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/*
* Copyright (C) 2014-2020 Lightbend Inc.
*/
package akka.stream.javadsl
import java.util
import java.util.Optional
import java.util.concurrent.{ CompletableFuture, CompletionStage }
import java.util.function.{ BiFunction, Supplier }
import akka.actor.{ ActorRef, Cancellable, ClassicActorSystemProvider }
import akka.dispatch.ExecutionContexts
import akka.event.LoggingAdapter
import akka.japi.function.Creator
import akka.japi.{ function, JavaPartialFunction, Pair, Util }
import akka.stream._
import akka.stream.impl.LinearTraversalBuilder
import akka.util.JavaDurationConverters._
import akka.util.ccompat.JavaConverters._
import akka.util.{ unused, _ }
import akka.{ Done, NotUsed }
import com.github.ghik.silencer.silent
import org.reactivestreams.{ Publisher, Subscriber }
import scala.annotation.unchecked.uncheckedVariance
import scala.collection.immutable
import scala.compat.java8.FutureConverters._
import scala.compat.java8.OptionConverters._
import scala.concurrent.duration.FiniteDuration
import scala.concurrent.{ Future, Promise }
import scala.reflect.ClassTag
/** Java API */
object Source {
private[this] val _empty = new Source[Any, NotUsed](scaladsl.Source.empty)
/**
* Create a `Source` with no elements, i.e. an empty stream that is completed immediately
* for every connected `Sink`.
*/
def empty[O](): Source[O, NotUsed] = _empty.asInstanceOf[Source[O, NotUsed]]
/**
* Create a `Source` with no elements. The result is the same as calling `Source.empty()`
*/
def empty[T](@unused clazz: Class[T]): Source[T, NotUsed] = empty[T]()
/**
* Create a `Source` which materializes a [[java.util.concurrent.CompletableFuture]] which controls what element
* will be emitted by the Source.
* If the materialized promise is completed with a filled Optional, that value will be produced downstream,
* followed by completion.
* If the materialized promise is completed with an empty Optional, no value will be produced downstream and completion will
* be signalled immediately.
* If the materialized promise is completed with a failure, then the source will fail with that error.
* If the downstream of this source cancels or fails before the promise has been completed, then the promise will be completed
* with an empty Optional.
*/
def maybe[T]: Source[T, CompletableFuture[Optional[T]]] = {
new Source(scaladsl.Source.maybe[T].mapMaterializedValue { scalaOptionPromise: Promise[Option[T]] =>
val javaOptionPromise = new CompletableFuture[Optional[T]]()
scalaOptionPromise.completeWith(
javaOptionPromise.toScala.map(_.asScala)(akka.dispatch.ExecutionContexts.sameThreadExecutionContext))
javaOptionPromise
})
}
/**
* Helper to create [[Source]] from `Publisher`.
*
* Construct a transformation starting with given publisher. The transformation steps
* are executed by a series of [[org.reactivestreams.Processor]] instances
* that mediate the flow of elements downstream and the propagation of
* back-pressure upstream.
*/
def fromPublisher[O](publisher: Publisher[O]): javadsl.Source[O, NotUsed] =
new Source(scaladsl.Source.fromPublisher(publisher))
/**
* Helper to create [[Source]] from `Iterator`.
* Example usage:
*
* {{{
* List data = new ArrayList();
* data.add(1);
* data.add(2);
* data.add(3);
* Source.from(() -> data.iterator());
* }}}
*
* Start a new `Source` from the given Iterator. The produced stream of elements
* will continue until the iterator runs empty or fails during evaluation of
* the `next()` method. Elements are pulled out of the iterator
* in accordance with the demand coming from the downstream transformation
* steps.
*/
def fromIterator[O](f: function.Creator[java.util.Iterator[O]]): javadsl.Source[O, NotUsed] =
new Source(scaladsl.Source.fromIterator(() => f.create().asScala))
/**
* Helper to create 'cycled' [[Source]] from iterator provider.
* Example usage:
*
* {{{
* Source.cycle(() -> Arrays.asList(1, 2, 3).iterator());
* }}}
*
* Start a new 'cycled' `Source` from the given elements. The producer stream of elements
* will continue infinitely by repeating the sequence of elements provided by function parameter.
*/
def cycle[O](f: function.Creator[java.util.Iterator[O]]): javadsl.Source[O, NotUsed] =
new Source(scaladsl.Source.cycle(() => f.create().asScala))
/**
* Helper to create [[Source]] from `Iterable`.
* Example usage:
* {{{
* List data = new ArrayList();
* data.add(1);
* data.add(2);
* data.add(3);
* Source.from(data);
* }}}
*
* Starts a new `Source` from the given `Iterable`. This is like starting from an
* Iterator, but every Subscriber directly attached to the Publisher of this
* stream will see an individual flow of elements (always starting from the
* beginning) regardless of when they subscribed.
*
* Make sure that the `Iterable` is immutable or at least not modified after
* being used as a `Source`. Otherwise the stream may fail with
* `ConcurrentModificationException` or other more subtle errors may occur.
*/
def from[O](iterable: java.lang.Iterable[O]): javadsl.Source[O, NotUsed] = {
// this adapter is not immutable if the underlying java.lang.Iterable is modified
// but there is not anything we can do to prevent that from happening.
// ConcurrentModificationException will be thrown in some cases.
val scalaIterable = new immutable.Iterable[O] {
override def iterator: Iterator[O] = iterable.iterator().asScala
}
new Source(scaladsl.Source(scalaIterable))
}
/**
* Creates [[Source]] that represents integer values in range ''[start;end]'', step equals to 1.
* It allows to create `Source` out of range as simply as on Scala `Source(1 to N)`
*
* Uses [[scala.collection.immutable.Range.inclusive(Int, Int)]] internally
*
* @see [[scala.collection.immutable.Range.inclusive(Int, Int)]]
*/
def range(start: Int, end: Int): javadsl.Source[Integer, NotUsed] = range(start, end, 1)
/**
* Creates [[Source]] that represents integer values in range ''[start;end]'', with the given step.
* It allows to create `Source` out of range as simply as on Scala `Source(1 to N)`
*
* Uses [[scala.collection.immutable.Range.inclusive(Int, Int, Int)]] internally
*
* @see [[scala.collection.immutable.Range.inclusive(Int, Int, Int)]]
*/
def range(start: Int, end: Int, step: Int): javadsl.Source[Integer, NotUsed] =
fromIterator[Integer](new function.Creator[util.Iterator[Integer]]() {
def create(): util.Iterator[Integer] =
Range.inclusive(start, end, step).iterator.asJava.asInstanceOf[util.Iterator[Integer]]
})
/**
* Start a new `Source` from the given `Future`. The stream will consist of
* one element when the `Future` is completed with a successful value, which
* may happen before or after materializing the `Flow`.
* The stream terminates with a failure if the `Future` is completed with a failure.
*/
@deprecated("Use 'Source.future' instead", "2.6.0")
def fromFuture[O](future: Future[O]): javadsl.Source[O, NotUsed] =
new Source(scaladsl.Source.future(future))
/**
* Starts a new `Source` from the given `CompletionStage`. The stream will consist of
* one element when the `CompletionStage` is completed with a successful value, which
* may happen before or after materializing the `Flow`.
* The stream terminates with a failure if the `CompletionStage` is completed with a failure.
*/
@deprecated("Use 'Source.completionStage' instead", "2.6.0")
def fromCompletionStage[O](future: CompletionStage[O]): javadsl.Source[O, NotUsed] =
new Source(scaladsl.Source.completionStage(future))
/**
* Streams the elements of the given future source once it successfully completes.
* If the [[Future]] fails the stream is failed with the exception from the future. If downstream cancels before the
* stream completes the materialized [[Future]] will be failed with a [[StreamDetachedException]].
*/
@deprecated("Use 'Source.futureSource' (potentially together with `Source.fromGraph`) instead", "2.6.0")
def fromFutureSource[T, M](future: Future[_ <: Graph[SourceShape[T], M]]): javadsl.Source[T, Future[M]] =
new Source(scaladsl.Source.fromFutureSource(future))
/**
* Streams the elements of an asynchronous source once its given [[CompletionStage]] completes.
* If the [[CompletionStage]] fails the stream is failed with the exception from the future.
* If downstream cancels before the stream completes the materialized [[CompletionStage]] will be failed
* with a [[StreamDetachedException]]
*/
@deprecated("Use 'Source.completionStageSource' (potentially together with `Source.fromGraph`) instead", "2.6.0")
def fromSourceCompletionStage[T, M](
completion: CompletionStage[_ <: Graph[SourceShape[T], M]]): javadsl.Source[T, CompletionStage[M]] =
completionStageSource(completion.thenApply(fromGraph[T, M]))
/**
* Elements are emitted periodically with the specified interval.
* The tick element will be delivered to downstream consumers that has requested any elements.
* If a consumer has not requested any elements at the point in time when the tick
* element is produced it will not receive that tick element later. It will
* receive new tick elements as soon as it has requested more elements.
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def tick[O](initialDelay: FiniteDuration, interval: FiniteDuration, tick: O): javadsl.Source[O, Cancellable] =
new Source(scaladsl.Source.tick(initialDelay, interval, tick))
/**
* Elements are emitted periodically with the specified interval.
* The tick element will be delivered to downstream consumers that has requested any elements.
* If a consumer has not requested any elements at the point in time when the tick
* element is produced it will not receive that tick element later. It will
* receive new tick elements as soon as it has requested more elements.
*/
@silent("deprecated")
def tick[O](initialDelay: java.time.Duration, interval: java.time.Duration, tick: O): javadsl.Source[O, Cancellable] =
Source.tick(initialDelay.asScala, interval.asScala, tick)
/**
* Create a `Source` with one element.
* Every connected `Sink` of this stream will see an individual stream consisting of one element.
*/
def single[T](element: T): Source[T, NotUsed] =
new Source(scaladsl.Source.single(element))
/**
* Create a `Source` that will continually emit the given element.
*/
def repeat[T](element: T): Source[T, NotUsed] =
new Source(scaladsl.Source.repeat(element))
/**
* Create a `Source` that will unfold a value of type `S` into
* a pair of the next state `S` and output elements of type `E`.
*/
def unfold[S, E](s: S, f: function.Function[S, Optional[Pair[S, E]]]): Source[E, NotUsed] =
new Source(scaladsl.Source.unfold(s)((s: S) => f.apply(s).asScala.map(_.toScala)))
/**
* Same as [[unfold]], but uses an async function to generate the next state-element tuple.
*/
def unfoldAsync[S, E](s: S, f: function.Function[S, CompletionStage[Optional[Pair[S, E]]]]): Source[E, NotUsed] =
new Source(scaladsl.Source.unfoldAsync(s)((s: S) =>
f.apply(s).toScala.map(_.asScala.map(_.toScala))(akka.dispatch.ExecutionContexts.sameThreadExecutionContext)))
/**
* Create a `Source` that immediately ends the stream with the `cause` failure to every connected `Sink`.
*/
def failed[T](cause: Throwable): Source[T, NotUsed] =
new Source(scaladsl.Source.failed(cause))
/**
* Creates a `Source` that is not materialized until there is downstream demand, when the source gets materialized
* the materialized future is completed with its value, if downstream cancels or fails without any demand the
* `create` factory is never called and the materialized `CompletionStage` is failed.
*/
@deprecated("Use 'Source.lazySource' instead", "2.6.0")
def lazily[T, M](create: function.Creator[Source[T, M]]): Source[T, CompletionStage[M]] =
scaladsl.Source.lazily[T, M](() => create.create().asScala).mapMaterializedValue(_.toJava).asJava
/**
* Creates a `Source` from supplied future factory that is not called until downstream demand. When source gets
* materialized the materialized future is completed with the value from the factory. If downstream cancels or fails
* without any demand the create factory is never called and the materialized `Future` is failed.
*
* @see [[Source.lazily]]
*/
@deprecated("Use 'Source.lazyCompletionStage' instead", "2.6.0")
def lazilyAsync[T](create: function.Creator[CompletionStage[T]]): Source[T, Future[NotUsed]] =
scaladsl.Source.lazilyAsync[T](() => create.create().toScala).asJava
/**
* Emits a single value when the given Scala `Future` is successfully completed and then completes the stream.
* The stream fails if the `Future` is completed with a failure.
*
* Here for Java interoperability, the normal use from Java should be [[Source.completionStage]]
*/
def future[T](futureElement: Future[T]): Source[T, NotUsed] =
scaladsl.Source.future(futureElement).asJava
/**
* Emits a single value when the given `CompletionStage` is successfully completed and then completes the stream.
* If the `CompletionStage` is completed with a failure the stream is failed.
*/
def completionStage[T](completionStage: CompletionStage[T]): Source[T, NotUsed] =
future(completionStage.toScala)
/**
* Turn a `CompletionStage[Source]` into a source that will emit the values of the source when the future completes successfully.
* If the `CompletionStage` is completed with a failure the stream is failed.
*/
def completionStageSource[T, M](completionStageSource: CompletionStage[Source[T, M]]): Source[T, CompletionStage[M]] =
scaladsl.Source
.futureSource(completionStageSource.toScala.map(_.asScala)(ExecutionContexts.sameThreadExecutionContext))
.mapMaterializedValue(_.toJava)
.asJava
/**
* Defers invoking the `create` function to create a single element until there is downstream demand.
*
* If the `create` function fails when invoked the stream is failed.
*
* Note that asynchronous boundaries (and other operators) in the stream may do pre-fetching which counter acts
* the laziness and will trigger the factory immediately.
*
* The materialized future `Done` value is completed when the `create` function has successfully been invoked,
* if the function throws the future materialized value is failed with that exception.
* If downstream cancels or fails before the function is invoked the materialized value
* is failed with a [[akka.stream.NeverMaterializedException]]
*/
def lazySingle[T](create: Creator[T]): Source[T, NotUsed] =
lazySource(() => single(create.create())).mapMaterializedValue(_ => NotUsed)
/**
* Defers invoking the `create` function to create a future element until there is downstream demand.
*
* The returned future element will be emitted downstream when it completes, or fail the stream if the future
* is failed or the `create` function itself fails.
*
* Note that asynchronous boundaries (and other operators) in the stream may do pre-fetching which counter acts
* the laziness and will trigger the factory immediately.
*
* The materialized future `Done` value is completed when the `create` function has successfully been invoked and the future completes,
* if the function throws or the future fails the future materialized value is failed with that exception.
* If downstream cancels or fails before the function is invoked the materialized value
* is failed with a [[akka.stream.NeverMaterializedException]]
*/
def lazyCompletionStage[T](create: Creator[CompletionStage[T]]): Source[T, NotUsed] =
scaladsl.Source
.lazySource { () =>
val f = create.create().toScala
scaladsl.Source.future(f)
}
.mapMaterializedValue(_ => NotUsed.notUsed())
.asJava
/**
* Defers invoking the `create` function to create a future source until there is downstream demand.
*
* The returned source will emit downstream and behave just like it was the outer source. Downstream completes
* when the created source completes and fails when the created source fails.
*
* Note that asynchronous boundaries (and other operators) in the stream may do pre-fetching which counter acts
* the laziness and will trigger the factory immediately.
*
* The materialized future value is completed with the materialized value of the created source when
* it has been materialized. If the function throws or the source materialization fails the future materialized value
* is failed with the thrown exception.
*
* If downstream cancels or fails before the function is invoked the materialized value
* is failed with a [[akka.stream.NeverMaterializedException]]
*/
def lazySource[T, M](create: Creator[Source[T, M]]): Source[T, CompletionStage[M]] =
scaladsl.Source.lazySource(() => create.create().asScala).mapMaterializedValue(_.toJava).asJava
/**
* Defers invoking the `create` function to create a future source until there is downstream demand.
*
* The returned future source will emit downstream and behave just like it was the outer source when the `CompletionStage` completes
* successfully. Downstream completes when the created source completes and fails when the created source fails.
* If the `CompletionStage` or the `create` function fails the stream is failed.
*
* Note that asynchronous boundaries (and other operators) in the stream may do pre-fetching which counter acts
* the laziness and triggers the factory immediately.
*
* The materialized `CompletionStage` value is completed with the materialized value of the created source when
* it has been materialized. If the function throws or the source materialization fails the future materialized value
* is failed with the thrown exception.
*
* If downstream cancels or fails before the function is invoked the materialized value
* is failed with a [[akka.stream.NeverMaterializedException]]
*/
def lazyCompletionStageSource[T, M](create: Creator[CompletionStage[Source[T, M]]]): Source[T, CompletionStage[M]] =
lazySource[T, CompletionStage[M]](() => completionStageSource(create.create()))
.mapMaterializedValue(_.thenCompose(csm => csm))
/**
* Creates a `Source` that is materialized as a [[org.reactivestreams.Subscriber]]
*/
def asSubscriber[T](): Source[T, Subscriber[T]] =
new Source(scaladsl.Source.asSubscriber)
/**
* Creates a `Source` that is materialized as an [[akka.actor.ActorRef]].
* Messages sent to this actor will be emitted to the stream if there is demand from downstream,
* otherwise they will be buffered until request for demand is received.
*
* Depending on the defined [[akka.stream.OverflowStrategy]] it might drop elements if
* there is no space available in the buffer.
*
* The strategy [[akka.stream.OverflowStrategy.backpressure]] is not supported, and an
* IllegalArgument("Backpressure overflowStrategy not supported") will be thrown if it is passed as argument.
*
* The buffer can be disabled by using `bufferSize` of 0 and then received messages are dropped if there is no demand
* from downstream. When `bufferSize` is 0 the `overflowStrategy` does not matter. An async boundary is added after
* this Source; as such, it is never safe to assume the downstream will always generate demand.
*
* The stream can be completed successfully by sending the actor reference a message that is matched by
* `completionMatcher` in which case already buffered elements will be signaled before signaling
* completion.
*
* The stream can be completed with failure by sending a message that is matched by `failureMatcher`. The extracted
* [[Throwable]] will be used to fail the stream. In case the Actor is still draining its internal buffer (after having received
* a message matched by `completionMatcher`) before signaling completion and it receives a message matched by `failureMatcher`,
* the failure will be signaled downstream immediately (instead of the completion signal).
*
* Note that terminating the actor without first completing it, either with a success or a
* failure, will prevent the actor triggering downstream completion and the stream will continue
* to run even though the source actor is dead. Therefore you should **not** attempt to
* manually terminate the actor such as with a [[akka.actor.PoisonPill]].
*
* The actor will be stopped when the stream is completed, failed or canceled from downstream,
* i.e. you can watch it to get notified when that happens.
*
* See also [[akka.stream.scaladsl.Source.queue]].
*
* @param completionMatcher catches the completion message to end the stream
* @param failureMatcher catches the failure message to fail the stream
* @param bufferSize The size of the buffer in element count
* @param overflowStrategy Strategy that is used when incoming elements cannot fit inside the buffer
*/
def actorRef[T](
completionMatcher: akka.japi.function.Function[Any, java.util.Optional[CompletionStrategy]],
failureMatcher: akka.japi.function.Function[Any, java.util.Optional[Throwable]],
bufferSize: Int,
overflowStrategy: OverflowStrategy): Source[T, ActorRef] =
new Source(scaladsl.Source.actorRef(new JavaPartialFunction[Any, CompletionStrategy] {
override def apply(x: Any, isCheck: Boolean): CompletionStrategy = {
val result = completionMatcher(x)
if (!result.isPresent) throw JavaPartialFunction.noMatch()
else result.get()
}
}, new JavaPartialFunction[Any, Throwable] {
override def apply(x: Any, isCheck: Boolean): Throwable = {
val result = failureMatcher(x)
if (!result.isPresent) throw JavaPartialFunction.noMatch()
else result.get()
}
}, bufferSize, overflowStrategy))
/**
* Creates a `Source` that is materialized as an [[akka.actor.ActorRef]].
* Messages sent to this actor will be emitted to the stream if there is demand from downstream,
* otherwise they will be buffered until request for demand is received.
*
* Depending on the defined [[akka.stream.OverflowStrategy]] it might drop elements if
* there is no space available in the buffer.
*
* The strategy [[akka.stream.OverflowStrategy.backpressure]] is not supported, and an
* IllegalArgument("Backpressure overflowStrategy not supported") will be thrown if it is passed as argument.
*
* The buffer can be disabled by using `bufferSize` of 0 and then received messages are dropped if there is no demand
* from downstream. When `bufferSize` is 0 the `overflowStrategy` does not matter. An async boundary is added after
* this Source; as such, it is never safe to assume the downstream will always generate demand.
*
* The stream can be completed successfully by sending the actor reference a [[akka.actor.Status.Success]]
* (whose content will be ignored) in which case already buffered elements will be signaled before signaling
* completion.
*
* The stream can be completed successfully by sending the actor reference a [[akka.actor.Status.Success]].
* If the content is [[akka.stream.CompletionStrategy.immediately]] the completion will be signaled immediately,
* otherwise if the content is [[akka.stream.CompletionStrategy.draining]] (or anything else)
* already buffered elements will be signaled before signaling completion.
* Sending [[akka.actor.PoisonPill]] will signal completion immediately but this behavior is deprecated and scheduled to be removed.
*
* The stream can be completed with failure by sending a [[akka.actor.Status.Failure]] to the
* actor reference. In case the Actor is still draining its internal buffer (after having received
* a [[akka.actor.Status.Success]]) before signaling completion and it receives a [[akka.actor.Status.Failure]],
* the failure will be signaled downstream immediately (instead of the completion signal).
*
* Note that terminating the actor without first completing it, either with a success or a
* failure, will prevent the actor triggering downstream completion and the stream will continue
* to run even though the source actor is dead. Therefore you should **not** attempt to
* manually terminate the actor such as with a [[akka.actor.PoisonPill]].
*
* The actor will be stopped when the stream is completed, failed or canceled from downstream,
* i.e. you can watch it to get notified when that happens.
*
* See also [[akka.stream.javadsl.Source.queue]].
*
* @param bufferSize The size of the buffer in element count
* @param overflowStrategy Strategy that is used when incoming elements cannot fit inside the buffer
*/
@Deprecated
@deprecated("Use variant accepting completion and failure matchers", "2.6.0")
def actorRef[T](bufferSize: Int, overflowStrategy: OverflowStrategy): Source[T, ActorRef] =
new Source(scaladsl.Source.actorRef({
case akka.actor.Status.Success(s: CompletionStrategy) => s
case akka.actor.Status.Success(_) => CompletionStrategy.Draining
case akka.actor.Status.Success => CompletionStrategy.Draining
}, { case akka.actor.Status.Failure(cause) => cause }, bufferSize, overflowStrategy))
/**
* Creates a `Source` that is materialized as an [[akka.actor.ActorRef]].
* Messages sent to this actor will be emitted to the stream if there is demand from downstream,
* and a new message will only be accepted after the previous messages has been consumed and acknowledged back.
* The stream will complete with failure if a message is sent before the acknowledgement has been replied back.
*
* The stream can be completed with failure by sending a message that is matched by `failureMatcher`. The extracted
* [[Throwable]] will be used to fail the stream. In case the Actor is still draining its internal buffer (after having received
* a message matched by `completionMatcher`) before signaling completion and it receives a message matched by `failureMatcher`,
* the failure will be signaled downstream immediately (instead of the completion signal).
*
* The actor will be stopped when the stream is completed, failed or canceled from downstream,
* i.e. you can watch it to get notified when that happens.
*/
def actorRefWithBackpressure[T](
ackMessage: Any,
completionMatcher: akka.japi.function.Function[Any, java.util.Optional[CompletionStrategy]],
failureMatcher: akka.japi.function.Function[Any, java.util.Optional[Throwable]]): Source[T, ActorRef] =
new Source(scaladsl.Source.actorRefWithBackpressure(ackMessage, new JavaPartialFunction[Any, CompletionStrategy] {
override def apply(x: Any, isCheck: Boolean): CompletionStrategy = {
val result = completionMatcher(x)
if (!result.isPresent) throw JavaPartialFunction.noMatch()
else result.get()
}
}, new JavaPartialFunction[Any, Throwable] {
override def apply(x: Any, isCheck: Boolean): Throwable = {
val result = failureMatcher(x)
if (!result.isPresent) throw JavaPartialFunction.noMatch()
else result.get()
}
}))
/**
* Creates a `Source` that is materialized as an [[akka.actor.ActorRef]].
* Messages sent to this actor will be emitted to the stream if there is demand from downstream,
* and a new message will only be accepted after the previous messages has been consumed and acknowledged back.
* The stream will complete with failure if a message is sent before the acknowledgement has been replied back.
*
* The stream can be completed with failure by sending a message that is matched by `failureMatcher`. The extracted
* [[Throwable]] will be used to fail the stream. In case the Actor is still draining its internal buffer (after having received
* a message matched by `completionMatcher`) before signaling completion and it receives a message matched by `failureMatcher`,
* the failure will be signaled downstream immediately (instead of the completion signal).
*
* The actor will be stopped when the stream is completed, failed or canceled from downstream,
* i.e. you can watch it to get notified when that happens.
*
* @deprecated Use actorRefWithBackpressure instead
*/
@Deprecated
@deprecated("Use actorRefWithBackpressure instead", "2.6.0")
def actorRefWithAck[T](
ackMessage: Any,
completionMatcher: akka.japi.function.Function[Any, java.util.Optional[CompletionStrategy]],
failureMatcher: akka.japi.function.Function[Any, java.util.Optional[Throwable]]): Source[T, ActorRef] =
new Source(scaladsl.Source.actorRefWithBackpressure(ackMessage, new JavaPartialFunction[Any, CompletionStrategy] {
override def apply(x: Any, isCheck: Boolean): CompletionStrategy = {
val result = completionMatcher(x)
if (!result.isPresent) throw JavaPartialFunction.noMatch()
else result.get()
}
}, new JavaPartialFunction[Any, Throwable] {
override def apply(x: Any, isCheck: Boolean): Throwable = {
val result = failureMatcher(x)
if (!result.isPresent) throw JavaPartialFunction.noMatch()
else result.get()
}
}))
/**
* Creates a `Source` that is materialized as an [[akka.actor.ActorRef]].
* Messages sent to this actor will be emitted to the stream if there is demand from downstream,
* and a new message will only be accepted after the previous messages has been consumed and acknowledged back.
* The stream will complete with failure if a message is sent before the acknowledgement has been replied back.
*
* The stream can be completed successfully by sending the actor reference a [[akka.actor.Status.Success]].
* If the content is [[akka.stream.CompletionStrategy.immediately]] the completion will be signaled immediately,
* otherwise if the content is [[akka.stream.CompletionStrategy.draining]] (or anything else)
* already buffered element will be signaled before signaling completion.
*
* The stream can be completed with failure by sending a [[akka.actor.Status.Failure]] to the
* actor reference. In case the Actor is still draining its internal buffer (after having received
* a [[akka.actor.Status.Success]]) before signaling completion and it receives a [[akka.actor.Status.Failure]],
* the failure will be signaled downstream immediately (instead of the completion signal).
*
* The actor will be stopped when the stream is completed, failed or canceled from downstream,
* i.e. you can watch it to get notified when that happens.
*/
@Deprecated
@deprecated("Use actorRefWithBackpressure accepting completion and failure matchers", "2.6.0")
def actorRefWithAck[T](ackMessage: Any): Source[T, ActorRef] =
new Source(scaladsl.Source.actorRefWithBackpressure(ackMessage, {
case akka.actor.Status.Success(s: CompletionStrategy) => s
case akka.actor.Status.Success(_) => CompletionStrategy.Draining
case akka.actor.Status.Success => CompletionStrategy.Draining
}, { case akka.actor.Status.Failure(cause) => cause }))
/**
* A graph with the shape of a source logically is a source, this method makes
* it so also in type.
*/
def fromGraph[T, M](g: Graph[SourceShape[T], M]): Source[T, M] =
g match {
case s: Source[T, M] => s
case s if s eq scaladsl.Source.empty => empty().asInstanceOf[Source[T, M]]
case other => new Source(scaladsl.Source.fromGraph(other))
}
/**
* Defers the creation of a [[Source]] until materialization. The `factory` function
* exposes [[Materializer]] which is going to be used during materialization and
* [[Attributes]] of the [[Source]] returned by this method.
*/
def fromMaterializer[T, M](
factory: BiFunction[Materializer, Attributes, Source[T, M]]): Source[T, CompletionStage[M]] =
scaladsl.Source.fromMaterializer((mat, attr) => factory(mat, attr).asScala).mapMaterializedValue(_.toJava).asJava
/**
* Defers the creation of a [[Source]] until materialization. The `factory` function
* exposes [[ActorMaterializer]] which is going to be used during materialization and
* [[Attributes]] of the [[Source]] returned by this method.
*/
@deprecated("Use 'fromMaterializer' instead", "2.6.0")
def setup[T, M](factory: BiFunction[ActorMaterializer, Attributes, Source[T, M]]): Source[T, CompletionStage[M]] =
scaladsl.Source.setup((mat, attr) => factory(mat, attr).asScala).mapMaterializedValue(_.toJava).asJava
/**
* Combines several sources with fan-in strategy like [[Merge]] or [[Concat]] into a single [[Source]].
*/
def combine[T, U](
first: Source[T, _ <: Any],
second: Source[T, _ <: Any],
rest: java.util.List[Source[T, _ <: Any]],
strategy: function.Function[java.lang.Integer, _ <: Graph[UniformFanInShape[T, U], NotUsed]])
: Source[U, NotUsed] = {
val seq = if (rest != null) Util.immutableSeq(rest).map(_.asScala) else immutable.Seq()
new Source(scaladsl.Source.combine(first.asScala, second.asScala, seq: _*)(num => strategy.apply(num)))
}
/**
* Combines two sources with fan-in strategy like `Merge` or `Concat` and returns `Source` with a materialized value.
*/
def combineMat[T, U, M1, M2, M](
first: Source[T, M1],
second: Source[T, M2],
strategy: function.Function[java.lang.Integer, _ <: Graph[UniformFanInShape[T, U], NotUsed]],
combine: function.Function2[M1, M2, M]): Source[U, M] = {
new Source(
scaladsl.Source.combineMat(first.asScala, second.asScala)(num => strategy.apply(num))(combinerToScala(combine)))
}
/**
* Combine the elements of multiple streams into a stream of lists.
*/
def zipN[T](sources: java.util.List[Source[T, _ <: Any]]): Source[java.util.List[T], NotUsed] = {
val seq = if (sources != null) Util.immutableSeq(sources).map(_.asScala) else immutable.Seq()
new Source(scaladsl.Source.zipN(seq).map(_.asJava))
}
/*
* Combine the elements of multiple streams into a stream of lists using a combiner function.
*/
def zipWithN[T, O](
zipper: function.Function[java.util.List[T], O],
sources: java.util.List[Source[T, _ <: Any]]): Source[O, NotUsed] = {
val seq = if (sources != null) Util.immutableSeq(sources).map(_.asScala) else immutable.Seq()
new Source(scaladsl.Source.zipWithN[T, O](seq => zipper.apply(seq.asJava))(seq))
}
/**
* Creates a `Source` that is materialized as an [[akka.stream.javadsl.SourceQueueWithComplete]].
* You can push elements to the queue and they will be emitted to the stream if there is demand from downstream,
* otherwise they will be buffered until request for demand is received. Elements in the buffer will be discarded
* if downstream is terminated.
*
* Depending on the defined [[akka.stream.OverflowStrategy]] it might drop elements if
* there is no space available in the buffer.
*
* Acknowledgement mechanism is available.
* [[akka.stream.javadsl.SourceQueueWithComplete.offer]] returns `CompletionStage` which completes with
* `QueueOfferResult.enqueued` if element was added to buffer or sent downstream. It completes with
* `QueueOfferResult.dropped` if element was dropped. Can also complete with `QueueOfferResult.Failure` -
* when stream failed or `QueueOfferResult.QueueClosed` when downstream is completed.
*
* The strategy [[akka.stream.OverflowStrategy.backpressure]] will not complete last `offer():CompletionStage`
* call when buffer is full.
*
* You can watch accessibility of stream with [[akka.stream.javadsl.SourceQueueWithComplete.watchCompletion]].
* It returns a future that completes with success when this operator is completed or fails when stream is failed.
*
* The buffer can be disabled by using `bufferSize` of 0 and then received message will wait
* for downstream demand unless there is another message waiting for downstream demand, in that case
* offer result will be completed according to the overflow strategy.
*
* SourceQueue that current source is materialized to is for single thread usage only.
*
* @param bufferSize size of buffer in element count
* @param overflowStrategy Strategy that is used when incoming elements cannot fit inside the buffer
*/
def queue[T](bufferSize: Int, overflowStrategy: OverflowStrategy): Source[T, SourceQueueWithComplete[T]] =
new Source(scaladsl.Source.queue[T](bufferSize, overflowStrategy).mapMaterializedValue(_.asJava))
/**
* Start a new `Source` from some resource which can be opened, read and closed.
* Interaction with resource happens in a blocking way.
*
* Example:
* {{{
* Source.unfoldResource(
* () -> new BufferedReader(new FileReader("...")),
* reader -> reader.readLine(),
* reader -> reader.close())
* }}}
*
* You can use the supervision strategy to handle exceptions for `read` function. All exceptions thrown by `create`
* or `close` will fail the stream.
*
* `Restart` supervision strategy will close and create blocking IO again. Default strategy is `Stop` which means
* that stream will be terminated on error in `read` function by default.
*
* You can configure the default dispatcher for this Source by changing the `akka.stream.materializer.blocking-io-dispatcher` or
* set it for a given Source by using [[ActorAttributes]].
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* @param create - function that is called on stream start and creates/opens resource.
* @param read - function that reads data from opened resource. It is called each time backpressure signal
* is received. Stream calls close and completes when `read` returns None.
* @param close - function that closes resource
*/
def unfoldResource[T, S](
create: function.Creator[S],
read: function.Function[S, Optional[T]],
close: function.Procedure[S]): javadsl.Source[T, NotUsed] =
new Source(scaladsl.Source.unfoldResource[T, S](create.create _, (s: S) => read.apply(s).asScala, close.apply))
/**
* Start a new `Source` from some resource which can be opened, read and closed.
* It's similar to `unfoldResource` but takes functions that return `CompletionStage` instead of plain values.
*
* You can use the supervision strategy to handle exceptions for `read` function or failures of produced `Futures`.
* All exceptions thrown by `create` or `close` as well as fails of returned futures will fail the stream.
*
* `Restart` supervision strategy will close and create resource. Default strategy is `Stop` which means
* that stream will be terminated on error in `read` function (or future) by default.
*
* You can configure the default dispatcher for this Source by changing the `akka.stream.materializer.blocking-io-dispatcher` or
* set it for a given Source by using [[ActorAttributes]].
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* @param create - function that is called on stream start and creates/opens resource.
* @param read - function that reads data from opened resource. It is called each time backpressure signal
* is received. Stream calls close and completes when `CompletionStage` from read function returns None.
* @param close - function that closes resource
*/
def unfoldResourceAsync[T, S](
create: function.Creator[CompletionStage[S]],
read: function.Function[S, CompletionStage[Optional[T]]],
close: function.Function[S, CompletionStage[Done]]): javadsl.Source[T, NotUsed] =
new Source(
scaladsl.Source.unfoldResourceAsync[T, S](
() => create.create().toScala,
(s: S) => read.apply(s).toScala.map(_.asScala)(akka.dispatch.ExecutionContexts.sameThreadExecutionContext),
(s: S) => close.apply(s).toScala))
/**
* Upcast a stream of elements to a stream of supertypes of that element. Useful in combination with
* fan-in operators where you do not want to pay the cost of casting each element in a `map`.
*
* Example:
*
* {{{
* Source apples = Source.single(new Apple());
* Source oranges = Source.single(new Orange());
* Source appleFruits = Source.upcast(apples);
* Source orangeFruits = Source.upcast(oranges);
*
* Source fruits = appleFruits.merge(orangeFruits);
* }}}
*
* @tparam SuperOut a supertype to the type of elements in stream
* @return A source with the supertype as elements
*/
def upcast[SuperOut, Out <: SuperOut, Mat](source: Source[Out, Mat]): Source[SuperOut, Mat] =
source.asInstanceOf[Source[SuperOut, Mat]]
}
/**
* Java API
*
* A `Source` is a set of stream processing steps that has one open output and an attached input.
* Can be used as a `Publisher`
*/
final class Source[Out, Mat](delegate: scaladsl.Source[Out, Mat]) extends Graph[SourceShape[Out], Mat] {
import akka.util.ccompat.JavaConverters._
override def shape: SourceShape[Out] = delegate.shape
override def traversalBuilder: LinearTraversalBuilder = delegate.traversalBuilder
override def toString: String = delegate.toString
/**
* Converts this Java DSL element to its Scala DSL counterpart.
*/
def asScala: scaladsl.Source[Out, Mat] = delegate
/**
* Transform only the materialized value of this Source, leaving all other properties as they were.
*/
def mapMaterializedValue[Mat2](f: function.Function[Mat, Mat2]): Source[Out, Mat2] =
new Source(delegate.mapMaterializedValue(f.apply _))
/**
* Materializes this Source, immediately returning (1) its materialized value, and (2) a new Source
* that can be used to consume elements from the newly materialized Source.
*
* Note that the `ActorSystem` can be used as the `systemProvider` parameter.
*/
def preMaterialize(systemProvider: ClassicActorSystemProvider)
: Pair[Mat @uncheckedVariance, Source[Out @uncheckedVariance, NotUsed]] = {
val (mat, src) = delegate.preMaterialize()(SystemMaterializer(systemProvider.classicSystem).materializer)
Pair(mat, new Source(src))
}
/**
* Materializes this Source, immediately returning (1) its materialized value, and (2) a new Source
* that can be used to consume elements from the newly materialized Source.
*
* Prefer the method taking an `ActorSystem` unless you have special requirements.
*/
def preMaterialize(
materializer: Materializer): Pair[Mat @uncheckedVariance, Source[Out @uncheckedVariance, NotUsed]] = {
val (mat, src) = delegate.preMaterialize()(materializer)
Pair(mat, new Source(src))
}
/**
* Transform this [[Source]] by appending the given processing operators.
* {{{
* +----------------------------+
* | Resulting Source |
* | |
* | +------+ +------+ |
* | | | | | |
* | | this | ~Out~> | flow | ~~> T
* | | | | | |
* | +------+ +------+ |
* +----------------------------+
* }}}
* 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.
*/
def via[T, M](flow: Graph[FlowShape[Out, T], M]): javadsl.Source[T, Mat] =
new Source(delegate.via(flow))
/**
* Transform this [[Source]] by appending the given processing operators.
* {{{
* +----------------------------+
* | Resulting Source |
* | |
* | +------+ +------+ |
* | | | | | |
* | | this | ~Out~> | flow | ~~> T
* | | | | | |
* | +------+ +------+ |
* +----------------------------+
* }}}
* 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.Source[T, M2] =
new Source(delegate.viaMat(flow)(combinerToScala(combine)))
/**
* Connect this [[Source]] to a [[Sink]], concatenating the processing steps of both.
* {{{
* +----------------------------+
* | Resulting RunnableGraph |
* | |
* | +------+ +------+ |
* | | | | | |
* | | this | ~Out~> | sink | |
* | | | | | |
* | +------+ +------+ |
* +----------------------------+
* }}}
* 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.
*/
def to[M](sink: Graph[SinkShape[Out], M]): javadsl.RunnableGraph[Mat] =
RunnableGraph.fromGraph(delegate.to(sink))
/**
* Connect this [[Source]] to a [[Sink]], concatenating the processing steps of both.
* {{{
* +----------------------------+
* | Resulting RunnableGraph |
* | |
* | +------+ +------+ |
* | | | | | |
* | | this | ~Out~> | sink | |
* | | | | | |
* | +------+ +------+ |
* +----------------------------+
* }}}
* 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.RunnableGraph[M2] =
RunnableGraph.fromGraph(delegate.toMat(sink)(combinerToScala(combine)))
/**
* Connect this `Source` to a `Sink` and run it. The returned value is the materialized value
* of the `Sink`, e.g. the `Publisher` of a `Sink.asPublisher`.
*
* Note that the classic or typed `ActorSystem` can be used as the `systemProvider` parameter.
*/
def runWith[M](sink: Graph[SinkShape[Out], M], systemProvider: ClassicActorSystemProvider): M =
delegate.runWith(sink)(SystemMaterializer(systemProvider.classicSystem).materializer)
/**
* Connect this `Source` to a `Sink` and run it. The returned value is the materialized value
* of the `Sink`, e.g. the `Publisher` of a `Sink.asPublisher`.
*
* Prefer the method taking an `ActorSystem` unless you have special requirements
*/
def runWith[M](sink: Graph[SinkShape[Out], M], materializer: Materializer): M =
delegate.runWith(sink)(materializer)
/**
* Shortcut for running this `Source` with a fold function.
* The given function is invoked for every received element, giving it its previous
* output (or the given `zero` value) and the element as input.
* The returned [[java.util.concurrent.CompletionStage]] will be completed with value of the final
* function evaluation when the input stream ends, or completed with `Failure`
* if there is a failure is signaled in the stream.
*
* Note that the classic or typed `ActorSystem` can be used as the `systemProvider` parameter.
*/
def runFold[U](
zero: U,
f: function.Function2[U, Out, U],
systemProvider: ClassicActorSystemProvider): CompletionStage[U] =
runWith(Sink.fold(zero, f), systemProvider)
/**
* Shortcut for running this `Source` with a fold function.
* The given function is invoked for every received element, giving it its previous
* output (or the given `zero` value) and the element as input.
* The returned [[java.util.concurrent.CompletionStage]] will be completed with value of the final
* function evaluation when the input stream ends, or completed with `Failure`
* if there is a failure is signaled in the stream.
*
* Prefer the method taking an ActorSystem unless you have special requirements.
*/
def runFold[U](zero: U, f: function.Function2[U, Out, U], materializer: Materializer): CompletionStage[U] =
runWith(Sink.fold(zero, f), materializer)
/**
* Shortcut for running this `Source` with an asynchronous fold function.
* The given function is invoked for every received element, giving it its previous
* output (or the given `zero` value) and the element as input.
* The returned [[java.util.concurrent.CompletionStage]] will be completed with value of the final
* function evaluation when the input stream ends, or completed with `Failure`
* if there is a failure is signaled in the stream.
*
* Note that the classic or typed `ActorSystem` can be used as the `systemProvider` parameter.
*/
def runFoldAsync[U](
zero: U,
f: function.Function2[U, Out, CompletionStage[U]],
systemProvider: ClassicActorSystemProvider): CompletionStage[U] = runWith(Sink.foldAsync(zero, f), systemProvider)
/**
* Shortcut for running this `Source` with an asynchronous fold function.
* The given function is invoked for every received element, giving it its previous
* output (or the given `zero` value) and the element as input.
* The returned [[java.util.concurrent.CompletionStage]] will be completed with value of the final
* function evaluation when the input stream ends, or completed with `Failure`
* if there is a failure is signaled in the stream.
*
* Prefer the method taking an `ActorSystem` unless you have special requirements
*/
def runFoldAsync[U](
zero: U,
f: function.Function2[U, Out, CompletionStage[U]],
materializer: Materializer): CompletionStage[U] = runWith(Sink.foldAsync(zero, f), materializer)
/**
* Shortcut for running this `Source` with a reduce function.
* The given function is invoked for every received element, giving it its previous
* output (from the second ones) an the element as input.
* The returned [[java.util.concurrent.CompletionStage]] will be completed with value of the final
* function evaluation when the input stream ends, or completed with `Failure`
* if there is a failure is signaled in the stream.
*
* If the stream is empty (i.e. completes before signalling any elements),
* the reduce operator will fail its downstream with a [[NoSuchElementException]],
* which is semantically in-line with that Scala's standard library collections
* do in such situations.
*
* Note that the classic or typed `ActorSystem` can be used as the `systemProvider` parameter.
*/
def runReduce(
f: function.Function2[Out, Out, Out],
systemProvider: ClassicActorSystemProvider): CompletionStage[Out] =
runWith(Sink.reduce(f), systemProvider.classicSystem)
/**
* Shortcut for running this `Source` with a reduce function.
* The given function is invoked for every received element, giving it its previous
* output (from the second ones) an the element as input.
* The returned [[java.util.concurrent.CompletionStage]] will be completed with value of the final
* function evaluation when the input stream ends, or completed with `Failure`
* if there is a failure is signaled in the stream.
*
* If the stream is empty (i.e. completes before signalling any elements),
* the reduce operator will fail its downstream with a [[NoSuchElementException]],
* which is semantically in-line with that Scala's standard library collections
* do in such situations.
*
* Prefer the method taking an `ActorSystem` unless you have special requirements
*/
def runReduce(f: function.Function2[Out, Out, Out], materializer: Materializer): CompletionStage[Out] =
runWith(Sink.reduce(f), materializer)
/**
* Concatenate this [[Source]] with the given one, meaning that once current
* is exhausted and all result elements have been generated,
* the given source elements will be produced.
*
* Note that given [[Source]] is materialized together with this Flow and just kept
* from producing elements by asserting back-pressure until its time comes.
*
* If this [[Source]] gets upstream error - no elements from the given [[Source]] will be pulled.
*
* '''Emits when''' element is available from current source 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.Source[Out, Mat] =
new Source(delegate.concat(that))
/**
* Concatenate this [[Source]] with the given one, meaning that once current
* is exhausted and all result elements have been generated,
* the given source elements will be produced.
*
* Note that given [[Source]] is materialized together with this Flow and just kept
* from producing elements by asserting back-pressure until its time comes.
*
* If this [[Source]] 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.Source[Out, M2] =
new Source(delegate.concatMat(that)(combinerToScala(matF)))
/**
* Prepend the given [[Source]] to this one, meaning that once the given source
* is exhausted and all result elements have been generated, the current source's
* elements will be produced.
*
* Note that the current [[Source]] is materialized together with this Flow 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 [[Source]] will be pulled.
*
* '''Emits when''' element is available from current source or from the given [[Source]] when current is completed
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' given [[Source]] completes
*
* '''Cancels when''' downstream cancels
*/
def prepend[M](that: Graph[SourceShape[Out], M]): javadsl.Source[Out, Mat] =
new Source(delegate.prepend(that))
/**
* Prepend the given [[Source]] to this one, meaning that once the given source
* is exhausted and all result elements have been generated, the current source's
* elements will be produced.
*
* Note that the current [[Source]] is materialized together with this Flow 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 [[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 [[#prepend]].
*/
def prependMat[M, M2](
that: Graph[SourceShape[Out], M],
matF: function.Function2[Mat, M, M2]): javadsl.Source[Out, M2] =
new Source(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 operator 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.Source[Out, Mat] =
new Source(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[M, M2](
secondary: Graph[SourceShape[Out], M],
matF: function.Function2[Mat, M, M2]): javadsl.Source[Out, M2] =
new Source(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]].
*
* It is similar to [[#wireTap]] but will backpressure instead of dropping elements when the given [[Sink]] is not ready.
*
* '''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.Source[Out, Mat] =
new Source(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 similar to [[#wireTapMat]] but will backpressure instead of dropping elements when the given [[Sink]] is not 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 [[#alsoTo]]
*/
def alsoToMat[M2, M3](
that: Graph[SinkShape[Out], M2],
matF: function.Function2[Mat, M2, M3]): javadsl.Source[Out, M3] =
new Source(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.Source[Out, Mat] =
new Source(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.Source[Out, M3] =
new Source(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.
*
* It is similar to [[#alsoTo]] which does backpressure instead of dropping elements.
*
* '''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.Source[Out, Mat] =
new Source(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 similar to [[#alsoToMat]] which does backpressure instead of dropping elements.
*
* 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.Source[Out, M3] =
new Source(delegate.wireTapMat(that)(combinerToScala(matF)))
/**
* Interleave is a deterministic merge of the given [[Source]] with elements of this [[Source]].
* It first emits `segmentSize` number of elements from this flow to downstream, then - same amount for `that` source,
* then repeat process.
*
* Example:
* {{{
* Source.from(Arrays.asList(1, 2, 3)).interleave(Source.from(Arrays.asList(4, 5, 6, 7), 2)
* // 1, 2, 4, 5, 3, 6, 7
* }}}
*
* After one of sources is complete than all the rest elements will be emitted from the second one
*
* If one of sources 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''' this [[Source]] and given one completes
*
* '''Cancels when''' downstream cancels
*/
def interleave(that: Graph[SourceShape[Out], _], segmentSize: Int): javadsl.Source[Out, Mat] =
new Source(delegate.interleave(that, segmentSize))
/**
* Interleave is a deterministic merge of the given [[Source]] with elements of this [[Flow]].
* It first emits `segmentSize` number of elements from this flow to downstream, then - same amount for `that` source,
* then repeat process.
*
* If eagerClose is false and one of the upstreams complete the elements from the other upstream will continue passing
* through the interleave operator. 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.Source[Out, Mat] =
new Source(delegate.interleave(that, segmentSize, eagerClose))
/**
* Interleave is a deterministic merge of the given [[Source]] with elements of this [[Source]].
* It first emits `segmentSize` number of elements from this flow to downstream, then - same amount for `that` source,
* then repeat process.
*
* After one of sources is complete than all the rest elements will be emitted from the second one
*
* If one of sources 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.Source[Out, M2] =
new Source(delegate.interleaveMat(that, segmentSize)(combinerToScala(matF)))
/**
* Interleave is a deterministic merge of the given [[Source]] with elements of this [[Source]].
* 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 operator. 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.Source[Out, M2] =
new Source(delegate.interleaveMat(that, segmentSize, eagerClose)(combinerToScala(matF)))
/**
* Merge the given [[Source]] to the current one, 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.Source[Out, Mat] =
new Source(delegate.merge(that))
/**
* Merge the given [[Source]] to the current one, 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.Source[Out, Mat] =
new Source(delegate.merge(that, eagerComplete))
/**
* Merge the given [[Source]] to the current one, 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.Source[Out, M2] =
new Source(delegate.mergeMat(that)(combinerToScala(matF)))
/**
* Merge the given [[Source]] to the current one, 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.Source[Out, M2] =
new Source(delegate.mergeMat(that, eagerComplete)(combinerToScala(matF)))
/**
* MergeLatest joins elements from N input streams into stream of lists of size N.
* i-th element in list is the latest emitted element from i-th input stream.
* MergeLatest emits list for each element emitted from some input stream,
* but only after each input stream emitted at least one element.
*
* '''Emits when''' an element is available from some input and each input emits at least one element from stream start
*
* '''Completes when''' all upstreams complete (eagerClose=false) or one upstream completes (eagerClose=true)
*/
def mergeLatest[M](
that: Graph[SourceShape[Out], M],
eagerComplete: Boolean): javadsl.Source[java.util.List[Out], Mat] =
new Source(delegate.mergeLatest(that, eagerComplete).map(_.asJava))
/**
* MergeLatest joins elements from N input streams into stream of lists of size N.
* i-th element in list is the latest emitted element from i-th input stream.
* MergeLatest emits list for each element emitted from some input stream,
* but only after each input stream emitted at least one element.
*
* @see [[#mergeLatest]].
*
* 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 mergeLatestMat[Mat2, Mat3](
that: Graph[SourceShape[Out], Mat2],
eagerComplete: Boolean,
matF: function.Function2[Mat, Mat2, Mat3]): javadsl.Source[java.util.List[Out], Mat3] =
new Source(delegate.mergeLatestMat(that, eagerComplete)(combinerToScala(matF))).map(_.asJava)
/**
* Merge two sources. Prefer one source if both sources have elements ready.
*
* '''emits''' when one of the inputs has an element available. If multiple have elements available, prefer the 'right' one when 'preferred' is 'true', or the 'left' one when 'preferred' is 'false'.
*
* '''backpressures''' when downstream backpressures
*
* '''completes''' when all upstreams complete (This behavior is changeable to completing when any upstream completes by setting `eagerComplete=true`.)
*/
def mergePreferred[M](
that: Graph[SourceShape[Out], M],
preferred: Boolean,
eagerComplete: Boolean): javadsl.Source[Out, Mat] =
new Source(delegate.mergePreferred(that, preferred, eagerComplete))
/**
* Merge two sources. Prefer one source if both sources have elements ready.
*
* @see [[#mergePreferred]]
*
* 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 mergePreferredMat[Mat2, Mat3](
that: Graph[SourceShape[Out], Mat2],
preferred: Boolean,
eagerComplete: Boolean,
matF: function.Function2[Mat, Mat2, Mat3]): javadsl.Source[Out, Mat3] =
new Source(delegate.mergePreferredMat(that, preferred, eagerComplete)(combinerToScala(matF)))
/**
* Merge two sources. Prefer the sources depending on the 'priority' parameters.
*
* '''emits''' when one of the inputs has an element available, preferring inputs based on the 'priority' parameters if both have elements available
*
* '''backpressures''' when downstream backpressures
*
* '''completes''' when both upstreams complete (This behavior is changeable to completing when any upstream completes by setting `eagerComplete=true`.)
*/
def mergePrioritized[M](
that: Graph[SourceShape[Out], M],
leftPriority: Int,
rightPriority: Int,
eagerComplete: Boolean): javadsl.Source[Out, Mat] =
new Source(delegate.mergePrioritized(that, leftPriority, rightPriority, eagerComplete))
/**
* Merge multiple sources. Prefer the sources depending on the 'priority' parameters.
*
* 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 mergePrioritizedMat[Mat2, Mat3](
that: Graph[SourceShape[Out], Mat2],
leftPriority: Int,
rightPriority: Int,
eagerComplete: Boolean,
matF: function.Function2[Mat, Mat2, Mat3]): javadsl.Source[Out, Mat3] =
new Source(delegate.mergePrioritizedMat(that, leftPriority, rightPriority, eagerComplete)(combinerToScala(matF)))
/**
* Merge the given [[Source]] to this [[Source]], 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: util.Comparator[Out]): javadsl.Source[Out, Mat] =
new Source(delegate.mergeSorted(that)(Ordering.comparatorToOrdering(comp)))
/**
* Merge the given [[Source]] to this [[Source]], 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: util.Comparator[Out],
matF: function.Function2[Mat, Mat2, Mat3]): javadsl.Source[Out, Mat3] =
new Source(delegate.mergeSortedMat(that)(combinerToScala(matF))(Ordering.comparatorToOrdering(comp)))
/**
* Combine the elements of current [[Source]] and the given one into a stream of tuples.
*
* '''Emits when''' all of the inputs has an element available
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' any upstream completes
*
* '''Cancels when''' downstream cancels
*/
def zip[T](that: Graph[SourceShape[T], _]): javadsl.Source[Out @uncheckedVariance Pair T, Mat] =
zipMat(that, Keep.left)
/**
* Combine the elements of current [[Source]] and the given one 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.Source[Out @uncheckedVariance Pair T, M2] =
this.viaMat(Flow.create[Out].zipMat(that, Keep.right[NotUsed, M]), matF)
/**
* Combine the elements of current flow and the given [[Source]] into a stream of tuples.
*
* '''Emits when''' at first emits when both inputs emit, and then as long as any input emits (coupled to the default value of the completed input).
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' all upstream completes
*
* '''Cancels when''' downstream cancels
*/
def zipAll[U, A >: Out](that: Graph[SourceShape[U], _], thisElem: A, thatElem: U): Source[Pair[A, U], Mat] =
new Source(delegate.zipAll(that, thisElem, thatElem).map { case (a, u) => Pair.create(a, u) })
/**
* Combine the elements of current flow and the given [[Source]] into a stream of tuples.
*
* @see [[#zipAll]]
*
* '''Emits when''' at first emits when both inputs emit, and then as long as any input emits (coupled to the default value of the completed input).
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' all upstream completes
*
* '''Cancels when''' downstream cancels
*/
def zipAllMat[U, Mat2, Mat3, A >: Out](that: Graph[SourceShape[U], Mat2], thisElem: A, thatElem: U)(
matF: (Mat, Mat2) => Mat3): Source[Pair[A, U], Mat3] =
new Source(delegate.zipAllMat(that, thisElem, thatElem)(matF).map { case (a, u) => Pair.create(a, u) })
/**
* Combine the elements of 2 streams into a stream of tuples, picking always the latest element of each.
*
* A `ZipLatest` has a `left` and a `right` input port and one `out` port.
*
* No element is emitted until at least one element from each Source becomes available.
*
* '''Emits when''' all of the inputs have at least an element available, and then each time an element becomes
* * available on either of the inputs
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' any upstream completes
*
* '''Cancels when''' downstream cancels
*/
def zipLatest[T](that: Graph[SourceShape[T], _]): javadsl.Source[Out @uncheckedVariance Pair T, Mat] =
zipLatestMat(that, Keep.left)
/**
* Combine the elements of current [[Source]] and the given one into a stream of tuples, picking always the latest element of each.
*
* 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 [[#zipLatest]].
*/
def zipLatestMat[T, M, M2](
that: Graph[SourceShape[T], M],
matF: function.Function2[Mat, M, M2]): javadsl.Source[Out @uncheckedVariance Pair T, M2] =
this.viaMat(Flow.create[Out].zipLatestMat(that, Keep.right[NotUsed, M]), matF)
/**
* Put together the elements of current [[Source]] and the given one
* into a stream of combined elements using a combiner function.
*
* '''Emits when''' all of the inputs has 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.Source[Out3, Mat] =
new Source(delegate.zipWith[Out2, Out3](that)(combinerToScala(combine)))
/**
* Put together the elements of current [[Source]] and the given one
* 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.Source[Out3, M2] =
new Source(delegate.zipWithMat[Out2, Out3, M, M2](that)(combinerToScala(combine))(combinerToScala(matF)))
/**
* Combine the elements of multiple streams into a stream of combined elements using a combiner function,
* picking always the latest of the elements of each source.
*
* No element is emitted until at least one element from each Source becomes available. Whenever a new
* element appears, the zipping function is invoked with a tuple containing the new element
* and the other last seen elements.
*
* '''Emits when''' all of the inputs have at least an element available, and then each time an element becomes
* available on either of the inputs
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' any of the upstreams completes
*
* '''Cancels when''' downstream cancels
*/
def zipLatestWith[Out2, Out3](
that: Graph[SourceShape[Out2], _],
combine: function.Function2[Out, Out2, Out3]): javadsl.Source[Out3, Mat] =
new Source(delegate.zipLatestWith[Out2, Out3](that)(combinerToScala(combine)))
/**
* Put together the elements of current [[Source]] and the given one
* into a stream of combined elements using a combiner function,
* picking always the latest of the elements of each source.
*
* 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 [[#zipLatestWith]].
*/
def zipLatestWithMat[Out2, Out3, M, M2](
that: Graph[SourceShape[Out2], M],
combine: function.Function2[Out, Out2, Out3],
matF: function.Function2[Mat, M, M2]): javadsl.Source[Out3, M2] =
new Source(delegate.zipLatestWithMat[Out2, Out3, M, M2](that)(combinerToScala(combine))(combinerToScala(matF)))
/**
* Combine the elements of current [[Source]] 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: javadsl.Source[Pair[Out @uncheckedVariance, java.lang.Long], Mat] =
new Source(delegate.zipWithIndex.map { case (elem, index) => Pair[Out, java.lang.Long](elem, index) })
/**
* Shortcut for running this `Source` with a foreach procedure. The given procedure is invoked
* for each received element.
* The returned [[java.util.concurrent.CompletionStage]] will be completed normally when reaching the
* normal end of the stream, or completed exceptionally if there is a failure is signaled in
* the stream.
*
* Note that the classic or typed `ActorSystem` can be used as the `systemProvider` parameter.
*/
def runForeach(f: function.Procedure[Out], systemProvider: ClassicActorSystemProvider): CompletionStage[Done] =
runWith(Sink.foreach(f), systemProvider)
/**
* Shortcut for running this `Source` with a foreach procedure. The given procedure is invoked
* for each received element.
* The returned [[java.util.concurrent.CompletionStage]] will be completed normally when reaching the
* normal end of the stream, or completed exceptionally if there is a failure is signaled in
* the stream.
*
* Prefer the method taking an `ActorSystem` unless you have special requirements
*/
def runForeach(f: function.Procedure[Out], materializer: Materializer): CompletionStage[Done] =
runWith(Sink.foreach(f), materializer)
// COMMON OPS //
/**
* Transform this stream by applying the given function to each of the elements
* as they pass through this processing step.
*
* '''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.Source[T, Mat] =
new Source(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.
*
* If the wire-tap operation is slow (it backpressures), elements that would've been sent to it will be dropped instead.
*
* It is similar to [[#alsoTo]] which does backpressure instead of dropping elements.
*
* 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]] operator instead,
* along with appropriate `ActorAttributes.createLogLevels`.
*
* '''Emits when''' upstream emits an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels; Note that failures of the `f` function will not cause cancellation
*/
def wireTap(f: function.Procedure[Out]): javadsl.Source[Out, Mat] =
new Source(delegate.wireTap(f(_)))
/**
* 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 operator 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
*/
@deprecated("Use recoverWithRetries instead.", "2.4.4")
def recover(pf: PartialFunction[Throwable, Out]): javadsl.Source[Out, Mat] =
new Source(delegate.recover(pf))
/**
* 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 operator 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
*/
@deprecated("Use recoverWithRetries instead.", "2.4.4")
def recover(clazz: Class[_ <: Throwable], supplier: Supplier[Out]): javadsl.Source[Out, Mat] =
recover {
case elem if clazz.isInstance(elem) => supplier.get()
}
/**
* While similar to [[recover]] this operator 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 operator can recover the failure signal, but not the skipped elements, which will be dropped.
*
* Similarly 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.Source[Out, Mat] =
new Source(delegate.mapError(pf))
/**
* While similar to [[recover]] this operator 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 operator can recover the failure signal, but not the skipped elements, which will be dropped.
*
* Similarly 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[E <: Throwable](clazz: Class[E], f: function.Function[E, Throwable]): javadsl.Source[Out, Mat] =
mapError {
case err if clazz.isInstance(err) => f(clazz.cast(err))
}
/**
* 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 operator 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
*
*/
@silent("deprecated")
def recoverWith(pf: PartialFunction[Throwable, _ <: Graph[SourceShape[Out], NotUsed]]): Source[Out, Mat] =
new Source(delegate.recoverWith(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 operator 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
*
*/
def recoverWith(
clazz: Class[_ <: Throwable],
supplier: Supplier[Graph[SourceShape[Out], NotUsed]]): Source[Out, Mat] =
recoverWith {
case elem if clazz.isInstance(elem) => supplier.get()
}
/**
* 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 operator 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
*
*/
def recoverWithRetries(
attempts: Int,
pf: PartialFunction[Throwable, _ <: Graph[SourceShape[Out], NotUsed]]): Source[Out, Mat] =
new Source(delegate.recoverWithRetries(attempts, 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 operator 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 clazz the class object of the failure cause
* @param supplier supply the new Source to be materialized
*/
def recoverWithRetries(
attempts: Int,
clazz: Class[_ <: Throwable],
supplier: Supplier[Graph[SourceShape[Out], NotUsed]]): Source[Out, Mat] =
recoverWithRetries(attempts, {
case elem if clazz.isInstance(elem) => supplier.get()
})
/**
* 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 has been emitted
*
* '''Cancels when''' downstream cancels
*/
def mapConcat[T](f: function.Function[Out, _ <: java.lang.Iterable[T]]): javadsl.Source[T, Mat] =
new Source(delegate.mapConcat(elem => Util.immutableSeq(f.apply(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.Source[T, Mat] =
new Source(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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''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 CompletionStage is not completed
*
* '''Completes when''' upstream completes and all CompletionStages has been completed and all elements has been emitted
*
* '''Cancels when''' downstream cancels
*
* @see [[#mapAsyncUnordered]]
*/
def mapAsync[T](parallelism: Int, f: function.Function[Out, CompletionStage[T]]): javadsl.Source[T, Mat] =
new Source(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 CompletionStages
* 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 has been completed and all elements has been emitted
*
* '''Cancels when''' downstream cancels
*
* @see [[#mapAsync]]
*/
def mapAsyncUnordered[T](parallelism: Int, f: function.Function[Out, CompletionStage[T]]): javadsl.Source[T, Mat] =
new Source(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 operator 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 operator 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.Source[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 operator 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 operator are in-order with regards to the asks being issued
* (i.e. same behaviour as mapAsync).
*
* The operator 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.Source[S, Mat] =
new Source(delegate.ask[S](parallelism)(ref)(timeout, ClassTag(mapTo)))
/**
* The operator 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.Source[Out, Mat] =
new Source(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.Source[Out, Mat] =
new Source(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.Source[Out, Mat] =
new Source(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.Source[T, Mat] =
new Source(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.Source[T, Mat] =
new Source(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.Source[java.util.List[Out @uncheckedVariance], Mat] =
new Source(delegate.grouped(n).map(_.asJava))
/**
* 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
*
* '''Cancels when''' the defined number of elements has been taken or downstream cancels
*
* See also [[Flow.take]], [[Flow.takeWithin]], [[Flow.takeWhile]]
*/
def limit(n: Int): javadsl.Source[Out, Mat] = new Source(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
*
* '''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.Source[Out, Mat] = {
new Source(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): javadsl.Source[java.util.List[Out @uncheckedVariance], Mat] =
new Source(delegate.sliding(n, step).map(_.asJava))
/**
* 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.
*
* Note that the `zero` value must be immutable.
*
* '''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.Source[T, Mat] =
new Source(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.
*
* Note that the `zero` value must be immutable.
*
* '''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.Source[T, Mat] =
new Source(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.
*
* Note that the `zero` value must be immutable.
*
* '''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.Source[T, Mat] =
new Source(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.
*
* Note that the `zero` value must be immutable.
*
* '''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.Source[T, Mat] =
new Source(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.
*
* 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 @uncheckedVariance]): javadsl.Source[Out, Mat] =
new Source(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 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(list.intersperse(","))
* list.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.Source[Out, Mat] =
new Source(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 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.Source[Out, Mat] =
new Source(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.Source[java.util.List[Out @uncheckedVariance], Mat] =
new Source(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.
*/
@silent("deprecated")
def groupedWithin(n: Int, d: java.time.Duration): javadsl.Source[java.util.List[Out @uncheckedVariance], 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.Source[java.util.List[Out @uncheckedVariance], Mat] =
new Source(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.
*/
@silent("deprecated")
def groupedWeightedWithin(
maxWeight: Long,
costFn: function.Function[Out, java.lang.Long],
d: java.time.Duration): javadsl.Source[java.util.List[Out @uncheckedVariance], 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 `withAttributes(inputBuffer)`
*
* '''Emits when''' there is a pending element in the buffer and configured time for this element elapsed
* * EmitEarly - strategy do not wait to emit element if buffer is full
*
* '''Backpressures when''' depending on OverflowStrategy
* * Backpressure - backpressures when buffer is full
* * DropHead, DropTail, DropBuffer - never backpressures
* * Fail - fails the stream if buffer gets full
*
* '''Completes when''' upstream completes and buffered elements has 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): Source[Out, Mat] =
new Source(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 `withAttributes(inputBuffer)`
*
* '''Emits when''' there is a pending element in the buffer and configured time for this element elapsed
* * EmitEarly - strategy do not wait to emit element if buffer is full
*
* '''Backpressures when''' depending on OverflowStrategy
* * Backpressure - backpressures when buffer is full
* * DropHead, DropTail, DropBuffer - never backpressures
* * Fail - fails the stream if buffer gets full
*
* '''Completes when''' upstream completes and buffered elements has 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
*/
@silent("deprecated")
def delay(of: java.time.Duration, strategy: DelayOverflowStrategy): Source[Out, Mat] =
delay(of.asScala, strategy)
/**
* Shifts elements emission in time by an amount individually determined through delay strategy 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.
*
* It determines delay for each ongoing element invoking `DelayStrategy.nextDelay(elem: T): FiniteDuration`.
*
* Note that elements are not re-ordered: if an element is given a delay much shorter than its predecessor,
* it will still have to wait for the preceding element before being emitted.
* It is also important to notice that [[DelayStrategy]] can be stateful.
*
* 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 delayStrategySupplier creates new [[DelayStrategy]] object for each materialization
* @param overFlowStrategy Strategy that is used when incoming elements cannot fit inside the buffer
*/
def delayWith(
delayStrategySupplier: Supplier[DelayStrategy[Out]],
overFlowStrategy: DelayOverflowStrategy): Source[Out, Mat] =
new Source(delegate.delayWith(() => DelayStrategy.asScala(delayStrategySupplier.get), overFlowStrategy))
/**
* 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.Source[Out, Mat] =
new Source(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.Source[Out, Mat] =
new Source(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
*/
@silent("deprecated")
def dropWithin(d: java.time.Duration): javadsl.Source[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 if 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 [[Source.limit]], [[Source.limitWeighted]]
*/
def takeWhile(p: function.Predicate[Out], inclusive: Boolean): javadsl.Source[Out, Mat] =
new Source(delegate.takeWhile(p.test, inclusive))
/**
* Terminate processing (and cancel the upstream publisher) after predicate
* returns false for the first time. Due to input buffering some elements may have been
* requested from upstream publishers that will then not be processed downstream
* of this step.
*
* The stream will be completed without producing any elements if predicate is false for
* the first stream element.
*
* '''Emits when''' the predicate is true
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' predicate returned false or upstream completes
*
* '''Cancels when''' predicate returned false or downstream cancels
*
* See also [[Source.limit]], [[Source.limitWeighted]]
*/
def takeWhile(p: function.Predicate[Out]): javadsl.Source[Out, Mat] = new Source(delegate.takeWhile(p.test))
/**
* Discard elements at the beginning of the stream while predicate is true.
* No elements will be dropped after predicate first time returned false.
*
* 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
*
* @param p predicate is evaluated for each new element until first time returns false
*/
def dropWhile(p: function.Predicate[Out]): javadsl.Source[Out, Mat] = new Source(delegate.dropWhile(p.test))
/**
* 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
*/
def take(n: Long): javadsl.Source[Out, Mat] =
new Source(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
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def takeWithin(d: FiniteDuration): javadsl.Source[Out, Mat] =
new Source(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
*/
@silent("deprecated")
def takeWithin(d: java.time.Duration): javadsl.Source[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 [[Source.conflate]] [[Source.batch]] [[Source.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.Source[S, Mat] =
new Source(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 [[Source.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 [[Source.conflateWithSeed]] [[Source.batch]] [[Source.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.Source[Out, Mat] =
new Source(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 [[Source.conflate]], [[Source.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.Source[S, Mat] =
new Source(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 [[Source.conflate]], [[Source.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.Source[S, Mat] =
new Source(delegate.batchWeighted(max, costFn.apply, seed.apply)(aggregate.apply))
/**
* Allows a faster downstream to progress independently of a slower publisher by extrapolating elements from an older
* element until new element comes from the upstream. For example an expand step might repeat the last element for
* the subscriber until it receives an update from upstream.
*
* This element will never "drop" upstream elements as all elements go through at least one extrapolation step.
* This means that if the upstream is actually faster than the upstream it will be backpressured by the downstream
* subscriber.
*
* Expand does not support [[akka.stream.Supervision#restart]] and [[akka.stream.Supervision#resume]].
* Exceptions from the `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.Source[U, Mat] =
new Source(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]])
: Source[Out, Mat] =
new Source(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): Source[Out, Mat] =
new Source(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:
*
*
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 has 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.Source[Out, Mat] =
new Source(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 has 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 has been consumed and substream has been consumed
*
* '''Cancels when''' downstream cancels or substream cancels
*/
def prefixAndTail(n: Int): javadsl.Source[
Pair[java.util.List[Out @uncheckedVariance], javadsl.Source[Out @uncheckedVariance, NotUsed]],
Mat] =
new Source(delegate.prefixAndTail(n).map { case (taken, tail) => 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.
*
* WARNING: If `allowClosedSubstreamRecreation` is set to `false` (default behavior) the operator
* keeps track of all keys of streams that have already been closed. If you expect an infinite
* number of keys this can cause memory issues. Elements belonging to those keys are drained
* directly and not send to the substream.
*
* Note: If `allowClosedSubstreamRecreation` is set to `true` substream completion and incoming
* elements are subject to race-conditions. If elements arrive for a stream that is in the process
* of closing these elements might get lost.
*
* The object returned from this method is not a normal [[Flow]],
* it is a [[SubFlow]]. This means that after this operator 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
* @param f computes the key for each element
* @param allowClosedSubstreamRecreation enables recreation of already closed substreams if elements with their
* corresponding keys arrive after completion
*/
def groupBy[K](
maxSubstreams: Int,
f: function.Function[Out, K],
allowClosedSubstreamRecreation: Boolean): SubSource[Out, Mat] =
new SubSource(delegate.groupBy(maxSubstreams, f.apply, allowClosedSubstreamRecreation))
/**
* 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 [[SubSource]]. This means that after this operator 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 [[SubSource]] 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.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''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]): SubSource[Out @uncheckedVariance, Mat] =
new SubSource(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 [[SubSource]]. This means that after this operator 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 [[SubSource]] 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
*
* See also [[Source.splitAfter]].
*/
def splitWhen(p: function.Predicate[Out]): SubSource[Out, Mat] =
new SubSource(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]): SubSource[Out, Mat] =
new SubSource(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 [[SubSource]]. This means that after this operator 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 [[SubSource]] 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
*
* See also [[Source.splitWhen]].
*/
def splitAfter(p: function.Predicate[Out]): SubSource[Out, Mat] =
new SubSource(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]): SubSource[Out, Mat] =
new SubSource(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]]): Source[T, Mat] =
new Source(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]]): Source[T, Mat] =
new Source(delegate.flatMapMerge(breadth, o => f(o)))
/**
* If the first element has not passed through this operator 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.Source[Out, Mat] =
new Source(delegate.initialTimeout(timeout))
/**
* If the first element has not passed through this operator 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
*/
@silent("deprecated")
def initialTimeout(timeout: java.time.Duration): javadsl.Source[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.Source[Out, Mat] =
new Source(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
*/
@silent("deprecated")
def completionTimeout(timeout: java.time.Duration): javadsl.Source[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.Source[Out, Mat] =
new Source(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
*/
@silent("deprecated")
def idleTimeout(timeout: java.time.Duration): javadsl.Source[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.Source[Out, Mat] =
new Source(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
*/
@silent("deprecated")
def backpressureTimeout(timeout: java.time.Duration): javadsl.Source[Out, Mat] =
backpressureTimeout(timeout.asScala)
/**
* Injects additional elements if upstream does not emit for a configured amount of time. In other words, this
* operator 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.Source[Out, Mat] =
new Source(delegate.keepAlive(maxIdle, () => injectedElem.create()))
/**
* Injects additional elements if upstream does not emit for a configured amount of time. In other words, this
* operator 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
*/
@silent("deprecated")
def keepAlive(maxIdle: java.time.Duration, injectedElem: function.Creator[Out]): javadsl.Source[Out, Mat] =
keepAlive(maxIdle.asScala, injectedElem)
/**
* Sends elements downstream with speed limited to `elements/per`. In other words, this operator set the maximum rate
* for emitting messages. This operator 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.Source[Out, Mat] =
new Source(delegate.throttle(elements, per.asScala))
/**
* Sends elements downstream with speed limited to `elements/per`. In other words, this operator set the maximum rate
* for emitting messages. This operator 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.Source[Out, Mat] =
new Source(delegate.throttle(elements, per, maximumBurst, mode))
/**
* Sends elements downstream with speed limited to `elements/per`. In other words, this operator set the maximum rate
* for emitting messages. This operator 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.Source[Out, Mat] =
new Source(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 operator 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.Source[Out, Mat] =
new Source(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 operator 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.Source[Out, Mat] =
new Source(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 operator 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.Source[Out, Mat] =
new Source(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 operator 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: FiniteDuration, mode: ThrottleMode): javadsl.Source[Out, Mat] =
new Source(delegate.throttleEven(elements, per, mode))
/**
* This is a simplified version of throttle that spreads events evenly across the given time interval.
*
* Use this operator 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.Source[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 operator 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: (Out) => Int,
mode: ThrottleMode): javadsl.Source[Out, Mat] =
new Source(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 operator 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: (Out) => Int,
mode: ThrottleMode): javadsl.Source[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.Source[Out, Mat] = new Source(delegate.detach)
/**
* Materializes to `Future[Done]` that completes on getting termination message.
* The Future completes with success when received complete message from upstream or cancel
* from downstream. It fails with the same error when received error message from
* downstream.
*/
def watchTermination[M]()(matF: function.Function2[Mat, CompletionStage[Done], M]): javadsl.Source[Out, M] =
new Source(delegate.watchTermination()((left, right) => matF(left, right.toJava)))
/**
* Materializes to `FlowMonitor` 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.
*/
@Deprecated
@deprecated("Use monitor() or monitorMat(combine) instead", "2.5.17")
def monitor[M]()(combine: function.Function2[Mat, FlowMonitor[Out], M]): javadsl.Source[Out, M] =
new Source(delegate.monitorMat(combinerToScala(combine)))
/**
* 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 monitorMat[M](combine: function.Function2[Mat, FlowMonitor[Out], M]): javadsl.Source[Out, M] =
new Source(delegate.monitorMat(combinerToScala(combine)))
/**
* Materializes to `Pair>`, which is unlike most other operators (!),
* in which usually the default materialized value keeping semantics is to keep the left value
* (by passing `Keep.left()` to a `*Mat` version of a method). This operator is an exception from
* that rule and keeps both values since dropping its sole purpose is to introduce that materialized value.
*
* The `FlowMonitor` 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.
*/
def monitor(): Source[Out, Pair[Mat, FlowMonitor[Out]]] =
monitorMat(Keep.both)
/**
* 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.Source[Out, Mat] =
new Source(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
*/
@silent("deprecated")
def initialDelay(delay: java.time.Duration): javadsl.Source[Out, Mat] =
initialDelay(delay.asScala)
/**
* Replace the attributes of this [[Source]] with the given ones. If this Source is a composite
* of multiple graphs, new attributes on the composite will be less specific than attributes
* set directly on the individual graphs of the composite.
*/
override def withAttributes(attr: Attributes): javadsl.Source[Out, Mat] =
new Source(delegate.withAttributes(attr))
/**
* Add the given attributes to this [[Source]]. 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 Source is a composite of multiple graphs, new attributes on the composite will be
* less specific than attributes set directly on the individual graphs of the composite.
*/
override def addAttributes(attr: Attributes): javadsl.Source[Out, Mat] =
new Source(delegate.addAttributes(attr))
/**
* Add a ``name`` attribute to this Source.
*/
override def named(name: String): javadsl.Source[Out, Mat] =
new Source(delegate.named(name))
/**
* Put an asynchronous boundary around this `Source`
*/
override def async: javadsl.Source[Out, Mat] =
new Source(delegate.async)
/**
* Put an asynchronous boundary around this `Source`
*
* @param dispatcher Run the graph on this dispatcher
*/
override def async(dispatcher: String): javadsl.Source[Out, Mat] =
new Source(delegate.async(dispatcher))
/**
* Put an asynchronous boundary around this `Source`
*
* @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.Source[Out, Mat] =
new Source(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.Source[Out, Mat] =
new Source(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.Source[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.Source[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.Source[Out, Mat] =
this.log(name, ConstantFun.javaIdentityFunction[Out], null)
def asSourceWithContext[Ctx](extractContext: function.Function[Out, Ctx]): SourceWithContext[Out, Ctx, Mat] =
new scaladsl.SourceWithContext(this.asScala.map(x => (x, extractContext.apply(x)))).asJava
}
