwable]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.mapError(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.
*
* Throwing an exception inside `recoverWith` _will_ be logged on ERROR level automatically.
*
* '''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
*
*/
@deprecated("Use recoverWithRetries instead.", "2.4.4")
def recoverWith(pf: PartialFunction[Throwable, _ <: Graph[SourceShape[Out], NotUsed]]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.recoverWith(pf))
/**
* RecoverWithRetries allows to switch to alternative Source on flow failure. It will stay in effect after
* a failure has been recovered up to `attempts` number of times so that each time there is a failure
* it is fed into the `pf` and a new Source may be materialized. Note that if you pass in 0, this won't
* attempt to recover at all.
*
* A negative `attempts` number is interpreted as "infinite", which results in the exact same behavior as `recoverWith`.
*
* 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.
*
* Throwing an exception inside `recoverWithRetries` _will_ be logged on ERROR level automatically.
*
* '''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
*
* @param attempts Maximum number of retries or -1 to retry indefinitely
* @param pf Receives the failure cause and returns the new Source to be materialized if any
*/
def recoverWithRetries(attempts: Int, pf: PartialFunction[Throwable, Graph[SourceShape[Out], NotUsed]]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.recoverWithRetries(attempts, 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]]
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def takeWithin(d: FiniteDuration): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.takeWithin(d))
/**
* 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: java.time.Duration): javadsl.Flow[In, Out, Mat] =
takeWithin(d.asScala)
/**
* 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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''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(aggregate: function.Function2[Out, Out, Out]): javadsl.Flow[In, Out, 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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''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, java.lang.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 upstream 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 `expander` function will complete the stream with failure.
*
* See also [[#extrapolate]] for a version that always preserves the original element and allows for an initial "startup" element.
*
* '''Emits when''' downstream stops backpressuring
*
* '''Backpressures when''' downstream backpressures or iterator runs empty
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
* @param expander Takes the current extrapolation state to produce an output element and the next extrapolation
* state.
* @see [[#extrapolate]]
*/
def expand[U](expander: function.Function[Out, java.util.Iterator[U]]): javadsl.Flow[In, U, Mat] =
new Flow(delegate.expand(in ⇒ expander(in).asScala))
/**
* Allows a faster downstream to progress independent of a slower upstream.
*
* This is achieved by introducing "extrapolated" elements - based on those from upstream - whenever downstream
* signals demand.
*
* Extrapolate does not support [[akka.stream.Supervision#restart]] and [[akka.stream.Supervision#resume]].
* Exceptions from the `extrapolate` function will complete the stream with failure.
*
* See also [[#expand]] for a version that can overwrite the original element.
*
* '''Emits when''' downstream stops backpressuring, AND EITHER upstream emits OR initial element is present OR
* `extrapolate` is non-empty and applicable
*
* '''Backpressures when''' downstream backpressures or current `extrapolate` runs empty
*
* '''Completes when''' upstream completes and current `extrapolate` runs empty
*
* '''Cancels when''' downstream cancels
*
* @param extrapolator Takes the current upstream element and provides a sequence of "extrapolated" elements based
* on the original, to be emitted in case downstream signals demand.
* @see [[#expand]]
*/
def extrapolate(extrapolator: function.Function[Out @uncheckedVariance, java.util.Iterator[Out @uncheckedVariance]]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.extrapolate(in ⇒ extrapolator(in).asScala))
/**
* Allows a faster downstream to progress independent of a slower upstream.
*
* This is achieved by introducing "extrapolated" elements - based on those from upstream - whenever downstream
* signals demand.
*
* Extrapolate does not support [[akka.stream.Supervision#restart]] and [[akka.stream.Supervision#resume]].
* Exceptions from the `extrapolate` function will complete the stream with failure.
*
* See also [[#expand]] for a version that can overwrite the original element.
*
* '''Emits when''' downstream stops backpressuring, AND EITHER upstream emits OR initial element is present OR
* `extrapolate` is non-empty and applicable
*
* '''Backpressures when''' downstream backpressures or current `extrapolate` runs empty
*
* '''Completes when''' upstream completes and current `extrapolate` runs empty
*
* '''Cancels when''' downstream cancels
*
* @param extrapolator Takes the current upstream element and provides a sequence of "extrapolated" elements based
* on the original, to be emitted in case downstream signals demand.
* @param initial The initial element to be emitted, in case upstream is able to stall the entire stream.
* @see [[#expand]]
*/
def extrapolate(extrapolator: function.Function[Out @uncheckedVariance, java.util.Iterator[Out @uncheckedVariance]], initial: Out @uncheckedVariance): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.extrapolate(in ⇒ extrapolator(in).asScala, Some(initial)))
/**
* 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''' downstream backpressures or depending on OverflowStrategy:
* <ul>
* <li>Backpressure - backpressures when buffer is full</li>
* <li>DropHead, DropTail, DropBuffer - never backpressures</li>
* <li>Fail - fails the stream if buffer gets full</li>
* </ul>
*
* '''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))
/**
* 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], javadsl.Source[Out, 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.
*
* Function `f` MUST NOT return `null`. This will throw exception and trigger supervision decision mechanism.
*
* '''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, 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(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[M](that: Graph[SourceShape[Out], M]): javadsl.Flow[In, Out, 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.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#concat]]
*/
def concatMat[M, M2](that: Graph[SourceShape[Out], M], matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, Out, 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[M](that: Graph[SourceShape[Out], M]): javadsl.Flow[In, Out, 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.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#prepend]]
*/
def prependMat[M, M2](that: Graph[SourceShape[Out], M], matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, Out, M2] =
new Flow(delegate.prependMat(that)(combinerToScala(matF)))
/**
* Provides a secondary source that will be consumed if this source completes without any
* elements passing by. As soon as the first element comes through this stream, the alternative
* will be cancelled.
*
* 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 or it gets
* cancelled.
*
* On errors the stage is failed regardless of source of the error.
*
* '''Emits when''' element is available from first stream or first stream closed without emitting any elements and an element
* is available from the second stream
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' the primary stream completes after emitting at least one element, when the primary stream completes
* without emitting and the secondary stream already has completed or when the secondary stream completes
*
* '''Cancels when''' downstream cancels and additionally the alternative is cancelled as soon as an element passes
* by from this stream.
*/
def orElse[M](secondary: Graph[SourceShape[Out], M]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.orElse(secondary))
/**
* Provides a secondary source that will be consumed if this source completes without any
* elements passing by. As soon as the first element comes through this stream, the alternative
* will be cancelled.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#orElse]]
*/
def orElseMat[M2, M3](
secondary: Graph[SourceShape[Out], M2],
matF: function.Function2[Mat, M2, M3]): javadsl.Flow[In, Out, M3] =
new Flow(delegate.orElseMat(secondary)(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 or Sink 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]].
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @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)))
/**
* Attaches the given [[Sink]] to this [[Flow]], meaning that elements will be sent to the [[Sink]]
* instead of being passed through if the predicate `when` returns `true`.
*
* '''Emits when''' emits when an element is available from the input and the chosen output has demand
*
* '''Backpressures when''' the currently chosen output back-pressures
*
* '''Completes when''' upstream completes and no output is pending
*
* '''Cancels when''' any of the downstreams cancel
*/
def divertTo(that: Graph[SinkShape[Out], _], when: function.Predicate[Out]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.divertTo(that, when.test))
/**
* Attaches the given [[Sink]] to this [[Flow]], meaning that elements will be sent to the [[Sink]]
* instead of being passed through if the predicate `when` returns `true`.
*
* @see [[#divertTo]]
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*/
def divertToMat[M2, M3](that: Graph[SinkShape[Out], M2], when: function.Predicate[Out], matF: function.Function2[Mat, M2, M3]): javadsl.Flow[In, Out, M3] =
new Flow(delegate.divertToMat(that, when.test)(combinerToScala(matF)))
/**
* Attaches the given [[Sink]] to this [[Flow]] as a wire tap, meaning that elements that pass
* through will also be sent to the wire-tap Sink, without the latter affecting the mainline flow.
* If the wire-tap Sink backpressures, elements that would've been sent to it will be dropped instead.
*
* '''Emits when''' element is available and demand exists from the downstream; the element will
* also be sent to the wire-tap Sink if there is demand.
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def wireTap(that: Graph[SinkShape[Out], _]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.wireTap(that))
/**
* Attaches the given [[Sink]] to this [[Flow]] as a wire tap, meaning that elements that pass
* through will also be sent to the wire-tap Sink, without the latter affecting the mainline flow.
* If the wire-tap Sink backpressures, elements that would've been sent to it will be dropped instead.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#wireTap]]
*/
def wireTapMat[M2, M3](
that: Graph[SinkShape[Out], M2],
matF: function.Function2[Mat, M2, M3]): javadsl.Flow[In, Out, M3] =
new Flow(delegate.wireTapMat(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(that: Graph[SourceShape[Out], _], segmentSize: Int): javadsl.Flow[In, Out, Mat] =
interleave(that, segmentSize, eagerClose = false)
/**
* 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.
*
* If eagerClose is false and one of the upstreams complete the elements from the other upstream will continue passing
* through the interleave stage. If eagerClose is true and one of the upstream complete interleave will cancel the
* other upstream and complete itself.
*
* 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(that: Graph[SourceShape[Out], _], segmentSize: Int, eagerClose: Boolean): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.interleave(that, segmentSize, eagerClose))
/**
* 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 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.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#interleave]]
*/
def interleaveMat[M, M2](that: Graph[SourceShape[Out], M], segmentSize: Int,
matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, Out, M2] =
interleaveMat(that, segmentSize, eagerClose = false, 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.
*
* If eagerClose is false and one of the upstreams complete the elements from the other upstream will continue passing
* through the interleave stage. If eagerClose is true and one of the upstream complete interleave will cancel the
* other upstream and complete itself.
*
* If this [[Flow]] or [[Source]] gets upstream error - stream completes with failure.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#interleave]]
*/
def interleaveMat[M, M2](that: Graph[SourceShape[Out], M], segmentSize: Int, eagerClose: Boolean,
matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, Out, M2] =
new Flow(delegate.interleaveMat(that, segmentSize, eagerClose)(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(that: Graph[SourceShape[Out], _]): javadsl.Flow[In, Out, 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(that: Graph[SourceShape[Out], _], eagerComplete: Boolean): javadsl.Flow[In, Out, 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.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#merge]]
*/
def mergeMat[M, M2](
that: Graph[SourceShape[Out], M],
matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, Out, 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.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#merge]]
*/
def mergeMat[M, M2](
that: Graph[SourceShape[Out], M],
matF: function.Function2[Mat, M, M2],
eagerComplete: Boolean): javadsl.Flow[In, Out, M2] =
new Flow(delegate.mergeMat(that, eagerComplete)(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[M](that: Graph[SourceShape[Out], M], comp: Comparator[Out]): javadsl.Flow[In, Out, 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).
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#mergeSorted]].
*/
def mergeSortedMat[Mat2, Mat3](that: Graph[SourceShape[Out], Mat2], comp: Comparator[Out],
matF: function.Function2[Mat, Mat2, Mat3]): javadsl.Flow[In, Out, 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 Pair T, Mat] =
zipMat(source, Keep.left)
/**
* Combine the elements of current [[Flow]] and the given [[Source]] into a stream of tuples.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @see [[#zip]]
*/
def zipMat[T, M, M2](
that: Graph[SourceShape[T], M],
matF: function.Function2[Mat, M, M2]): javadsl.Flow[In, Out Pair T, M2] =
this.viaMat(Flow.fromGraph(GraphDSL.create(
that,
new function.Function2[GraphDSL.Builder[M], SourceShape[T], FlowShape[Out, Out Pair T]] {
def apply(b: GraphDSL.Builder[M], s: SourceShape[T]): FlowShape[Out, Out 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.
*
* It is recommended to use the internally optimized `Keep.left` and `Keep.right` combiners
* where appropriate instead of manually writing functions that pass through one of the values.
*
* @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)))
/**
* Combine the elements of current flow into a stream of tuples consisting
* of all elements paired with their index. Indices start at 0.
*
* '''Emits when''' upstream emits an element and is paired with their index
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def zipWithIndex: Flow[In, Pair[Out, Long], Mat] =
new Flow(delegate.zipWithIndex.map { case (elem, index) ⇒ Pair(elem, index) })
/**
* 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
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def initialTimeout(timeout: FiniteDuration): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.initialTimeout(timeout))
/**
* 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: java.time.Duration): javadsl.Flow[In, Out, Mat] =
initialTimeout(timeout.asScala)
/**
* 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
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def completionTimeout(timeout: FiniteDuration): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.completionTimeout(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: java.time.Duration): javadsl.Flow[In, Out, Mat] =
completionTimeout(timeout.asScala)
/**
* If the time between two processed elements exceeds the provided timeout, the stream is failed
* with a [[java.util.concurrent.TimeoutException]]. The timeout is checked periodically,
* so the resolution of the check is one period (equals to timeout value).
*
* '''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
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def idleTimeout(timeout: FiniteDuration): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.idleTimeout(timeout))
/**
* If the time between two processed elements exceeds the provided timeout, the stream is failed
* with a [[java.util.concurrent.TimeoutException]]. The timeout is checked periodically,
* so the resolution of the check is one period (equals to timeout value).
*
* '''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: java.time.Duration): javadsl.Flow[In, Out, Mat] =
idleTimeout(timeout.asScala)
/**
* If the time between the emission of an element and the following downstream demand exceeds the provided timeout,
* the stream is failed with a [[java.util.concurrent.TimeoutException]]. The timeout is checked periodically,
* so the resolution of the check is one period (equals to timeout value).
*
* '''Emits when''' upstream emits an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes or fails if timeout elapses between element emission and downstream demand.
*
* '''Cancels when''' downstream cancels
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def backpressureTimeout(timeout: FiniteDuration): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.backpressureTimeout(timeout))
/**
* If the time between the emission of an element and the following downstream demand exceeds the provided timeout,
* the stream is failed with a [[java.util.concurrent.TimeoutException]]. The timeout is checked periodically,
* so the resolution of the check is one period (equals to timeout value).
*
* '''Emits when''' upstream emits an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes or fails if timeout elapses between element emission and downstream demand.
*
* '''Cancels when''' downstream cancels
*/
def backpressureTimeout(timeout: java.time.Duration): javadsl.Flow[In, Out, Mat] =
backpressureTimeout(timeout.asScala)
/**
* Injects additional elements if 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
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def keepAlive(maxIdle: FiniteDuration, injectedElem: function.Creator[Out]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.keepAlive(maxIdle, () ⇒ injectedElem.create()))
/**
* Injects additional elements if 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(maxIdle: java.time.Duration, injectedElem: function.Creator[Out]): javadsl.Flow[In, Out, Mat] =
keepAlive(maxIdle.asScala, injectedElem)
/**
* 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).
* 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 costs. 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. Bucket is full when stream just materialized and
* started.
*
* The burst size is calculated based on the given rate (`cost/per`) as 0.1 * rate, for example:
* - rate < 20/second => burst size 1
* - rate 20/second => burst size 2
* - rate 100/second => burst size 10
* - rate 200/second => burst size 20
*
* The throttle `mode` is [[akka.stream.ThrottleMode.Shaping]], which makes pauses before emitting messages to
* meet throttle rate.
*
* '''Emits when''' upstream emits an element and configured time per each element elapsed
*
* '''Backpressures when''' downstream backpressures or the incoming rate is higher than the speed limit
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
*/
def throttle(elements: Int, per: java.time.Duration): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.throttle(elements, per.asScala))
/**
* 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 element costs. 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. Bucket is full when stream just materialized and started.
*
* Parameter `mode` manages behavior 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
*
* It is recommended to use non-zero burst sizes as they improve both performance and throttling precision by allowing
* the implementation to avoid using the scheduler when input rates fall below the enforced limit and to reduce
* most of the inaccuracy caused by the scheduler resolution (which is in the range of milliseconds).
*
* WARNING: Be aware that throttle is using scheduler to slow down the stream. This scheduler has minimal time of triggering
* next push. Consequently it will slow down the stream as it has minimal pause for emitting. This can happen in
* case burst is 0 and speed is higher than 30 events per second. You need to increase the `maximumBurst` if
* elements arrive with small interval (30 milliseconds or less). Use the overloaded `throttle` method without
* `maximumBurst` parameter to automatically calculate the `maximumBurst` based on the given rate (`cost/per`).
* In other words the throttler always enforces the rate limit when `maximumBurst` parameter is given, but in
* certain cases (mostly due to limited scheduler resolution) it enforces a tighter bound than what was prescribed.
*
* '''Emits when''' upstream emits an element and configured time per each element elapsed
*
* '''Backpressures when''' downstream backpressures or the incoming rate is higher than the speed limit
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
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 `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 element costs. 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. Bucket is full when stream just materialized and started.
*
* Parameter `mode` manages behavior 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
*
* It is recommended to use non-zero burst sizes as they improve both performance and throttling precision by allowing
* the implementation to avoid using the scheduler when input rates fall below the enforced limit and to reduce
* most of the inaccuracy caused by the scheduler resolution (which is in the range of milliseconds).
*
* WARNING: Be aware that throttle is using scheduler to slow down the stream. This scheduler has minimal time of triggering
* next push. Consequently it will slow down the stream as it has minimal pause for emitting. This can happen in
* case burst is 0 and speed is higher than 30 events per second. You need to increase the `maximumBurst` if
* elements arrive with small interval (30 milliseconds or less). Use the overloaded `throttle` method without
* `maximumBurst` parameter to automatically calculate the `maximumBurst` based on the given rate (`cost/per`).
* In other words the throttler always enforces the rate limit when `maximumBurst` parameter is given, but in
* certain cases (mostly due to limited scheduler resolution) it enforces a tighter bound than what was prescribed.
*
* '''Emits when''' upstream emits an element and configured time per each element elapsed
*
* '''Backpressures when''' downstream backpressures or the incoming rate is higher than the speed limit
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
*/
def throttle(elements: Int, per: java.time.Duration, maximumBurst: Int,
mode: ThrottleMode): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.throttle(elements, per.asScala, 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 costs. 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. Bucket is full when stream just materialized and started.
*
* Parameter `mode` manages behavior 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
*
* It is recommended to use non-zero burst sizes as they improve both performance and throttling precision by allowing
* the implementation to avoid using the scheduler when input rates fall below the enforced limit and to reduce
* most of the inaccuracy caused by the scheduler resolution (which is in the range of milliseconds).
*
* WARNING: Be aware that throttle is using scheduler to slow down the stream. This scheduler has minimal time of triggering
* next push. Consequently it will slow down the stream as it has minimal pause for emitting. This can happen in
* case burst is 0 and speed is higher than 30 events per second. You need to increase the `maximumBurst` if
* elements arrive with small interval (30 milliseconds or less). Use the overloaded `throttle` method without
* `maximumBurst` parameter to automatically calculate the `maximumBurst` based on the given rate (`cost/per`).
* In other words the throttler always enforces the rate limit when `maximumBurst` parameter is given, but in
* certain cases (mostly due to limited scheduler resolution) it enforces a tighter bound than what was prescribed.
*
* '''Emits when''' upstream emits an element and configured time per each element elapsed
*
* '''Backpressures when''' downstream backpressures or the incoming rate is higher than the speed limit
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
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))
/**
* 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).
* 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 costs. 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. Bucket is full when stream just materialized and
* started.
*
* The burst size is calculated based on the given rate (`cost/per`) as 0.1 * rate, for example:
* - rate < 20/second => burst size 1
* - rate 20/second => burst size 2
* - rate 100/second => burst size 10
* - rate 200/second => burst size 20
*
* The throttle `mode` is [[akka.stream.ThrottleMode.Shaping]], which makes pauses before emitting messages to
* meet throttle rate.
*
* '''Emits when''' upstream emits an element and configured time per each element elapsed
*
* '''Backpressures when''' downstream backpressures or the incoming rate is higher than the speed limit
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
*/
def throttle(cost: Int, per: java.time.Duration,
costCalculation: function.Function[Out, Integer]): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.throttle(cost, per.asScala, costCalculation.apply))
/**
* 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 costs. 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. Bucket is full when stream just materialized and started.
*
* Parameter `mode` manages behavior 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
*
* It is recommended to use non-zero burst sizes as they improve both performance and throttling precision by allowing
* the implementation to avoid using the scheduler when input rates fall below the enforced limit and to reduce
* most of the inaccuracy caused by the scheduler resolution (which is in the range of milliseconds).
*
* WARNING: Be aware that throttle is using scheduler to slow down the stream. This scheduler has minimal time of triggering
* next push. Consequently it will slow down the stream as it has minimal pause for emitting. This can happen in
* case burst is 0 and speed is higher than 30 events per second. You need to increase the `maximumBurst` if
* elements arrive with small interval (30 milliseconds or less). Use the overloaded `throttle` method without
* `maximumBurst` parameter to automatically calculate the `maximumBurst` based on the given rate (`cost/per`).
* In other words the throttler always enforces the rate limit when `maximumBurst` parameter is given, but in
* certain cases (mostly due to limited scheduler resolution) it enforces a tighter bound than what was prescribed.
*
* '''Emits when''' upstream emits an element and configured time per each element elapsed
*
* '''Backpressures when''' downstream backpressures or the incoming rate is higher than the speed limit
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
*/
def throttle(cost: Int, per: java.time.Duration, maximumBurst: Int,
costCalculation: function.Function[Out, Integer], mode: ThrottleMode): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.throttle(cost, per.asScala, maximumBurst, costCalculation.apply, mode))
/**
* This is a simplified version of throttle that spreads events evenly across the given time interval.
*
* Use this combinator when you need just slow down a stream without worrying about exact amount
* of time between events.
*
* If you want to be sure that no time interval has no more than specified number of events you need to use
* [[throttle()]] with maximumBurst attribute.
* @see [[#throttle]]
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def throttleEven(elements: Int, per: FiniteDuration, mode: ThrottleMode): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.throttleEven(elements, per, mode))
/**
* This is a simplified version of throttle that spreads events evenly across the given time interval.
*
* Use this combinator when you need just slow down a stream without worrying about exact amount
* of time between events.
*
* If you want to be sure that no time interval has no more than specified number of events you need to use
* [[throttle()]] with maximumBurst attribute.
* @see [[#throttle]]
*/
@Deprecated
@deprecated("Use throttle without `maximumBurst` parameter instead.", "2.5.12")
def throttleEven(elements: Int, per: java.time.Duration, mode: ThrottleMode): javadsl.Flow[In, Out, Mat] =
throttleEven(elements, per.asScala, mode)
/**
* This is a simplified version of throttle that spreads events evenly across the given time interval.
*
* Use this combinator when you need just slow down a stream without worrying about exact amount
* of time between events.
*
* If you want to be sure that no time interval has no more than specified number of events you need to use
* [[throttle()]] with maximumBurst attribute.
* @see [[#throttle]]
*/
@Deprecated
@deprecated("Use throttle without `maximumBurst` parameter instead.", "2.5.12")
def throttleEven(cost: Int, per: FiniteDuration,
costCalculation: function.Function[Out, Integer], mode: ThrottleMode): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.throttleEven(cost, per, costCalculation.apply, mode))
/**
* This is a simplified version of throttle that spreads events evenly across the given time interval.
*
* Use this combinator when you need just slow down a stream without worrying about exact amount
* of time between events.
*
* If you want to be sure that no time interval has no more than specified number of events you need to use
* [[throttle()]] with maximumBurst attribute.
* @see [[#throttle]]
*/
@Deprecated
@deprecated("Use throttle without `maximumBurst` parameter instead.", "2.5.12")
def throttleEven(cost: Int, per: java.time.Duration,
costCalculation: function.Function[Out, Integer], mode: ThrottleMode): javadsl.Flow[In, Out, Mat] =
throttleEven(cost, per.asScala, costCalculation, 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 `CompletionStage<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)))
/**
* Materializes to `FlowMonitor[Out]` that allows monitoring of the current flow. All events are propagated
* by the monitor unchanged. Note that the monitor inserts a memory barrier every time it processes an
* event, and may therefor affect performance.
* The `combine` function is used to combine the `FlowMonitor` with this flow's materialized value.
*/
def monitor[M]()(combine: function.Function2[Mat, FlowMonitor[Out], M]): javadsl.Flow[In, Out, M] =
new Flow(delegate.monitor()(combinerToScala(combine)))
/**
* Delays the initial element by the specified duration.
*
* '''Emits when''' upstream emits an element if the initial delay is already elapsed
*
* '''Backpressures when''' downstream backpressures or initial delay is not yet elapsed
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def initialDelay(delay: FiniteDuration): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.initialDelay(delay))
/**
* Delays the initial element by the specified duration.
*
* '''Emits when''' upstream emits an element if the initial delay is already elapsed
*
* '''Backpressures when''' downstream backpressures or initial delay is not yet elapsed
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def initialDelay(delay: java.time.Duration): javadsl.Flow[In, Out, Mat] =
initialDelay(delay.asScala)
/**
* Replace the attributes of this [[Flow]] with the given ones. If this Flow is a composite
* of multiple graphs, new attributes on the composite will be less specific than attributes
* set directly on the individual graphs of the composite.
*
* 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 [[Flow]]. If the specific attribute was already present
* on this graph this means the added attribute will be more specific than the existing one.
* If this Flow is a composite of multiple graphs, new attributes on the composite will be
* less specific than attributes set directly on the individual graphs of the composite.
*/
override def 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)
/**
* Put an asynchronous boundary around this `Flow`
*
* @param dispatcher Run the graph on this dispatcher
*/
override def async(dispatcher: String): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.async(dispatcher))
/**
* Put an asynchronous boundary around this `Flow`
*
* @param dispatcher Run the graph on this dispatcher
* @param inputBufferSize Set the input buffer to this size for the graph
*/
override def async(dispatcher: String, inputBufferSize: Int): javadsl.Flow[In, Out, Mat] =
new Flow(delegate.async(dispatcher, inputBufferSize))
/**
* 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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''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, Out]] = {
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] {
override def shape = ClosedShape
override def traversalBuilder = runnable.traversalBuilder
override def toString: String = runnable.toString
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)
}
}
}
/**
* 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]
override def withAttributes(attr: Attributes): RunnableGraph[Mat]
override def addAttributes(attr: Attributes): RunnableGraph[Mat] =
withAttributes(traversalBuilder.attributes and attr)
override def named(name: String): RunnableGraph[Mat] =
withAttributes(Attributes.name(name))
}
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