Merge remote-tracking branch 'pr/19106' into release-2.3-dev

akka-docs-dev/rst/java/stream-customize.rst

@@ -9,6 +9,12 @@ is sometimes necessary to define new transformation stages either because some f
 stock operations, or for performance reasons. In this part we show how to build custom processing stages and graph
 junctions of various kinds.
 
+.. note::
+   A custom graph stage should not be the first tool you reach for, defining graphs using flows
+   and the graph DSL is in general easier and does to a larger extent protect you from mistakes that
+   might be easy to make with a custom :class:`GraphStage`
+
+
 .. _graphstage-java:
 
 Custom processing with GraphStage
@@ -79,7 +85,7 @@ in that state.
 |
 
 .. image:: ../images/outport_transitions.png
-:align: center
+   :align: center
 
 |
 
@@ -115,7 +121,7 @@ in that state.
 |
 
 .. image:: ../images/inport_transitions.png
-:align: center
+   :align: center
 
 |
 
@@ -125,22 +131,160 @@ Finally, there are two methods available for convenience to complete the stage a
 * ``failStage(exception)`` is equivalent to failing all output ports and cancelling all input ports.
 
 
+In some cases it is inconvenient and error prone to react on the regular state machine events with the
+signal based API described above. For those cases there is a API which allows for a more declarative sequencing
+of actions which will greatly simplify some use cases at the cost of some extra allocations. The difference
+between the two APIs could be described as that the first one is signal driven from the outside, while this API
+is more active and drives its surroundings.
+
+The operations of this part of the :class:``GraphStage`` API are:
+
+* ``emit(out, elem)`` and ``emitMultiple(out, Iterable(elem1, elem2))`` replaces the ``OutHandler`` with a handler that emits
+  one or more elements when there is demand, and then reinstalls the current handlers
+* ``read(in)(andThen)`` and ``readN(in, n)(andThen)`` replaces the ``InHandler`` with a handler that reads one or
+  more elements as they are pushed and allows the handler to react once the requested number of elements has been read.
+* ``abortEmitting()`` and ``abortReading()`` which will cancel an ongoing emit or read
+
+Note that since the above methods are implemented by temporarily replacing the handlers of the stage you should never
+call ``setHandler`` while they are running ``emit`` or ``read`` as that interferes with how they are implemented.
+The following methods are safe to call after invoking ``emit`` and ``read`` (and will lead to actually running the
+operation when those are done): ``complete(out)``, ``completeStage()``, ``emit``, ``emitMultiple``, ``abortEmitting()``
+and ``abortReading()``
+
+An example of how this API simplifies a stage can be found below in the second version of the :class:``Duplicator``.
+
+Custom linear processing stages using GraphStage
+------------------------------------------------
+
+Graph stages allows for custom linear processing stages through letting them
+have one input and one output and using :class:`FlowShape` as their shape.
+
+Such a stage can be illustrated as a box with two flows as it is
+seen in the illustration below. Demand flowing upstream leading to elements
+flowing downstream.
+
+|
+
+.. image:: ../images/graph_stage_conceptual.png
+   :align: center
+   :width: 500
+
+|
+
+
+To illustrate these concepts we create a small :class:`GraphStage` that implements the ``map`` transformation.
+
+|
+
+.. image:: ../images/graph_stage_map.png
+   :align: center
+   :width: 300
+
+|
+
+Map calls ``push(out)`` from the ``onPush()`` handler and it also calls ``pull()`` from the ``onPull`` handler resulting in the
+conceptual wiring above, and fully expressed in code below:
+
+.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/GraphStageDocTest.java#one-to-one
+
+Map is a typical example of a one-to-one transformation of a stream where
+demand is passed along upstream elements passed on downstream.
+
+To demonstrate a many-to-one stage we will implement
+filter. The conceptual wiring of ``Filter`` looks like this:
+
+|
+
+.. image:: ../images/graph_stage_filter.png
+   :align: center
+   :width: 300
+
+|
+
+
+As we see above, if the given predicate matches the current element we are propagating it downwards, otherwise
+we return the “ball” to our upstream so that we get the new element. This is achieved by modifying the map
+example by adding a conditional in the ``onPush`` handler and decide between a ``pull(in)`` or ``push(out)`` call
+(and of course not having a mapping ``f`` function).
+
+.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/GraphStageDocTest.java#many-to-one
+
+To complete the picture we define a one-to-many transformation as the next step. We chose a straightforward example stage
+that emits every upstream element twice downstream. The conceptual wiring of this stage looks like this:
+
+|
+
+.. image:: ../images/graph_stage_duplicate.png
+   :align: center
+   :width: 300
+
+|
+
+This is a stage that has state: an option with the last element it has seen indicating if it
+has duplicated this last element already or not. We must also make sure to emit the extra element
+if the upstream completes.
+
+.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/GraphStageDocTest.java#one-to-many
+
+In this case a pull from downstream might be consumed by the stage itself rather
+than passed along upstream as the stage might contain an element it wants to
+push. Note that we also need to handle the case where the upstream closes while
+the stage still has elements it wants to push downstream. This is done by
+overriding `onUpstreamFinish` in the `AbstractInHandler` and provide custom logic
+that should happen when the upstream has been finished.
+
+This example can be simplified by replacing the usage of a mutable state with calls to
+``emitMultiple`` which will replace the handlers, emit each of multiple elements and then
+reinstate the original handlers:
+
+.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/GraphStageDocTest.java#simpler-one-to-many
+
+Finally, to demonstrate all of the stages above, we put them together into a processing chain,
+which conceptually would correspond to the following structure:
+
+
+|
+
+.. image:: ../images/graph_stage_chain.png
+   :align: center
+   :width: 700
+
+|
+
+In code this is only a few lines, using the ``via`` use our custom stages in a stream:
+
+.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/GraphStageDocTest.java#graph-stage-chain
+
+If we attempt to draw the sequence of events, it shows that there is one "event token"
+in circulation in a potential chain of stages, just like our conceptual "railroad tracks" representation predicts.
+
+
+|
+
+.. image:: ../images/graph_stage_tracks_1.png
+   :align: center
+   :width: 700
+
+|
+
+
 Completion
 ----------
 
-**This section is a stub and will be extended in the next release**
+Completion handling usually (but not exclusively) comes into the picture when processing stages need to emit
+a few more elements after their upstream source has been completed. We have seen an example of this in our
+first :class:`Duplicator` implementation where the last element needs to be doubled even after the upstream neighbor
+stage has been completed. This can be done by overriding the ``onUpstreamFinish`` method in ``AbstractInHandler``.
 
 Stages by default automatically stop once all of their ports (input and output) have been closed externally or internally.
-It is possible to opt out from this behavior by overriding ``keepGoingAfterAllPortsClosed`` and returning true in
+It is possible to opt out from this behavior by invoking ``setKeepGoing(true)`` (which is not supported from the stage’s
-the :class:`GraphStageLogic` implementation. In this case the stage **must** be explicitly closed by calling ``completeStage()``
+constructor and usually done in ``preStart``). In this case the stage **must** be explicitly closed by calling ``completeStage()``
 or ``failStage(exception)``. This feature carries the risk of leaking streams and actors, therefore it should be used
 with care.
 
 Using timers
 ------------
 
-**This section is a stub and will be extended in the next release**
-
 It is possible to use timers in :class:`GraphStages` by using :class:`TimerGraphStageLogic` as the base class for
 the returned logic. Timers can be scheduled by calling one of ``scheduleOnce(key,delay)``, ``schedulePeriodically(key,period)`` or
 ``schedulePeriodicallyWithInitialDelay(key,delay,period)`` and passing an object as a key for that timer (can be any object, for example
@@ -151,11 +295,14 @@ fires. It is possible to cancel a timer using ``cancelTimer(key)`` and check the
 Timers can not be scheduled from the constructor of the logic, but it is possible to schedule them from the
 ``preStart()`` lifecycle hook.
 
+In this sample the stage toggles between open and closed, where open means no elements are passed through. The
+stage starts out as closed but as soon as an element is pushed downstream the gate becomes open for a duration
+of time during which it will consume and drop upstream messages:
+
+.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/GraphStageDocTest.java#timed
+
 Using asynchronous side-channels
 --------------------------------
-
-**This section is a stub and will be extended in the next release**
-
 In order to receive asynchronous events that are not arriving as stream elements (for example a completion of a future
 or a callback from a 3rd party API) one must acquire a :class:`AsyncCallback` by calling ``getAsyncCallback()`` from the
 stage logic. The method ``getAsyncCallback`` takes as a parameter a callback that will be called once the asynchronous
@@ -167,6 +314,13 @@ implementation.
 Sharing the AsyncCallback from the constructor risks race conditions, therefore it is recommended to use the
 ``preStart()`` lifecycle hook instead.
 
+
+This example shows an asynchronous side channel graph stage that starts dropping elements
+when a future completes:
+
+.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/GraphStageDocTest.java#async-side-channel
+
+
 Integration with actors
 -----------------------
 
@@ -188,8 +342,6 @@ or ``unwatch(ref)`` methods. The reference can be also watched by external actor
 Custom materialized values
 --------------------------
 
-**This section is a stub and will be extended in the next release**
-
 Custom stages can return materialized values instead of ``Unit`` by inheriting from :class:`GraphStageWithMaterializedValue`
 instead of the simpler :class:`GraphStage`. The difference is that in this case the method
 ``createLogicAndMaterializedValue(inheritedAttributes)`` needs to be overridden, overridden, and in addition to the
@@ -200,6 +352,10 @@ stage logic the materialized value must be provided
    the thread that got hold of the materialized value. It is the responsibility of the programmer to add the
    necessary (non-blocking) synchronization and visibility guarantees to this shared object.
 
+In this sample the materialized value is a future containing the first element to go through the stream:
+
+.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/GraphStageDocTest.java#materialized
+
 Using attributes to affect the behavior of a stage
 --------------------------------------------------
 
@@ -213,237 +369,52 @@ decision.
 See :ref:`composition-java` for an explanation on how attributes work.
 
 
-Custom linear processing stages
+Rate decoupled graph stages
-===============================
+---------------------------
 
-To extend the available transformations on a :class:`Flow` or :class:`Source` one can use the ``transform()`` method
+Sometimes it is desirable to *decouple* the rate of the upstream and downstream of a stage, synchronizing only
-which takes a factory function returning a :class:`Stage`. Stages come in different flavors swhich we will introduce in this
+when needed.
-page.
 
-.. _stream-using-push-pull-stage-java:
+This is achieved in the model by representing a :class:`GraphStage` as a *boundary* between two regions where the
+demand sent upstream is decoupled from the demand that arrives from downstream. One immediate consequence of this
+difference is that an ``onPush`` call does not always lead to calling ``push`` and an ``onPull``  call does not always
+lead to calling ``pull``.
 
-Using PushPullStage
+One of the important use-case for this is to build buffer-like entities, that allow independent progress
--------------------
-
-The most elementary transformation stage is the :class:`PushPullStage` which can express a large class of algorithms
-working on streams. A :class:`PushPullStage` can be illustrated as a box with two "input" and two "output ports" as it is
-seen in the illustration below.
-
-|
-
-.. image:: ../images/stage_conceptual.png
-   :align: center
-   :width: 600
-
-|
-
-The "input ports" are implemented as event handlers ``onPush(elem,ctx)`` and ``onPull(ctx)`` while "output ports"
-correspond to methods on the :class:`Context` object that is handed as a parameter to the event handlers. By calling
-exactly one "output port" method we wire up these four ports in various ways which we demonstrate shortly.
-
-.. warning::
-   There is one very important rule to remember when working with a ``Stage``. **Exactly one** method should be called
-   on the **currently passed** :class:`Context` **exactly once** and as the **last statement of the handler** where the return type
-   of the called method **matches the expected return type of the handler**. Any violation of this rule will
-   almost certainly result in unspecified behavior (in other words, it will break in spectacular ways). Exceptions
-   to this rule are the query methods ``isHolding()`` and ``isFinishing()``
-
-To illustrate these concepts we create a small :class:`PushPullStage` that implements the ``map`` transformation.
-
-|
-
-.. image:: ../images/stage_map.png
-   :align: center
-   :width: 300
-
-|
-
-Map calls ``ctx.push()`` from the ``onPush()`` handler and it also calls ``ctx.pull()`` form the ``onPull``
-handler resulting in the conceptual wiring above, and fully expressed in code below:
-
-.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/FlowStagesDocTest.java#one-to-one
-
-Map is a typical example of a one-to-one transformation of a stream. To demonstrate a many-to-one stage we will implement
-filter. The conceptual wiring of ``Filter`` looks like this:
-
-|
-
-.. image:: ../images/stage_filter.png
-   :align: center
-   :width: 300
-
-|
-
-As we see above, if the given predicate matches the current element we are propagating it downwards, otherwise
-we return the "ball" to our upstream so that we get the new element. This is achieved by modifying the map
-example by adding a conditional in the ``onPush`` handler and decide between a ``ctx.pull()`` or ``ctx.push()`` call
-(and of course not having a mapping ``f`` function).
-
-.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/FlowStagesDocTest.java#many-to-one
-
-To complete the picture we define a one-to-many transformation as the next step. We chose a straightforward example stage
-that emits every upstream element twice downstream. The conceptual wiring of this stage looks like this:
-
-|
-
-.. image:: ../images/stage_doubler.png
-   :align: center
-   :width: 300
-
-|
-
-This is a stage that has state: the last element it has seen, and a flag ``oneLeft`` that indicates if we
-have duplicated this last element already or not. Looking at the code below, the reader might notice that our ``onPull``
-method is more complex than it is demonstrated by the figure above. The reason for this is completion handling, which we
-will explain a little bit later. For now it is enough to look at the ``if(!ctx.isFinishing)`` block which
-corresponds to the logic we expect by looking at the conceptual picture.
-
-.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/FlowStagesDocTest.java#one-to-many
-
-Finally, to demonstrate all of the stages above, we put them together into a processing chain, which conceptually
-would correspond to the following structure:
-
-|
-
-.. image:: ../images/stage_chain.png
-   :align: center
-   :width: 650
-
-|
-
-In code this is only a few lines, using the ``transform`` method to inject our custom processing into a stream:
-
-.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/FlowStagesDocTest.java#stage-chain
-
-If we attempt to draw the sequence of events, it shows that there is one "event token"
-in circulation in a potential chain of stages, just like our conceptual "railroad tracks" representation predicts.
-
-|
-
-.. image:: ../images/stage_msc_general.png
-   :align: center
-
-|
-
-Completion handling
-^^^^^^^^^^^^^^^^^^^
-
-Completion handling usually (but not exclusively) comes into the picture when processing stages need to emit a few
-more elements after their upstream source has been completed. We have seen an example of this in our ``Duplicator`` class
-where the last element needs to be doubled even after the upstream neighbor stage has been completed. Since the
-``onUpstreamFinish()`` handler expects a :class:`TerminationDirective` as the return type we are only allowed to call
-``ctx.finish()``, ``ctx.fail()`` or ``ctx.absorbTermination()``. Since the first two of these available methods will
-immediately terminate, our only option is ``absorbTermination()``. It is also clear from the return type of
-``onUpstreamFinish`` that we cannot call ``ctx.push()`` but we need to emit elements somehow! The trick is that after
-calling ``absorbTermination()`` the ``onPull()`` handler will be called eventually, and at the same time
-``ctx.isFinishing`` will return true, indicating that ``ctx.pull()`` cannot be called anymore. Now we are free to
-emit additional elementss and call ``ctx.finish()`` or ``ctx.pushAndFinish()`` eventually to finish processing.
-
-The reason for this slightly complex termination sequence is that the underlying ``onComplete`` signal of
-Reactive Streams may arrive without any pending demand, i.e. without respecting backpressure. This means that
-our push/pull structure that was illustrated in the figure of our custom processing chain does not
-apply to termination. Our neat model that is analogous to a ball that bounces back-and-forth in a
-pipe (it bounces back on ``Filter``, ``Duplicator`` for example) cannot describe the termination signals. By calling
-``absorbTermination()`` the execution environment checks if the conceptual token was *above* the current stage at
-that time (which means that it will never come back, so the environment immediately calls ``onPull``) or it was
-*below* (which means that it will come back eventually, so the environment does not need to call anything yet).
-
-The first of the two scenarios is when a termination signal arrives after a stage passed the event to its downstream. As
-we can see in the following diagram, there is no need to do anything by ``absorbTermination()`` since the black arrows
-representing the movement of the "event token" is uninterrupted.
-
-|
-
-.. image:: ../images/stage_msc_absorb_1.png
-   :align: center
-
-|
-
-In the second scenario the "event token" is somewhere upstream when the termination signal arrives. In this case
-``absorbTermination`` needs to ensure that a new "event token" is generated replacing the old one that is forever gone
-(since the upstream finished). This is done by calling the ``onPull()`` event handler of the stage.
-
-|
-
-.. image:: ../images/stage_msc_absorb_2.png
-   :align: center
-
-|
-
-Observe, that in both scenarios ``onPull()`` kicks off the continuation of the processing logic, the only difference is
-whether it is the downstream or the ``absorbTermination()`` call that calls the event handler.
-
-.. warning::
-  It is not allowed to call ``absorbTermination()`` from ``onDownstreamFinish()``. If the method is called anyway,
-  it will be logged at ``ERROR`` level, but no further action will be taken as at that point there is no active
-  downstream to propagate the error to. Cancellation in the upstream direction will continue undisturbed.
-
-Using PushStage
----------------
-
-Many one-to-one and many-to-one transformations do not need to override the ``onPull()`` handler at all since all
-they do is just propagate the pull upwards. For such transformations it is better to extend PushStage directly. For
-example our ``Map`` and ``Filter`` would look like this:
-
-.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/FlowStagesDocTest.java#pushstage
-
-The reason to use ``PushStage`` is not just cosmetic: internal optimizations rely on the fact that the onPull method
-only calls ``ctx.pull()`` and allow the environment do process elements faster than without this knowledge. By
-extending ``PushStage`` the environment can be sure that ``onPull()`` was not overridden since it is ``final`` on
-``PushStage``.
-
-Using DetachedStage
--------------------
-
-The model described in previous sections, while conceptually simple, cannot describe all desired stages. The main
-limitation is the "single-ball" (single "event token") model which prevents independent progress of an upstream and
-downstream of a stage. Sometimes it is desirable to *detach* the progress (and therefore, rate) of the upstream and
-downstream of a stage, synchronizing only when needed.
-
-This is achieved in the model by representing a :class:`DetachedStage` as a *boundary* between two "single-ball" regions.
-One immediate consequence of this difference is that **it is not allowed to call** ``ctx.pull()`` **from** ``onPull()`` **and
-it is not allowed to call** ``ctx.push()`` **from** ``onPush()`` as such combinations would "steal" a token from one region
-(resulting in zero tokens left) and would inject an unexpected second token to the other region. This is enforced
-by the expected return types of these callback functions.
-
-One of the important use-cases for :class:`DetachedStage` is to build buffer-like entities, that allow independent progress
 of upstream and downstream stages when the buffer is not full or empty, and slowing down the appropriate side if the
-buffer becomes empty or full. The next diagram illustrates the event sequence for a buffer with capacity of two elements.
+buffer becomes empty or full.
+
+The next diagram illustrates the event sequence for a buffer with capacity of two elements in a setting where
+the downstream demand is slow to start and the buffer will fill up with upstream elements before any demand
+is seen from downstream.
 
 |
 
-.. image:: ../images/stage_msc_buffer.png
+.. image:: ../images/graph_stage_detached_tracks_1.png
    :align: center
+   :width: 500
 
 |
 
-The very first difference we can notice is that our ``Buffer`` stage is automatically pulling its upstream on
+Another scenario would be where the demand from downstream starts coming in before any element is pushed
-initialization. Remember that it is forbidden to call ``ctx.pull`` from ``onPull``, therefore it is the task of the
+into the buffer stage.
-framework to kick off the first "event token" in the upstream region, which will remain there until the upstream stages
-stop. The diagram distinguishes between the actions of the two regions by colors: *purple* arrows indicate the actions
-involving the upstream "event token", while *red* arrows show the downstream region actions. This demonstrates the clear
-separation of these regions, and the invariant that the number of tokens in the two regions are kept unchanged.
 
-For buffer it is necessary to detach the two regions, but it is also necessary to sometimes hold back the upstream
-or downstream. The new API calls that are available for :class:`DetachedStage` s are the various ``ctx.holdXXX()`` methods
-, ``ctx.pushAndPull()`` and variants, and ``ctx.isHoldingXXX()``.
-Calling ``ctx.holdXXX()`` from ``onPull()`` or ``onPush`` results in suspending the corresponding
-region from progress, and temporarily taking ownership of the "event token". This state can be queried by ``ctx.isHolding()``
-which will tell if the stage is currently holding a token or not. It is only allowed to suspend one of the regions, not
-both, since that would disable all possible future events, resulting in a dead-lock. Releasing the held token is only
-possible by calling ``ctx.pushAndPull()``. This is to ensure that both the held token is released, and the triggering region
-gets its token back (one inbound token + one held token = two released tokens).
 
-The following code example demonstrates the buffer class corresponding to the message sequence chart we discussed.
+|
 
-.. includecode:: ../../../akka-samples/akka-docs-java-lambda/src/test/java/docs/stream/FlowStagesDocTest.java#detached
+.. image:: ../images/graph_stage_detached_tracks_2.png
+   :align: center
+   :width: 500
 
-.. warning::
+|
-  If ``absorbTermination()`` is called on a :class:`DetachedStage` while it holds downstream (``isHoldingDownstream``
+
-  returns true) then ``onPull()`` will be called on the stage. This ensures that the stage does not end up in a
+
-  deadlocked case. Since at the point when the termination is absorbed there will be no way to get any callbacks because
+The first difference we can notice is that our ``Buffer`` stage is automatically pulling its upstream on
-  the downstream is held, so the framework invokes onPull() to avoid this situation. This is similar to the termination
+initialization. The buffer has demand for up to two elements without any downstream demand.
-  logic already shown for :class:`PushPullStage`.
+
+The following code example demonstrates a buffer class corresponding to the message sequence chart above.
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#detached
 
 Thread safety of custom processing stages
 =========================================

akka-docs-dev/rst/scala/code/docs/stream/GraphStageDocSpec.scala

@@ -3,15 +3,16 @@
  */
 package docs.stream
 
-import akka.stream.javadsl.Sink
+import akka.stream.scaladsl.{ Keep, Sink, Flow, Source }
-import akka.stream.scaladsl.Source
+import akka.stream.stage._
-import akka.stream.stage.{ OutHandler, GraphStage, GraphStageLogic }
 import akka.stream._
 
-import akka.stream.testkit.AkkaSpec
+import akka.stream.testkit.{ TestPublisher, TestSubscriber, AkkaSpec }
 
-import scala.concurrent.{ Await, Future }
+import scala.collection.mutable
+import scala.concurrent.{ Promise, Await, Future }
 import scala.concurrent.duration._
+import scala.collection.immutable.Iterable
 
 class GraphStageDocSpec extends AkkaSpec {
 
@@ -83,4 +84,425 @@ class GraphStageDocSpec extends AkkaSpec {
     Await.result(result2, 3.seconds) should ===(5050)
   }
 
+  //#one-to-one
+  class Map[A, B](f: A => B) extends GraphStage[FlowShape[A, B]] {
+
+    val in = Inlet[A]("Map.in")
+    val out = Outlet[B]("Map.out")
+
+    override val shape = FlowShape.of(in, out)
+
+    override def createLogic(attr: Attributes): GraphStageLogic =
+      new GraphStageLogic(shape) {
+        setHandler(in, new InHandler {
+          override def onPush(): Unit = {
+            push(out, f(grab(in)))
+          }
+        })
+        setHandler(out, new OutHandler {
+          override def onPull(): Unit = {
+            pull(in)
+          }
+        })
+      }
+  }
+  //#one-to-one
+
+  "Demonstrate a one to one element GraphStage" in {
+    // tests:
+    val stringLength = Flow.fromGraph(new Map[String, Int](_.length))
+
+    val result =
+      Source(Vector("one", "two", "three"))
+        .via(stringLength)
+        .runFold(Seq.empty[Int])((elem, acc) => elem :+ acc)
+
+    Await.result(result, 3.seconds) should ===(Seq(3, 3, 5))
+  }
+
+  //#many-to-one
+  class Filter[A](p: A => Boolean) extends GraphStage[FlowShape[A, A]] {
+
+    val in = Inlet[A]("Filter.in")
+    val out = Outlet[A]("Filter.out")
+
+    val shape = FlowShape.of(in, out)
+
+    override def createLogic(inheritedAttributes: Attributes): GraphStageLogic =
+      new GraphStageLogic(shape) {
+        setHandler(in, new InHandler {
+          override def onPush(): Unit = {
+            val elem = grab(in)
+            if (p(elem)) push(out, elem)
+            else pull(in)
+          }
+        })
+        setHandler(out, new OutHandler {
+          override def onPull(): Unit = {
+            pull(in)
+          }
+        })
+      }
+  }
+  //#many-to-one
+
+  "Demonstrate a many to one element GraphStage" in {
+
+    // tests:
+    val evenFilter = Flow.fromGraph(new Filter[Int](_ % 2 == 0))
+
+    val result =
+      Source(Vector(1, 2, 3, 4, 5, 6))
+        .via(evenFilter)
+        .runFold(Seq.empty[Int])((elem, acc) => elem :+ acc)
+
+    Await.result(result, 3.seconds) should ===(Seq(2, 4, 6))
+  }
+
+  //#one-to-many
+  class Duplicator[A] extends GraphStage[FlowShape[A, A]] {
+
+    val in = Inlet[A]("Duplicator.in")
+    val out = Outlet[A]("Duplicator.out")
+
+    val shape = FlowShape.of(in, out)
+
+    override def createLogic(inheritedAttributes: Attributes): GraphStageLogic =
+      new GraphStageLogic(shape) {
+        // Again: note that all mutable state
+        // MUST be inside the GraphStageLogic
+        var lastElem: Option[A] = None
+
+        setHandler(in, new InHandler {
+          override def onPush(): Unit = {
+            val elem = grab(in)
+            lastElem = Some(elem)
+            push(out, elem)
+          }
+
+          override def onUpstreamFinish(): Unit = {
+            if (lastElem.isDefined) emit(out, lastElem.get)
+            complete(out)
+          }
+
+        })
+        setHandler(out, new OutHandler {
+          override def onPull(): Unit = {
+            if (lastElem.isDefined) {
+              push(out, lastElem.get)
+              lastElem = None
+            } else {
+              pull(in)
+            }
+          }
+        })
+      }
+  }
+  //#one-to-many
+
+  "Demonstrate a one to many element GraphStage" in {
+    // tests:
+    val duplicator = Flow.fromGraph(new Duplicator[Int])
+
+    val result =
+      Source(Vector(1, 2, 3))
+        .via(duplicator)
+        .runFold(Seq.empty[Int])((elem, acc) => elem :+ acc)
+
+    Await.result(result, 3.seconds) should ===(Seq(1, 1, 2, 2, 3, 3))
+  }
+
+  "Demonstrate a simpler one to many stage" in {
+    //#simpler-one-to-many
+    class Duplicator[A] extends GraphStage[FlowShape[A, A]] {
+
+      val in = Inlet[A]("Duplicator.in")
+      val out = Outlet[A]("Duplicator.out")
+
+      val shape = FlowShape.of(in, out)
+
+      override def createLogic(inheritedAttributes: Attributes): GraphStageLogic =
+        new GraphStageLogic(shape) {
+
+          setHandler(in, new InHandler {
+            override def onPush(): Unit = {
+              val elem = grab(in)
+              // this will temporarily suspend this handler until the two elems
+              // are emitted and then reinstates it
+              emitMultiple(out, Iterable(elem, elem))
+            }
+          })
+          setHandler(out, new OutHandler {
+            override def onPull(): Unit = {
+              pull(in)
+            }
+          })
+        }
+    }
+    //#simpler-one-to-many
+
+    // tests:
+    val duplicator = Flow.fromGraph(new Duplicator[Int])
+
+    val result =
+      Source(Vector(1, 2, 3))
+        .via(duplicator)
+        .runFold(Seq.empty[Int])((elem, acc) => elem :+ acc)
+
+    Await.result(result, 3.seconds) should ===(Seq(1, 1, 2, 2, 3, 3))
+
+  }
+
+  "Demonstrate chaining of graph stages" in {
+    val sink = Sink.fold[List[Int], Int](List.empty[Int])((acc, n) => acc :+ n)
+
+    //#graph-stage-chain
+    val resultFuture = Source(1 to 5)
+      .via(new Filter(_ % 2 == 0))
+      .via(new Duplicator())
+      .via(new Map(_ / 2))
+      .runWith(sink)
+
+    //#graph-stage-chain
+
+    Await.result(resultFuture, 3.seconds) should ===(List(1, 1, 2, 2))
+  }
+
+  "Demonstrate an asynchronous side channel" in {
+    import system.dispatcher
+    //#async-side-channel
+    // will close upstream when the future completes
+    class KillSwitch[A](switch: Future[Unit]) extends GraphStage[FlowShape[A, A]] {
+
+      val in = Inlet[A]("KillSwitch.in")
+      val out = Outlet[A]("KillSwitch.out")
+
+      val shape = FlowShape.of(in, out)
+
+      override def createLogic(inheritedAttributes: Attributes): GraphStageLogic =
+        new GraphStageLogic(shape) {
+
+          override def preStart(): Unit = {
+            val callback = getAsyncCallback[Unit] { (_) =>
+              completeStage()
+            }
+            switch.foreach(callback.invoke)
+          }
+
+          setHandler(in, new InHandler {
+            override def onPush(): Unit = { push(out, grab(in)) }
+          })
+          setHandler(out, new OutHandler {
+            override def onPull(): Unit = { pull(in) }
+          })
+        }
+    }
+    //#async-side-channel
+
+    // tests:
+    val switch = Promise[Unit]()
+    val duplicator = Flow.fromGraph(new KillSwitch[Int](switch.future))
+
+    // TODO this is probably racey, is there a way to make sure it happens after?
+    val valueAfterKill = switch.future.flatMap(_ => Future(4))
+
+    val result =
+      Source(Vector(1, 2, 3)).concat(Source.fromFuture(valueAfterKill))
+        .via(duplicator)
+        .runFold(Seq.empty[Int])((elem, acc) => elem :+ acc)
+
+    switch.success(Unit)
+
+    Await.result(result, 3.seconds) should ===(Seq(1, 2, 3))
+  }
+
+  "Demonstrate a graph stage with a timer" in {
+
+    //#timed
+    // each time an event is pushed through it will trigger a period of silence
+    class TimedGate[A](silencePeriod: FiniteDuration) extends GraphStage[FlowShape[A, A]] {
+
+      val in = Inlet[A]("TimedGate.in")
+      val out = Outlet[A]("TimedGate.out")
+
+      val shape = FlowShape.of(in, out)
+
+      override def createLogic(inheritedAttributes: Attributes): GraphStageLogic =
+        new TimerGraphStageLogic(shape) {
+
+          var open = false
+
+          setHandler(in, new InHandler {
+            override def onPush(): Unit = {
+              val elem = grab(in)
+              if (open) pull(in)
+              else {
+                push(out, elem)
+                open = true
+                scheduleOnce(None, silencePeriod)
+              }
+            }
+          })
+          setHandler(out, new OutHandler {
+            override def onPull(): Unit = { pull(in) }
+          })
+
+          override protected def onTimer(timerKey: Any): Unit = {
+            open = false
+          }
+        }
+    }
+    //#timed
+
+    // tests:
+    val result =
+      Source(Vector(1, 2, 3))
+        .via(new TimedGate[Int](2.second))
+        .takeWithin(250.millis)
+        .runFold(Seq.empty[Int])((elem, acc) => elem :+ acc)
+
+    Await.result(result, 3.seconds) should ===(Seq(1))
+  }
+
+  "Demonstrate a custom materialized value" in {
+
+    //#materialized
+    class FirstValue[A] extends GraphStageWithMaterializedValue[FlowShape[A, A], Future[A]] {
+
+      val in = Inlet[A]("FirstValue.in")
+      val out = Outlet[A]("FirstValue.out")
+
+      val shape = FlowShape.of(in, out)
+
+      override def createLogicAndMaterializedValue(inheritedAttributes: Attributes): (GraphStageLogic, Future[A]) = {
+        val promise = Promise[A]()
+        val logic = new GraphStageLogic(shape) {
+
+          setHandler(in, new InHandler {
+            override def onPush(): Unit = {
+              val elem = grab(in)
+              promise.success(elem)
+              push(out, elem)
+
+              // replace handler with one just forwarding
+              setHandler(in, new InHandler {
+                override def onPush(): Unit = {
+                  push(out, grab(in))
+                }
+              })
+            }
+          })
+
+          setHandler(out, new OutHandler {
+            override def onPull(): Unit = {
+              pull(in)
+            }
+          })
+
+        }
+
+        (logic, promise.future)
+      }
+    }
+    //#materialized
+
+    // tests:
+    val flow = Source(Vector(1, 2, 3))
+      .viaMat(new FirstValue)(Keep.right)
+      .to(Sink.ignore)
+
+    val result: Future[Int] = flow.run()
+
+    Await.result(result, 3.seconds) should ===(1)
+
+  }
+
+  "Demonstrate a detached graph stage" in {
+
+    //#detached
+    class TwoBuffer[A] extends GraphStage[FlowShape[A, A]] {
+
+      val in = Inlet[A]("TwoBuffer.in")
+      val out = Outlet[A]("TwoBuffer.out")
+
+      val shape = FlowShape.of(in, out)
+
+      override def createLogic(inheritedAttributes: Attributes): GraphStageLogic =
+        new GraphStageLogic(shape) {
+
+          val buffer = mutable.Queue[A]()
+          def bufferFull = buffer.size == 2
+          var downstreamWaiting = false
+
+          override def preStart(): Unit = {
+            // a detached stage needs to start upstream demand
+            // itself as it is not triggered by downstream demand
+            pull(in)
+          }
+
+          setHandler(in, new InHandler {
+            override def onPush(): Unit = {
+              val elem = grab(in)
+              buffer.enqueue(elem)
+              if (downstreamWaiting) {
+                downstreamWaiting = false
+                val bufferedElem = buffer.dequeue()
+                push(out, bufferedElem)
+              }
+              if (!bufferFull) {
+                pull(in)
+              }
+            }
+
+            override def onUpstreamFinish(): Unit = {
+              if (buffer.nonEmpty) {
+                // emit the rest if possible
+                emitMultiple(out, buffer.toIterator)
+              }
+              completeStage()
+            }
+          })
+
+          setHandler(out, new OutHandler {
+            override def onPull(): Unit = {
+              if (buffer.isEmpty) {
+                downstreamWaiting = true
+              } else {
+                val elem = buffer.dequeue
+                push(out, elem)
+              }
+              if (!bufferFull && !hasBeenPulled(in)) {
+                pull(in)
+              }
+            }
+          })
+        }
+
+    }
+    //#detached
+
+    // tests:
+    val result1 = Source(Vector(1, 2, 3))
+      .via(new TwoBuffer)
+      .runFold(Vector.empty[Int])((acc, n) => acc :+ n)
+
+    Await.result(result1, 3.seconds) should ===(Vector(1, 2, 3))
+
+    val subscriber = TestSubscriber.manualProbe[Int]()
+    val publisher = TestPublisher.probe[Int]()
+    val flow2 =
+      Source.fromPublisher(publisher)
+        .via(new TwoBuffer)
+        .to(Sink.fromSubscriber(subscriber))
+
+    val result2 = flow2.run()
+
+    val sub = subscriber.expectSubscription()
+    // this happens even though the subscriber has not signalled any demand
+    publisher.sendNext(1)
+    publisher.sendNext(2)
+
+    sub.cancel()
+  }
+
 }

akka-docs-dev/rst/scala/stream-customize.rst

@@ -9,6 +9,12 @@ is sometimes necessary to define new transformation stages either because some f
 stock operations, or for performance reasons. In this part we show how to build custom processing stages and graph
 junctions of various kinds.
 
+.. note::
+   A custom graph stage should not be the first tool you reach for, defining graphs using flows
+   and the graph DSL is in general easier and does to a larger extent protect you from mistakes that
+   might be easy to make with a custom :class:`GraphStage`
+
+
 .. _graphstage-scala:
 
 Custom processing with GraphStage
@@ -129,22 +135,162 @@ Finally, there are two methods available for convenience to complete the stage a
 * ``failStage(exception)`` is equivalent to failing all output ports and cancelling all input ports.
 
 
+In some cases it is inconvenient and error prone to react on the regular state machine events with the
+signal based API described above. For those cases there is a API which allows for a more declarative sequencing
+of actions which will greatly simplify some use cases at the cost of some extra allocations. The difference
+between the two APIs could be described as that the first one is signal driven from the outside, while this API
+is more active and drives its surroundings.
+
+The operations of this part of the :class:``GraphStage`` API are:
+
+* ``emit(out, elem)`` and ``emitMultiple(out, Iterable(elem1, elem2))`` replaces the ``OutHandler`` with a handler that emits
+  one or more elements when there is demand, and then reinstalls the current handlers
+* ``read(in)(andThen)`` and ``readN(in, n)(andThen)`` replaces the ``InHandler`` with a handler that reads one or
+  more elements as they are pushed and allows the handler to react once the requested number of elements has been read.
+* ``abortEmitting()`` and ``abortReading()`` which will cancel an ongoing emit or read
+
+Note that since the above methods are implemented by temporarily replacing the handlers of the stage you should never
+call ``setHandler`` while they are running ``emit`` or ``read`` as that interferes with how they are implemented.
+The following methods are safe to call after invoking ``emit`` and ``read`` (and will lead to actually running the
+operation when those are done): ``complete(out)``, ``completeStage()``, ``emit``, ``emitMultiple``, ``abortEmitting()``
+and ``abortReading()``
+
+An example of how this API simplifies a stage can be found below in the second version of the :class:``Duplicator``.
+
+
+Custom linear processing stages using GraphStage
+------------------------------------------------
+
+Graph stages allows for custom linear processing stages through letting them
+have one input and one output and using :class:`FlowShape` as their shape.
+
+Such a stage can be illustrated as a box with two flows as it is
+seen in the illustration below. Demand flowing upstream leading to elements
+flowing downstream.
+
+|
+
+.. image:: ../images/graph_stage_conceptual.png
+   :align: center
+   :width: 500
+
+|
+
+
+To illustrate these concepts we create a small :class:`GraphStage` that implements the ``map`` transformation.
+
+|
+
+.. image:: ../images/graph_stage_map.png
+   :align: center
+   :width: 300
+
+|
+
+Map calls ``push(out)`` from the ``onPush()`` handler and it also calls ``pull()`` from the ``onPull`` handler resulting in the
+conceptual wiring above, and fully expressed in code below:
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#one-to-one
+
+Map is a typical example of a one-to-one transformation of a stream where
+demand is passed along upstream elements passed on downstream.
+
+To demonstrate a many-to-one stage we will implement
+filter. The conceptual wiring of ``Filter`` looks like this:
+
+|
+
+.. image:: ../images/graph_stage_filter.png
+   :align: center
+   :width: 300
+
+|
+
+
+As we see above, if the given predicate matches the current element we are propagating it downwards, otherwise
+we return the “ball” to our upstream so that we get the new element. This is achieved by modifying the map
+example by adding a conditional in the ``onPush`` handler and decide between a ``pull(in)`` or ``push(out)`` call
+(and of course not having a mapping ``f`` function).
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#many-to-one
+
+To complete the picture we define a one-to-many transformation as the next step. We chose a straightforward example stage
+that emits every upstream element twice downstream. The conceptual wiring of this stage looks like this:
+
+|
+
+.. image:: ../images/graph_stage_duplicate.png
+   :align: center
+   :width: 300
+
+|
+
+This is a stage that has state: an option with the last element it has seen indicating if it
+has duplicated this last element already or not. We must also make sure to emit the extra element
+if the upstream completes.
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#one-to-many
+
+In this case a pull from downstream might be consumed by the stage itself rather
+than passed along upstream as the stage might contain an element it wants to
+push. Note that we also need to handle the case where the upstream closes while
+the stage still has elements it wants to push downstream. This is done by
+overriding `onUpstreamFinish` in the `InHandler` and provide custom logic
+that should happen when the upstream has been finished.
+
+This example can be simplified by replacing the usage of a mutable state with calls to
+``emitMultiple`` which will replace the handlers, emit each of multiple elements and then
+reinstate the original handlers:
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#simpler-one-to-many
+
+
+Finally, to demonstrate all of the stages above, we put them together into a processing chain,
+which conceptually would correspond to the following structure:
+
+
+|
+
+.. image:: ../images/graph_stage_chain.png
+   :align: center
+   :width: 700
+
+|
+
+In code this is only a few lines, using the ``via`` use our custom stages in a stream:
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#graph-stage-chain
+
+If we attempt to draw the sequence of events, it shows that there is one "event token"
+in circulation in a potential chain of stages, just like our conceptual "railroad tracks" representation predicts.
+
+
+|
+
+.. image:: ../images/graph_stage_tracks_1.png
+   :align: center
+   :width: 700
+
+|
+
 Completion
 ----------
 
-**This section is a stub and will be extended in the next release**
+Completion handling usually (but not exclusively) comes into the picture when processing stages need to emit
+a few more elements after their upstream source has been completed. We have seen an example of this in our
+first :class:`Duplicator` implementation where the last element needs to be doubled even after the upstream neighbor
+stage has been completed. This can be done by overriding the ``onUpstreamFinish`` method in ``InHandler``.
 
 Stages by default automatically stop once all of their ports (input and output) have been closed externally or internally.
-It is possible to opt out from this behavior by overriding ``keepGoingAfterAllPortsClosed`` and returning true in
+It is possible to opt out from this behavior by invoking ``setKeepGoing(true)`` (which is not supported from the stage’s
-the :class:`GraphStageLogic` implementation. In this case the stage **must** be explicitly closed by calling ``completeStage()``
+constructor and usually done in ``preStart``). In this case the stage **must** be explicitly closed by calling ``completeStage()``
 or ``failStage(exception)``. This feature carries the risk of leaking streams and actors, therefore it should be used
 with care.
 
+
 Using timers
 ------------
 
-**This section is a stub and will be extended in the next release**
-
 It is possible to use timers in :class:`GraphStages` by using :class:`TimerGraphStageLogic` as the base class for
 the returned logic. Timers can be scheduled by calling one of ``scheduleOnce(key,delay)``, ``schedulePeriodically(key,period)`` or
 ``schedulePeriodicallyWithInitialDelay(key,delay,period)`` and passing an object as a key for that timer (can be any object, for example
@@ -155,11 +301,15 @@ fires. It is possible to cancel a timer using ``cancelTimer(key)`` and check the
 Timers can not be scheduled from the constructor of the logic, but it is possible to schedule them from the
 ``preStart()`` lifecycle hook.
 
+In this sample the stage toggles between open and closed, where open means no elements are passed through. The
+stage starts out as closed but as soon as an element is pushed downstream the gate becomes open for a duration
+of time during which it will consume and drop upstream messages:
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#timed
+
+
 Using asynchronous side-channels
 --------------------------------
-
-**This section is a stub and will be extended in the next release**
-
 In order to receive asynchronous events that are not arriving as stream elements (for example a completion of a future
 or a callback from a 3rd party API) one must acquire a :class:`AsyncCallback` by calling ``getAsyncCallback()`` from the
 stage logic. The method ``getAsyncCallback`` takes as a parameter a callback that will be called once the asynchronous
@@ -171,6 +321,12 @@ implementation.
 Sharing the AsyncCallback from the constructor risks race conditions, therefore it is recommended to use the
 ``preStart()`` lifecycle hook instead.
 
+
+This example shows an asynchronous side channel graph stage that starts dropping elements
+when a future completes:
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#async-side-channel
+
 Integration with actors
 -----------------------
 
@@ -189,11 +345,10 @@ or ``unwatch(ref)`` methods. The reference can be also watched by external actor
  - they cannot be accessed from the constructor of the :class:`GraphStageLogic`, but they can be accessed from the
    ``preStart()`` method.
 
+
 Custom materialized values
 --------------------------
 
-**This section is a stub and will be extended in the next release**
-
 Custom stages can return materialized values instead of ``Unit`` by inheriting from :class:`GraphStageWithMaterializedValue`
 instead of the simpler :class:`GraphStage`. The difference is that in this case the method
 ``createLogicAndMaterializedValue(inheritedAttributes)`` needs to be overridden, overridden, and in addition to the
@@ -204,6 +359,11 @@ stage logic the materialized value must be provided
    the thread that got hold of the materialized value. It is the responsibility of the programmer to add the
    necessary (non-blocking) synchronization and visibility guarantees to this shared object.
 
+In this sample the materialized value is a future containing the first element to go through the stream:
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#materialized
+
+
 Using attributes to affect the behavior of a stage
 --------------------------------------------------
 
@@ -217,239 +377,54 @@ decision.
 See :ref:`composition-scala` for an explanation on how attributes work.
 
 
-Custom linear processing stages with PushPullStage
-==================================================
-
-To extend the available transformations on a :class:`Flow` or :class:`Source` one can use the ``transform()`` method
-which takes a factory function returning a :class:`Stage`. Stages come in different flavors swhich we will introduce in this
-page.
-
-.. _stream-using-push-pull-stage-scala:
-
-Using PushPullStage
--------------------
-
-The most elementary transformation stage is the :class:`PushPullStage` which can express a large class of algorithms
-working on streams. A :class:`PushPullStage` can be illustrated as a box with two "input" and two "output ports" as it is
-seen in the illustration below.
-
-|
-
-.. image:: ../images/stage_conceptual.png
-   :align: center
-   :width: 600
-
-|
-
-The "input ports" are implemented as event handlers ``onPush(elem,ctx)`` and ``onPull(ctx)`` while "output ports"
-correspond to methods on the :class:`Context` object that is handed as a parameter to the event handlers. By calling
-exactly one "output port" method we wire up these four ports in various ways which we demonstrate shortly.
-
-.. warning::
-   There is one very important rule to remember when working with a ``Stage``. **Exactly one** method should be called
-   on the **currently passed** :class:`Context` **exactly once** and as the **last statement of the handler** where the return type
-   of the called method **matches the expected return type of the handler**. Any violation of this rule will
-   almost certainly result in unspecified behavior (in other words, it will break in spectacular ways). Exceptions
-   to this rule are the query methods ``isHolding()`` and ``isFinishing()``
-
-To illustrate these concepts we create a small :class:`PushPullStage` that implements the ``map`` transformation.
-
-|
-
-.. image:: ../images/stage_map.png
-   :align: center
-   :width: 300
-
-|
-
-Map calls ``ctx.push()`` from the ``onPush()`` handler and it also calls ``ctx.pull()`` form the ``onPull``
-handler resulting in the conceptual wiring above, and fully expressed in code below:
-
-.. includecode:: code/docs/stream/FlowStagesSpec.scala#one-to-one
-
-Map is a typical example of a one-to-one transformation of a stream. To demonstrate a many-to-one stage we will implement
-filter. The conceptual wiring of ``Filter`` looks like this:
-
-|
-
-.. image:: ../images/stage_filter.png
-   :align: center
-   :width: 300
-
-|
-
-As we see above, if the given predicate matches the current element we are propagating it downwards, otherwise
-we return the "ball" to our upstream so that we get the new element. This is achieved by modifying the map
-example by adding a conditional in the ``onPush`` handler and decide between a ``ctx.pull()`` or ``ctx.push()`` call
-(and of course not having a mapping ``f`` function).
-
-.. includecode:: code/docs/stream/FlowStagesSpec.scala#many-to-one
-
-To complete the picture we define a one-to-many transformation as the next step. We chose a straightforward example stage
-that emits every upstream element twice downstream. The conceptual wiring of this stage looks like this:
-
-|
-
-.. image:: ../images/stage_doubler.png
-   :align: center
-   :width: 300
-
-|
-
-This is a stage that has state: the last element it has seen, and a flag ``oneLeft`` that indicates if we
-have duplicated this last element already or not. Looking at the code below, the reader might notice that our ``onPull``
-method is more complex than it is demonstrated by the figure above. The reason for this is completion handling, which we
-will explain a little bit later. For now it is enough to look at the ``if(!ctx.isFinishing)`` block which
-corresponds to the logic we expect by looking at the conceptual picture.
-
-.. includecode:: code/docs/stream/FlowStagesSpec.scala#one-to-many
-
-Finally, to demonstrate all of the stages above, we put them together into a processing chain, which conceptually
-would correspond to the following structure:
-
-|
-
-.. image:: ../images/stage_chain.png
-   :align: center
-   :width: 650
-
-|
-
-In code this is only a few lines, using the ``transform`` method to inject our custom processing into a stream:
-
-.. includecode:: code/docs/stream/FlowStagesSpec.scala#stage-chain
-
-If we attempt to draw the sequence of events, it shows that there is one "event token"
-in circulation in a potential chain of stages, just like our conceptual "railroad tracks" representation predicts.
-
-|
-
-.. image:: ../images/stage_msc_general.png
-   :align: center
-
-|
 
 
-Completion handling
+Rate decoupled graph stages
-^^^^^^^^^^^^^^^^^^^
+---------------------------
 
-Completion handling usually (but not exclusively) comes into the picture when processing stages need to emit a few
+Sometimes it is desirable to *decouple* the rate of the upstream and downstream of a stage, synchronizing only
-more elements after their upstream source has been completed. We have seen an example of this in our ``Duplicator`` class
+when needed.
-where the last element needs to be doubled even after the upstream neighbor stage has been completed. Since the
-``onUpstreamFinish()`` handler expects a :class:`TerminationDirective` as the return type we are only allowed to call
-``ctx.finish()``, ``ctx.fail()`` or ``ctx.absorbTermination()``. Since the first two of these available methods will
-immediately terminate, our only option is ``absorbTermination()``. It is also clear from the return type of
-``onUpstreamFinish`` that we cannot call ``ctx.push()`` but we need to emit elements somehow! The trick is that after
-calling ``absorbTermination()`` the ``onPull()`` handler will be called eventually, and at the same time
-``ctx.isFinishing`` will return true, indicating that ``ctx.pull()`` cannot be called anymore. Now we are free to
-emit additional elementss and call ``ctx.finish()`` or ``ctx.pushAndFinish()`` eventually to finish processing.
 
-The reason for this slightly complex termination sequence is that the underlying ``onComplete`` signal of
+This is achieved in the model by representing a :class:`GraphStage` as a *boundary* between two regions where the
-Reactive Streams may arrive without any pending demand, i.e. without respecting backpressure. This means that
+demand sent upstream is decoupled from the demand that arrives from downstream. One immediate consequence of this
-our push/pull structure that was illustrated in the figure of our custom processing chain does not
+difference is that an ``onPush`` call does not always lead to calling ``push`` and an ``onPull``  call does not always
-apply to termination. Our neat model that is analogous to a ball that bounces back-and-forth in a
+lead to calling ``pull``.
-pipe (it bounces back on ``Filter``, ``Duplicator`` for example) cannot describe the termination signals. By calling
-``absorbTermination()`` the execution environment checks if the conceptual token was *above* the current stage at
-that time (which means that it will never come back, so the environment immediately calls ``onPull``) or it was
-*below* (which means that it will come back eventually, so the environment does not need to call anything yet).
 
-The first of the two scenarios is when a termination signal arrives after a stage passed the event to its downstream. As
+One of the important use-case for this is to build buffer-like entities, that allow independent progress
-we can see in the following diagram, there is no need to do anything by ``absorbTermination()`` since the black arrows
-representing the movement of the "event token" is uninterrupted.
-
-|
-
-.. image:: ../images/stage_msc_absorb_1.png
-   :align: center
-
-|
-
-In the second scenario the "event token" is somewhere upstream when the termination signal arrives. In this case
-``absorbTermination`` needs to ensure that a new "event token" is generated replacing the old one that is forever gone
-(since the upstream finished). This is done by calling the ``onPull()`` event handler of the stage.
-
-|
-
-.. image:: ../images/stage_msc_absorb_2.png
-   :align: center
-
-|
-
-Observe, that in both scenarios ``onPull()`` kicks off the continuation of the processing logic, the only difference is
-whether it is the downstream or the ``absorbTermination()`` call that calls the event handler.
-
-.. warning::
-  It is not allowed to call ``absorbTermination()`` from ``onDownstreamFinish()``. If the method is called anyway,
-  it will be logged at ``ERROR`` level, but no further action will be taken as at that point there is no active
-  downstream to propagate the error to. Cancellation in the upstream direction will continue undisturbed.
-
-Using PushStage
----------------
-
-Many one-to-one and many-to-one transformations do not need to override the ``onPull()`` handler at all since all
-they do is just propagate the pull upwards. For such transformations it is better to extend PushStage directly. For
-example our ``Map`` and ``Filter`` would look like this:
-
-.. includecode:: code/docs/stream/FlowStagesSpec.scala#pushstage
-
-The reason to use ``PushStage`` is not just cosmetic: internal optimizations rely on the fact that the onPull method
-only calls ``ctx.pull()`` and allow the environment do process elements faster than without this knowledge. By
-extending ``PushStage`` the environment can be sure that ``onPull()`` was not overridden since it is ``final`` on
-``PushStage``.
-
-
-Using DetachedStage
--------------------
-
-The model described in previous sections, while conceptually simple, cannot describe all desired stages. The main
-limitation is the "single-ball" (single "event token") model which prevents independent progress of an upstream and
-downstream of a stage. Sometimes it is desirable to *detach* the progress (and therefore, rate) of the upstream and
-downstream of a stage, synchronizing only when needed.
-
-This is achieved in the model by representing a :class:`DetachedStage` as a *boundary* between two "single-ball" regions.
-One immediate consequence of this difference is that **it is not allowed to call** ``ctx.pull()`` **from** ``onPull()`` **and
-it is not allowed to call** ``ctx.push()`` **from** ``onPush()`` as such combinations would "steal" a token from one region
-(resulting in zero tokens left) and would inject an unexpected second token to the other region. This is enforced
-by the expected return types of these callback functions.
-
-One of the important use-cases for :class:`DetachedStage` is to build buffer-like entities, that allow independent progress
 of upstream and downstream stages when the buffer is not full or empty, and slowing down the appropriate side if the
-buffer becomes empty or full. The next diagram illustrates the event sequence for a buffer with capacity of two elements.
+buffer becomes empty or full.
+
+The next diagram illustrates the event sequence for a buffer with capacity of two elements in a setting where
+the downstream demand is slow to start and the buffer will fill up with upstream elements before any demand
+is seen from downstream.
 
 |
 
-.. image:: ../images/stage_msc_buffer.png
+.. image:: ../images/graph_stage_detached_tracks_1.png
    :align: center
+   :width: 500
 
 |
 
-The very first difference we can notice is that our ``Buffer`` stage is automatically pulling its upstream on
+Another scenario would be where the demand from downstream starts coming in before any element is pushed
-initialization. Remember that it is forbidden to call ``ctx.pull`` from ``onPull``, therefore it is the task of the
+into the buffer stage.
-framework to kick off the first "event token" in the upstream region, which will remain there until the upstream stages
-stop. The diagram distinguishes between the actions of the two regions by colors: *purple* arrows indicate the actions
-involving the upstream "event token", while *red* arrows show the downstream region actions. This demonstrates the clear
-separation of these regions, and the invariant that the number of tokens in the two regions are kept unchanged.
 
-For buffer it is necessary to detach the two regions, but it is also necessary to sometimes hold back the upstream
-or downstream. The new API calls that are available for :class:`DetachedStage` s are the various ``ctx.holdXXX()`` methods
-, ``ctx.pushAndPull()`` and variants, and ``ctx.isHoldingXXX()``.
-Calling ``ctx.holdXXX()`` from ``onPull()`` or ``onPush`` results in suspending the corresponding
-region from progress, and temporarily taking ownership of the "event token". This state can be queried by ``ctx.isHolding()``
-which will tell if the stage is currently holding a token or not. It is only allowed to suspend one of the regions, not
-both, since that would disable all possible future events, resulting in a dead-lock. Releasing the held token is only
-possible by calling ``ctx.pushAndPull()``. This is to ensure that both the held token is released, and the triggering region
-gets its token back (one inbound token + one held token = two released tokens).
 
-The following code example demonstrates the buffer class corresponding to the message sequence chart we discussed.
+|
 
-.. includecode:: code/docs/stream/FlowStagesSpec.scala#detached
+.. image:: ../images/graph_stage_detached_tracks_2.png
+   :align: center
+   :width: 500
 
-.. warning::
+|
-  If ``absorbTermination()`` is called on a :class:`DetachedStage` while it holds downstream (``isHoldingDownstream``
+
-  returns true) then ``onPull()`` will be called on the stage. This ensures that the stage does not end up in a
+
-  deadlocked case. Since at the point when the termination is absorbed there will be no way to get any callbacks because
+The first difference we can notice is that our ``Buffer`` stage is automatically pulling its upstream on
-  the downstream is held, so the framework invokes onPull() to avoid this situation. This is similar to the termination
+initialization. The buffer has demand for up to two elements without any downstream demand.
-  logic already shown for :class:`PushPullStage`.
+
+The following code example demonstrates a buffer class corresponding to the message sequence chart above.
+
+.. includecode:: code/docs/stream/GraphStageDocSpec.scala#detached
 
 
 Thread safety of custom processing stages

akka-stream/src/main/scala/akka/stream/stage/GraphStage.scala

@@ -3,16 +3,15 @@
  */
 package akka.stream.stage
 
-import java.util
+import java.util.concurrent.atomic.{ AtomicReference }
-import java.util.concurrent.atomic.{ AtomicReferenceFieldUpdater, AtomicReference }
 
 import akka.actor._
 import akka.dispatch.sysmsg.{ DeathWatchNotification, SystemMessage, Unwatch, Watch }
 import akka.event.LoggingAdapter
+import akka.japi.function.{ Effect, Procedure }
 import akka.stream._
 import akka.stream.impl.StreamLayout.Module
-import akka.stream.impl.fusing.GraphInterpreter.GraphAssembly
+import akka.stream.impl.fusing.{ GraphInterpreter, GraphStageModule }
-import akka.stream.impl.fusing.{ GraphInterpreter, GraphModule, GraphStageModule }
 import akka.stream.impl.{ ReactiveStreamsCompliance, SeqActorName }
 
 import scala.annotation.tailrec
@@ -612,6 +611,17 @@ abstract class GraphStageLogic private[stream] (val inCount: Int, val outCount:
       }
     }
 
+  /**
+   * Java API: Read a number of elements from the given inlet and continue with the given function,
+   * suspending execution if necessary. This action replaces the [[InHandler]]
+   * for the given inlet if suspension is needed and reinstalls the current
+   * handler upon receiving the last `onPush()` signal (before invoking the `andThen` function).
+   */
+  final protected def readN[T](in: Inlet[T], n: Int, andThen: Procedure[java.util.List[T]], onClose: Procedure[java.util.List[T]]): Unit = {
+    import collection.JavaConverters._
+    readN(in, n)(seq ⇒ andThen(seq.asJava), seq ⇒ onClose(seq.asJava))
+  }
+
   /**
    * Read an element from the given inlet and continue with the given function,
    * suspending execution if necessary. This action replaces the [[InHandler]]
@@ -631,6 +641,16 @@ abstract class GraphStageLogic private[stream] (val inCount: Int, val outCount:
     }
   }
 
+  /**
+   * Java API: Read an element from the given inlet and continue with the given function,
+   * suspending execution if necessary. This action replaces the [[InHandler]]
+   * for the given inlet if suspension is needed and reinstalls the current
+   * handler upon receiving the `onPush()` signal (before invoking the `andThen` function).
+   */
+  final protected def read[T](in: Inlet[T], andThen: Procedure[T], onClose: Effect): Unit = {
+    read(in)(andThen.apply, onClose.apply)
+  }
+
   /**
    * Abort outstanding (suspended) reading for the given inlet, if there is any.
    * This will reinstall the replaced handler that was in effect before the `read`
@@ -690,6 +710,32 @@ abstract class GraphStageLogic private[stream] (val inCount: Int, val outCount:
    */
   final protected def emitMultiple[T](out: Outlet[T], elems: immutable.Iterable[T]): Unit = emitMultiple(out, elems, DoNothing)
 
+  /**
+   * Java API
+   *
+   * Emit a sequence of elements through the given outlet, suspending execution if necessary.
+   * This action replaces the [[AbstractOutHandler]] for the given outlet if suspension
+   * is needed and reinstalls the current handler upon receiving an `onPull()`
+   * signal.
+   */
+  final protected def emitMultiple[T](out: Outlet[T], elems: java.util.Iterator[T]): Unit = {
+    import collection.JavaConverters._
+    emitMultiple(out, elems.asScala, DoNothing)
+  }
+
+  /**
+   * Java API
+   *
+   * Emit a sequence of elements through the given outlet, suspending execution if necessary.
+   * This action replaces the [[AbstractOutHandler]] for the given outlet if suspension
+   * is needed and reinstalls the current handler upon receiving an `onPull()`
+   * signal.
+   */
+  final protected def emitMultiple[T](out: Outlet[T], elems: java.util.Iterator[T], andThen: Effect): Unit = {
+    import collection.JavaConverters._
+    emitMultiple(out, elems.asScala, andThen.apply _)
+  }
+
   /**
    * Emit a sequence of elements through the given outlet and continue with the given thunk
    * afterwards, suspending execution if necessary.
@@ -740,6 +786,10 @@ abstract class GraphStageLogic private[stream] (val inCount: Int, val outCount:
    */
   final protected def emit[T](out: Outlet[T], elem: T): Unit = emit(out, elem, DoNothing)
 
+  final protected def emit[T](out: Outlet[T], elem: T, andThen: Effect): Unit = {
+    emit(out, elem, andThen.apply _)
+  }
+
   /**
    * Abort outstanding (suspended) emissions for the given outlet, if there are any.
    * This will reinstall the replaced handler that was in effect before the `emit`
@@ -875,6 +925,18 @@ abstract class GraphStageLogic private[stream] (val inCount: Int, val outCount:
     }
   }
 
+  /**
+   * Java API: Obtain a callback object that can be used asynchronously to re-enter the
+   * current [[GraphStage]] with an asynchronous notification. The [[invoke()]] method of the returned
+   * [[AsyncCallback]] is safe to be called from other threads and it will in the background thread-safely
+   * delegate to the passed callback function. I.e. [[invoke()]] will be called by the external world and
+   * the passed handler will be invoked eventually in a thread-safe way by the execution environment.
+   *
+   * This object can be cached and reused within the same [[GraphStageLogic]].
+   */
+  final protected def createAsyncCallback[T](handler: Procedure[T]): AsyncCallback[T] =
+    getAsyncCallback(handler.apply)
+
   private var _stageActorRef: StageActorRef = _
   final def stageActorRef: ActorRef = _stageActorRef match {
     case null ⇒ throw StageActorRefNotInitializedException()
@@ -982,6 +1044,7 @@ abstract class TimerGraphStageLogic(_shape: Shape) extends GraphStageLogic(_shap
 
   /**
    * Will be called when the scheduled timer is triggered.
+   *
    * @param timerKey key of the scheduled timer
    */
   protected def onTimer(timerKey: Any): Unit = ()
@@ -1029,6 +1092,7 @@ abstract class TimerGraphStageLogic(_shape: Shape) extends GraphStageLogic(_shap
 
   /**
    * Cancel timer, ensuring that the [[#onTimer]] is not subsequently called.
+   *
    * @param timerKey key of the timer to cancel
    */
   final protected def cancelTimer(timerKey: Any): Unit =