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/*
* Copyright (C) 2015-2020 Lightbend Inc.
*/
package akka.stream.javadsl
import akka.NotUsed
import akka.event.LoggingAdapter
import akka.japi.{ function, Pair, Util }
import akka.stream._
import akka.util.ConstantFun
import akka.util.JavaDurationConverters._
import akka.util.ccompat.JavaConverters._
import scala.annotation.unchecked.uncheckedVariance
import scala.concurrent.duration.FiniteDuration
import java.util.Comparator
import java.util.function.Supplier
import scala.compat.java8.FutureConverters._
import java.util.concurrent.CompletionStage
import com.github.ghik.silencer.silent
import scala.reflect.ClassTag
object SubFlow {
/**
* 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`.
*
* @tparam SuperOut a supertype to the type of element flowing out of the flow
* @return A flow that accepts `In` and outputs elements of the super type
*/
def upcast[In, SuperOut, Out <: SuperOut, M](flow: SubFlow[In, Out, M]): SubFlow[In, SuperOut, M] =
flow.asInstanceOf[SubFlow[In, SuperOut, M]]
}
/**
* A “stream of streams” sub-flow of data elements, e.g. produced by `groupBy`.
* SubFlows cannot contribute to the super-flow’s materialized value since they
* are materialized later, during the runtime of the flow graph processing.
*/
class SubFlow[In, Out, Mat](
delegate: scaladsl.SubFlow[Out, Mat, scaladsl.Flow[In, Out, Mat]#Repr, scaladsl.Sink[In, Mat]]) {
/** Converts this Flow to its Scala DSL counterpart */
def asScala: scaladsl.SubFlow[Out, Mat, scaladsl.Flow[In, Out, Mat]#Repr, scaladsl.Sink[In, Mat]] @uncheckedVariance =
delegate
/**
* Flatten the sub-flows back into the super-flow by performing a merge
* without parallelism limit (i.e. having an unbounded number of sub-flows
* active concurrently).
*
* This is identical in effect to `mergeSubstreamsWithParallelism(Integer.MAX_VALUE)`.
*/
def mergeSubstreams(): Flow[In, Out, Mat] =
new Flow(delegate.mergeSubstreams)
/**
* Flatten the sub-flows back into the super-flow by performing a merge
* with the given parallelism limit. This means that only up to `parallelism`
* substreams will be executed at any given time. Substreams that are not
* yet executed are also not materialized, meaning that back-pressure will
* be exerted at the operator that creates the substreams when the parallelism
* limit is reached.
*/
def mergeSubstreamsWithParallelism(parallelism: Int): Flow[In, Out, Mat] =
new Flow(delegate.mergeSubstreamsWithParallelism(parallelism))
/**
* Flatten the sub-flows back into the super-flow by concatenating them.
* This is usually a bad idea when combined with `groupBy` since it can
* easily lead to deadlock—the concatenation does not consume from the second
* substream until the first has finished and the `groupBy` operator will get
* back-pressure from the second stream.
*
* This is identical in effect to `mergeSubstreamsWithParallelism(1)`.
*/
def concatSubstreams(): Flow[In, Out, Mat] =
new Flow(delegate.concatSubstreams)
/**
* Transform this [[Flow]] by appending the given processing steps.
*
* {{{
* +----------------------------+
* | Resulting Flow |
* | |
* | +------+ +------+ |
* | | | | | |
* In ~~> | 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
* [[Flow#viaMat viaMat]] if a different strategy is needed.
*/
def via[T, M](flow: Graph[FlowShape[Out, T], M]): SubFlow[In, T, Mat] =
new SubFlow(delegate.via(flow))
/**
* Connect this [[SubFlow]] to a [[Sink]], concatenating the processing steps of both.
* This means that all sub-flows that result from the previous sub-stream operator
* will be attached to the given sink.
* {{{
* +----------------------------+
* | Resulting Sink |
* | |
* | +------+ +------+ |
* | | | | | |
* In ~~> | flow | ~Out~> | sink | |
* | | | | | |
* | +------+ +------+ |
* +----------------------------+
* }}}
*
* Note that attributes set on the returned graph, including async boundaries are now for the entire graph and not
* the `SubFlow`. for example `async` will not have any effect as the returned graph is the entire, closed graph.
*/
def to(sink: Graph[SinkShape[Out], _]): Sink[In, Mat] =
new Sink(delegate.to(sink))
/**
* Transform this stream by applying the given function to each of the elements
* as they pass through this processing step.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the mapping function returns an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def map[T](f: function.Function[Out, T]): SubFlow[In, T, Mat] =
new SubFlow(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.logLevels`.
*
* '''Emits when''' upstream emits an element; the same element will be passed to the attached function,
* as well as to the downstream operator
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
*/
def wireTap(f: function.Procedure[Out]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.wireTap(f(_)))
/**
* Transform each input element into an `Iterable` of output elements that is
* then flattened into the output stream.
*
* Make sure that the `Iterable` is immutable or at least not modified after
* being used as an output sequence. Otherwise the stream may fail with
* `ConcurrentModificationException` or other more subtle errors may occur.
*
* The returned `Iterable` MUST NOT contain `null` values,
* as they are illegal as stream elements - according to the Reactive Streams specification.
*
* '''Emits when''' the mapping function returns an element or there are still remaining elements
* from the previously calculated collection
*
* '''Backpressures when''' downstream backpressures or there are still remaining elements from the
* previously calculated collection
*
* '''Completes when''' upstream completes and all remaining elements has been emitted
*
* '''Cancels when''' downstream cancels
*/
def mapConcat[T](f: function.Function[Out, java.lang.Iterable[T]]): SubFlow[In, T, Mat] =
new SubFlow(delegate.mapConcat { elem =>
Util.immutableSeq(f(elem))
})
/**
* Transform each input element into an `Iterable` of output elements that is
* then flattened into the output stream. The transformation is meant to be stateful,
* which is enabled by creating the transformation function anew for every materialization —
* the returned function will typically close over mutable objects to store state between
* invocations. For the stateless variant see [[#mapConcat]].
*
* Make sure that the `Iterable` is immutable or at least not modified after
* being used as an output sequence. Otherwise the stream may fail with
* `ConcurrentModificationException` or other more subtle errors may occur.
*
* The returned `Iterable` MUST NOT contain `null` values,
* as they are illegal as stream elements - according to the Reactive Streams specification.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the mapping function returns an element or there are still remaining elements
* from the previously calculated collection
*
* '''Backpressures when''' downstream backpressures or there are still remaining elements from the
* previously calculated collection
*
* '''Completes when''' upstream completes and all remaining elements has been emitted
*
* '''Cancels when''' downstream cancels
*/
def statefulMapConcat[T](f: function.Creator[function.Function[Out, java.lang.Iterable[T]]]): SubFlow[In, T, Mat] =
new SubFlow(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]]): SubFlow[In, T, Mat] =
new SubFlow(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 have 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]]): SubFlow[In, T, Mat] =
new SubFlow(delegate.mapAsyncUnordered(parallelism)(x => f(x).toScala))
/**
* 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]): SubFlow[In, Out, Mat] =
new SubFlow(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]): SubFlow[In, Out, Mat] =
new SubFlow(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]): SubFlow[In, T, Mat] =
new SubFlow(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.SubFlow[In, T, Mat] =
new SubFlow(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): SubFlow[In, java.util.List[Out @uncheckedVariance], Mat] =
new SubFlow(delegate.grouped(n).map(_.asJava)) // TODO optimize to one step
/**
* Ensure stream boundedness by limiting the number of elements from upstream.
* If the number of incoming elements exceeds max, it will signal
* upstream failure `StreamLimitException` downstream.
*
* Due to input buffering some elements may have been
* requested from upstream publishers that will then not be processed downstream
* of this step.
*
* The stream will be completed without producing any elements if `n` is zero
* or negative.
*
* '''Emits when''' the specified number of elements to take has not yet been reached
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' the defined number of elements has been taken or upstream completes
*
* '''Cancels when''' the defined number of elements has been taken or downstream cancels
*
* See also [[Flow.take]], [[Flow.takeWithin]], [[Flow.takeWhile]]
*/
def limit(n: Long): javadsl.SubFlow[In, Out, Mat] = new SubFlow(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.SubFlow[In, Out, Mat] = {
new SubFlow(delegate.limitWeighted(n)(costFn.apply))
}
/**
* Apply a sliding window over the stream and return the windows as groups of elements, with the last group
* possibly smaller than requested due to end-of-stream.
*
* `n` must be positive, otherwise IllegalArgumentException is thrown.
* `step` must be positive, otherwise IllegalArgumentException is thrown.
*
* '''Emits when''' enough elements have been collected within the window or upstream completed
*
* '''Backpressures when''' a window has been assembled and downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def sliding(n: Int, step: Int = 1): SubFlow[In, java.util.List[Out @uncheckedVariance], Mat] =
new SubFlow(delegate.sliding(n, step).map(_.asJava)) // TODO optimize to one step
/**
* Similar to `fold` but is not a terminal operation,
* emits its current value which starts at `zero` and then
* applies the current and next value to the given function `f`,
* emitting the next current value.
*
* If the function `f` throws an exception and the supervision decision is
* [[akka.stream.Supervision#restart]] current value starts at `zero` again
* the stream will continue.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* 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]): SubFlow[In, T, Mat] =
new SubFlow(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]]): SubFlow[In, T, Mat] =
new SubFlow(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]): SubFlow[In, T, Mat] =
new SubFlow(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]]): SubFlow[In, T, Mat] =
new SubFlow(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]): SubFlow[In, Out, Mat] =
new SubFlow(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(flow.intersperse(","))
* flow.intersperse(",").concat(Source.single("END"))
* }}}
*
* '''Emits when''' upstream emits (or before with the `start` element if provided)
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def intersperse(start: Out, inject: Out, end: Out): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, java.util.List[Out @uncheckedVariance], Mat] =
new SubFlow(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): SubFlow[In, 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.SubFlow[In, java.util.List[Out @uncheckedVariance], Mat] =
new SubFlow(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.SubFlow[In, 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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, 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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
dropWithin(d.asScala)
/**
* 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 1 after predicate returns false if `inclusive` or upstream completes
*
* '''Cancels when''' predicate returned false or downstream cancels
*/
def takeWhile(p: function.Predicate[Out]): SubFlow[In, Out, Mat] = takeWhile(p, false)
/**
* Terminate processing (and cancel the upstream publisher) after predicate
* returns false for the first time, including the first failed element iff inclusive is true
* Due to input buffering some elements may have been requested from upstream publishers
* that will then not be processed downstream of this step.
*
* The stream will be completed without producing any elements if predicate is false for
* the first stream element.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' the predicate is true
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' predicate returned false (or 1 after predicate returns false if `inclusive` or upstream completes
*
* '''Cancels when''' predicate returned false or downstream cancels
*/
def takeWhile(p: function.Predicate[Out], inclusive: Boolean): SubFlow[In, Out, Mat] =
new SubFlow(delegate.takeWhile(p.test, inclusive))
/**
* Discard elements at the beginning of the stream while predicate is true.
* All elements will be taken after predicate returns false first time.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' predicate returned false and for all following stream elements
*
* '''Backpressures when''' predicate returned false and downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def dropWhile(p: function.Predicate[Out]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.dropWhile(p.test))
/**
* Recover allows to send last element on failure and gracefully complete the stream
* Since the underlying failure signal onError arrives out-of-band, it might jump over existing elements.
* This 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
*
*/
def recover(pf: PartialFunction[Throwable, Out]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.recover(pf))
/**
* RecoverWith allows to switch to alternative Source on flow failure. It will stay in effect after
* a failure has been recovered so that each time there is a failure it is fed into the `pf` and a new
* Source may be materialized.
*
* Since the underlying failure signal onError arrives out-of-band, it might jump over existing elements.
* This 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
*
*/
@deprecated("Use recoverWithRetries instead.", "2.4.4")
def recoverWith(
pf: PartialFunction[Throwable, Graph[SourceShape[Out], NotUsed]]): SubFlow[In, Out, Mat @uncheckedVariance] =
new SubFlow(delegate.recoverWith(pf))
/**
* RecoverWithRetries allows to switch to alternative Source on flow failure. It will stay in effect after
* a failure has been recovered up to `attempts` number of times so that each time there is a failure
* it is fed into the `pf` and a new Source may be materialized. Note that if you pass in 0, this won't
* attempt to recover at all.
*
* A negative `attempts` number is interpreted as "infinite", which results in the exact same behavior as `recoverWith`.
*
* Since the underlying failure signal onError arrives out-of-band, it might jump over existing elements.
* This 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]]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.recoverWithRetries(attempts, 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(pf: PartialFunction[Throwable, Throwable]): SubFlow[In, Out, Mat @uncheckedVariance] =
new SubFlow(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.SubFlow[In, Out, Mat @uncheckedVariance] =
mapError {
case err if clazz.isInstance(err) => f(clazz.cast(err))
}
/**
* 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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
takeWithin(d.asScala)
/**
* Allows a faster upstream to progress independently of a slower subscriber by conflating elements into a summary
* until the subscriber is ready to accept them. For example a conflate step might average incoming numbers if the
* upstream publisher is faster.
*
* This version of conflate allows to derive a seed from the first element and change the aggregated type to be
* different than the input type. See [[Flow.conflate]] for a simpler version that does not change types.
*
* This element only rolls up elements if the upstream is faster, but if the downstream is faster it will not
* duplicate elements.
*
* Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute.
*
* '''Emits when''' downstream stops backpressuring and there is a conflated element available
*
* '''Backpressures when''' never
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
* see also [[SubFlow.conflate]] [[SubFlow.batch]] [[SubFlow.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]): SubFlow[In, S, Mat] =
new SubFlow(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 [[SubFlow.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 [[SubFlow.conflateWithSeed]] [[SubFlow.batch]] [[SubFlow.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]): SubFlow[In, Out, Mat] =
new SubFlow(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 [[SubFlow.conflate]], [[SubFlow.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]): SubFlow[In, S, Mat] =
new SubFlow(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 [[SubFlow.conflate]], [[SubFlow.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]): SubFlow[In, S, Mat] =
new SubFlow(delegate.batchWeighted(max, costFn.apply, seed.apply)(aggregate.apply))
/**
* Allows a faster downstream to progress independently of a slower upstream by extrapolating elements from an older
* element until new element comes from the upstream. For example an expand step might repeat the last element for
* the subscriber until it receives an update from upstream.
*
* This element will never "drop" upstream elements as all elements go through at least one extrapolation step.
* This means that if the upstream is actually faster than the upstream it will be backpressured by the downstream
* subscriber.
*
* Expand does not support [[akka.stream.Supervision#restart]] and [[akka.stream.Supervision#resume]].
* Exceptions from the `expander` function will complete the stream with failure.
*
* See also [[#extrapolate]] for a version that always preserves the original element and allows for an initial "startup" element.
*
* '''Emits when''' downstream stops backpressuring
*
* '''Backpressures when''' downstream backpressures or iterator runs empty
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*
* @param expander Takes the current extrapolation state to produce an output element and the next extrapolation
* state.
* @see [[#extrapolate]] for a version that always preserves the original element and allows for an initial "startup"
* element.
*/
def expand[U](expander: function.Function[Out, java.util.Iterator[U]]): SubFlow[In, U, Mat] =
new SubFlow(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]])
: SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
new SubFlow(delegate.buffer(size, overflowStrategy))
/**
* Takes up to `n` elements from the stream (less than `n` only 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): SubFlow[
In,
akka.japi.Pair[java.util.List[Out @uncheckedVariance], javadsl.Source[Out @uncheckedVariance, NotUsed]],
Mat] =
new SubFlow(delegate.prefixAndTail(n).map { case (taken, tail) => akka.japi.Pair(taken.asJava, tail.asJava) })
/**
* 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]]): SubFlow[In, T, Mat] =
new SubFlow(delegate.flatMapConcat(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]]): SubFlow[In, T, Mat] =
new SubFlow(delegate.flatMapMerge(breadth, o => f(o)))
/**
* Concatenate the given [[Source]] to this [[Flow]], meaning that once this
* Flow’s input is exhausted and all result elements have been generated,
* the Source’s elements will be produced.
*
* Note that the [[Source]] is materialized together with this Flow and just kept
* from producing elements by asserting back-pressure until its time comes.
*
* If this [[Flow]] gets upstream error - no elements from the given [[Source]] will be pulled.
*
* '''Emits when''' element is available from current stream or from the given [[Source]] when current is completed
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' given [[Source]] completes
*
* '''Cancels when''' downstream cancels
*/
def concat[M](that: Graph[SourceShape[Out], M]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.concat(that))
/**
* Prepend the given [[Source]] to this [[Flow]], meaning that before elements
* are generated from this Flow, the Source's elements will be produced until it
* is exhausted, at which point Flow elements will start being produced.
*
* Note that this Flow will be materialized together with the [[Source]] and just kept
* from producing elements by asserting back-pressure until its time comes.
*
* If the given [[Source]] gets upstream error - no elements from this [[Flow]] will be pulled.
*
* '''Emits when''' element is available from the given [[Source]] or from current stream when the [[Source]] is completed
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' this [[Flow]] completes
*
* '''Cancels when''' downstream cancels
*/
def prepend[M](that: Graph[SourceShape[Out], M]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.prepend(that))
/**
* 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.SubFlow[In, Out, Mat] =
new SubFlow(delegate.orElse(secondary))
/**
* 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], _]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.alsoTo(that))
/**
* 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]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.divertTo(that, when.test))
/**
* 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], _]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.wireTap(that))
/**
* Merge the given [[Source]] to this [[Flow]], taking elements as they arrive from input streams,
* picking randomly when several elements ready.
*
* '''Emits when''' one of the inputs has an element available
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' all upstreams complete
*
* '''Cancels when''' downstream cancels
*/
def merge(that: Graph[SourceShape[Out], _]): SubFlow[In, Out, Mat] =
new SubFlow(delegate.merge(that))
/**
* Interleave is a deterministic merge of the given [[Source]] with elements of this [[Flow]].
* It first emits `segmentSize` number of elements from this flow to downstream, then - same amount for `that`
* source, then repeat process.
*
* Example:
* {{{
* Source(List(1, 2, 3)).interleave(List(4, 5, 6, 7), 2) // 1, 2, 4, 5, 3, 6, 7
* }}}
*
* After one of upstreams is complete than all the rest elements will be emitted from the second one
*
* If it gets error from one of upstreams - 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): SubFlow[In, Out, Mat] =
new SubFlow(delegate.interleave(that, segmentSize))
/**
* 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.SubFlow[In, java.util.List[Out], Mat] =
new SubFlow(delegate.mergeLatest(that, eagerComplete).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.SubFlow[In, Out, Mat] =
new SubFlow(delegate.mergePreferred(that, preferred, eagerComplete))
/**
* 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.SubFlow[In, Out, Mat] =
new SubFlow(delegate.mergePrioritized(that, leftPriority, rightPriority, eagerComplete))
/**
* Merge the given [[Source]] to this [[Flow]], taking elements as they arrive from input streams,
* picking always the smallest of the available elements (waiting for one element from each side
* to be available). This means that possible contiguity of the input streams is not exploited to avoid
* waiting for elements, this merge will block when one of the inputs does not have more elements (and
* does not complete).
*
* '''Emits when''' all of the inputs have an element available
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' all upstreams complete
*
* '''Cancels when''' downstream cancels
*/
def mergeSorted[M](that: Graph[SourceShape[Out], M], comp: Comparator[Out]): javadsl.SubFlow[In, Out, Mat] =
new SubFlow(delegate.mergeSorted(that)(Ordering.comparatorToOrdering(comp)))
/**
* Combine the elements of current [[Flow]] and the given [[Source]] 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](source: Graph[SourceShape[T], _]): SubFlow[In, akka.japi.Pair[Out @uncheckedVariance, T], Mat] =
new SubFlow(delegate.zip(source).map { case (o, t) => akka.japi.Pair.create(o, t) })
/**
* 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): SubFlow[In, akka.japi.Pair[A, U], Mat] =
new SubFlow(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, picking always the latest element of each.
*
* '''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](source: Graph[SourceShape[T], _]): SubFlow[In, akka.japi.Pair[Out @uncheckedVariance, T], Mat] =
new SubFlow(delegate.zipLatest(source).map { case (o, t) => akka.japi.Pair.create(o, t) })
/**
* Put together the elements of current [[Flow]] and the given [[Source]]
* into a stream of combined elements using a combiner function.
*
* '''Emits when''' all of the inputs 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]): SubFlow[In, Out3, Mat] =
new SubFlow(delegate.zipWith[Out2, Out3](that)(combinerToScala(combine)))
/**
* Put together the elements of current [[Flow]] and the given [[Source]]
* into a stream of combined elements using a combiner function, picking always the latest element of each.
*
* '''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 zipLatestWith[Out2, Out3](
that: Graph[SourceShape[Out2], _],
combine: function.Function2[Out, Out2, Out3]): SubFlow[In, Out3, Mat] =
new SubFlow(delegate.zipLatestWith[Out2, Out3](that)(combinerToScala(combine)))
/**
* Combine the elements of current [[Flow]] into a stream of tuples consisting
* of all elements paired with their index. Indices start at 0.
*
* '''Emits when''' upstream emits an element and is paired with their index
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def zipWithIndex: SubFlow[In, akka.japi.Pair[Out @uncheckedVariance, java.lang.Long], Mat] =
new SubFlow(delegate.zipWithIndex.map { case (elem, index) => akka.japi.Pair[Out, java.lang.Long](elem, index) })
/**
* 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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
initialTimeout(timeout.asScala)
/**
* If the completion of the stream does not happen until the provided timeout, the stream is failed
* with a [[java.util.concurrent.TimeoutException]].
*
* '''Emits when''' upstream emits an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes or fails if timeout elapses before upstream completes
*
* '''Cancels when''' downstream cancels
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def completionTimeout(timeout: FiniteDuration): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
completionTimeout(timeout.asScala)
/**
* If the time between two processed elements exceeds the provided timeout, the stream is failed
* with a [[java.util.concurrent.TimeoutException]]. The timeout is checked periodically,
* so the resolution of the check is one period (equals to timeout value).
*
* '''Emits when''' upstream emits an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes or fails if timeout elapses between two emitted elements
*
* '''Cancels when''' downstream cancels
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def idleTimeout(timeout: FiniteDuration): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
idleTimeout(timeout.asScala)
/**
* If the time between the emission of an element and the following downstream demand exceeds the provided timeout,
* the stream is failed with a [[java.util.concurrent.TimeoutException]]. The timeout is checked periodically,
* so the resolution of the check is one period (equals to timeout value).
*
* '''Emits when''' upstream emits an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes or fails if timeout elapses between element emission and downstream demand.
*
* '''Cancels when''' downstream cancels
*/
@Deprecated
@deprecated("Use the overloaded one which accepts java.time.Duration instead.", since = "2.5.12")
def backpressureTimeout(timeout: FiniteDuration): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, 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]): SubFlow[In, Out, Mat] =
new SubFlow(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]): SubFlow[In, 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.SubFlow[In, Out, Mat] =
new SubFlow(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.SubFlow[In, Out, Mat] =
new SubFlow(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.SubFlow[In, Out, Mat] =
new SubFlow(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.SubFlow[In, Out, Mat] =
new SubFlow(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.SubFlow[In, Out, Mat] =
new SubFlow(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.SubFlow[In, Out, Mat] =
new SubFlow(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.SubFlow[In, Out, Mat] =
new SubFlow(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.SubFlow[In, Out, Mat] =
throttleEven(elements, per.asScala, mode)
/**
* This is a simplified version of throttle that spreads events evenly across the given time interval.
*
* Use this 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: function.Function[Out, Integer],
mode: ThrottleMode): javadsl.SubFlow[In, Out, Mat] =
new SubFlow(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: function.Function[Out, Integer],
mode: ThrottleMode): javadsl.SubFlow[In, Out, Mat] =
throttleEven(cost, per.asScala, costCalculation, mode)
/**
* Detaches upstream demand from downstream demand without detaching the
* stream rates; in other words acts like a buffer of size 1.
*
* '''Emits when''' upstream emits an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def detach: javadsl.SubFlow[In, Out, Mat] = new SubFlow(delegate.detach)
/**
* 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): SubFlow[In, Out, Mat] =
new SubFlow(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): SubFlow[In, Out, Mat] =
initialDelay(delay.asScala)
/**
* Change the attributes of this [[Source]] to the given ones and seal the list
* of attributes. This means that further calls will not be able to remove these
* attributes, but instead add new ones. Note that this
* operation has no effect on an empty Flow (because the attributes apply
* only to the contained processing operators).
*/
def withAttributes(attr: Attributes): SubFlow[In, Out, Mat] =
new SubFlow(delegate.withAttributes(attr))
/**
* Add the given attributes to this Source. Further calls to `withAttributes`
* will not remove these attributes. Note that this
* operation has no effect on an empty Flow (because the attributes apply
* only to the contained processing operators).
*/
def addAttributes(attr: Attributes): SubFlow[In, Out, Mat] =
new SubFlow(delegate.addAttributes(attr))
/**
* Add a ``name`` attribute to this Flow.
*/
def named(name: String): SubFlow[In, Out, Mat] =
new SubFlow(delegate.named(name))
/**
* Put an asynchronous boundary around this `SubFlow`
*/
def async: SubFlow[In, Out, Mat] =
new SubFlow(delegate.async)
/**
* Logs elements flowing through the stream as well as completion and erroring.
*
* By default element and completion signals are logged on debug level, and errors are logged on Error level.
* This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow:
*
* The `extract` function will be applied to each element before logging, so it is possible to log only those fields
* of a complex object flowing through this element.
*
* Uses the given [[LoggingAdapter]] for logging.
*
* 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): SubFlow[In, Out, Mat] =
new SubFlow(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]): SubFlow[In, Out, Mat] =
this.log(name, extract, null)
/**
* Logs elements flowing through the stream as well as completion and erroring.
*
* By default element and completion signals are logged on debug level, and errors are logged on Error level.
* This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow:
*
* Uses the given [[LoggingAdapter]] for logging.
*
* '''Emits when''' the mapping function returns an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def log(name: String, log: LoggingAdapter): SubFlow[In, Out, Mat] =
this.log(name, ConstantFun.javaIdentityFunction[Out], log)
/**
* Logs elements flowing through the stream as well as completion and erroring.
*
* By default element and completion signals are logged on debug level, and errors are logged on Error level.
* This can be adjusted according to your needs by providing a custom [[Attributes.LogLevels]] attribute on the given Flow.
*
* Uses an internally created [[LoggingAdapter]] which uses `akka.stream.Log` as it's source (use this class to configure slf4j loggers).
*
* '''Emits when''' the mapping function returns an element
*
* '''Backpressures when''' downstream backpressures
*
* '''Completes when''' upstream completes
*
* '''Cancels when''' downstream cancels
*/
def log(name: String): SubFlow[In, Out, Mat] =
this.log(name, ConstantFun.javaIdentityFunction[Out], null)
}
