In Akka Streams computation graphs are not expressed using a fluent DSL like linear computations are, instead they are written in a more graph-resembling DSL which aims to make translating graph drawings (e.g. from notes taken from design discussions, or illustrations in protocol specifications) to and from code simpler. In this section we'll dive into the multiple ways of constructing and re-using graphs, as well as explain common pitfalls and how to avoid them.
Graphs are needed whenever you want to perform any kind of fan-in ("multiple inputs") or fan-out ("multiple outputs") operations.
Considering linear Flows to be like roads, we can picture graph operations as junctions: multiple flows being connected at a single point.
Some graph operations which are common enough and fit the linear style of Flows, such as concat
(which concatenates two
streams, such that the second one is consumed after the first one has completed), may have shorthand methods defined on
Flow
or Source
themselves, however you should keep in mind that those are also implemented as graph junctions.
Graphs are built from simple Flows which serve as the linear connections within the graphs as well as junctions which serve as fan-in and fan-out points for Flows. Thanks to the junctions having meaningful types based on their behaviour and making them explicit elements these elements should be rather straightforward to use.
Akka Streams currently provide these junctions (for a detailed list see @refstages overview):
-
Fan-out
- @scala[
Broadcast[T]
]@java[Broadcast<T>
] – (1 input, N outputs) given an input element emits to each output - @scala[
Balance[T]
]@java[Balance<T>
] – (1 input, N outputs) given an input element emits to one of its output ports - @scala[
UnzipWith[In,A,B,...]
]@java[UnzipWith<In,A,B,...>
] – (1 input, N outputs) takes a function of 1 input that given a value for each input emits N output elements (where N <= 20) - @scala[
UnZip[A,B]
]@java[UnZip<A,B>
] – (1 input, 2 outputs) splits a stream of @scala[(A,B)
]@java[Pair<A,B>
] tuples into two streams, one of typeA
and one of typeB
- @scala[
-
Fan-in
- @scala[
Merge[In]
]@java[Merge<In>
] – (N inputs , 1 output) picks randomly from inputs pushing them one by one to its output - @scala[
MergePreferred[In]
]@java[MergePreferred<In>
] – likeMerge
but if elements are available onpreferred
port, it picks from it, otherwise randomly fromothers
- @scala[
MergePrioritized[In]
]@java[MergePrioritized<In>
] – likeMerge
but if elements are available on all input ports, it picks from them randomly based on theirpriority
- @scala[
ZipWith[A,B,...,Out]
]@java[ZipWith<A,B,...,Out>
] – (N inputs, 1 output) which takes a function of N inputs that given a value for each input emits 1 output element - @scala[
Zip[A,B]
]@java[Zip<A,B>
] – (2 inputs, 1 output) is aZipWith
specialised to zipping input streams ofA
andB
into a @scala[(A,B)
]@java[Pair(A,B)
] tuple stream - @scala[
Concat[A]
]@java[Concat<A>
] – (2 inputs, 1 output) concatenates two streams (first consume one, then the second one)
- @scala[
One of the goals of the GraphDSL DSL is to look similar to how one would draw a graph on a whiteboard, so that it is simple to translate a design from whiteboard to code and be able to relate those two. Let's illustrate this by translating the below hand drawn graph into Akka Streams:
Such graph is simple to translate to the Graph DSL since each linear element corresponds to a Flow
,
and each circle corresponds to either a Junction
or a Source
or Sink
if it is beginning
or ending a Flow
. @scala[Junctions must always be created with defined type parameters, as otherwise the Nothing
type
will be inferred.]
Scala : @@snip GraphDSLDocSpec.scala { #simple-graph-dsl }
Java : @@snip GraphDSLDocTest.java { #simple-graph-dsl }
@@@ note
Junction reference equality defines graph node equality (i.e. the same merge instance used in a GraphDSL refers to the same location in the resulting graph).
@@@
@scala[Notice the import GraphDSL.Implicits._
which brings into scope the ~>
operator (read as "edge", "via" or "to")
and its inverted counterpart <~
(for noting down flows in the opposite direction where appropriate).]
By looking at the snippets above, it should be apparent that the @scala[GraphDSL.Builder
]@java[builder
] object is mutable.
@scala[It is used (implicitly) by the ~>
operator, also making it a mutable operation as well.]
The reason for this design choice is to enable simpler creation of complex graphs, which may even contain cycles.
Once the GraphDSL has been constructed though, the @scala[GraphDSL
]@java[RunnableGraph
] instance is immutable, thread-safe, and freely shareable.
The same is true of all graph pieces—sources, sinks, and flows—once they are constructed.
This means that you can safely re-use one given Flow or junction in multiple places in a processing graph.
We have seen examples of such re-use already above: the merge and broadcast junctions were imported
into the graph using builder.add(...)
, an operation that will make a copy of the blueprint that
is passed to it and return the inlets and outlets of the resulting copy so that they can be wired up.
Another alternative is to pass existing graphs—of any shape—into the factory method that produces a
new graph. The difference between these approaches is that importing using builder.add(...)
ignores the
materialized value of the imported graph while importing via the factory method allows its inclusion;
for more details see @refStream Materialization.
In the example below we prepare a graph that consists of two parallel streams,
in which we re-use the same instance of Flow
, yet it will properly be
materialized as two connections between the corresponding Sources and Sinks:
Scala : @@snip GraphDSLDocSpec.scala { #graph-dsl-reusing-a-flow }
Java : @@snip GraphDSLDocTest.java { #graph-dsl-reusing-a-flow }
Sometimes it is not possible (or needed) to construct the entire computation graph in one place, but instead construct all of its different phases in different places and in the end connect them all into a complete graph and run it.
This can be achieved by @scala[returning a different Shape
than ClosedShape
, for example FlowShape(in, out)
, from the
function given to GraphDSL.create
. See Predefined shapes for a list of such predefined shapes.
Making a Graph
a RunnableGraph
]@java[using the returned Graph
from GraphDSL.create()
rather than
passing it to RunnableGraph.fromGraph()
to wrap it in a RunnableGraph
.The reason of representing it as a different type is that a
RunnableGraph
] requires all ports to be connected, and if they are not
it will throw an exception at construction time, which helps to avoid simple
wiring errors while working with graphs. A partial graph however allows
you to return the set of yet to be connected ports from the code block that
performs the internal wiring.
Let's imagine we want to provide users with a specialized element that given 3 inputs will pick the greatest int value of each zipped triple. We'll want to expose 3 input ports (unconnected sources) and one output port (unconnected sink).
Scala : @@snip StreamPartialGraphDSLDocSpec.scala { #simple-partial-graph-dsl }
Java : @@snip StreamPartialGraphDSLDocTest.java { #simple-partial-graph-dsl }
As you can see, first we construct the partial graph that @scala[contains all the zipping and comparing of stream
elements. This partial graph will have three inputs and one output, wherefore we use the UniformFanInShape
]@java[describes how to compute the maximum of two input streams.
then we reuse that twice while constructing the partial graph that extends this to three input streams].
Then we import it (all of its nodes and connections) explicitly into the @scala[closed graph built in the second step]@java[last graph] in which all
the undefined elements are rewired to real sources and sinks. The graph can then be run and yields the expected result.
@@@ warning
Please note that GraphDSL
is not able to provide compile time type-safety about whether or not all
elements have been properly connected—this validation is performed as a runtime check during the graph's instantiation.
A partial graph also verifies that all ports are either connected or part of the returned Shape
.
@@@
Instead of treating a @scala[partial graph]@java[Graph
] as simply a collection of flows and junctions which may not yet all be
connected it is sometimes useful to expose such a complex graph as a simpler structure,
such as a Source
, Sink
or Flow
.
In fact, these concepts can be easily expressed as special cases of a partially connected graph:
Source
is a partial graph with exactly one output, that is it returns aSourceShape
.Sink
is a partial graph with exactly one input, that is it returns aSinkShape
.Flow
is a partial graph with exactly one input and exactly one output, that is it returns aFlowShape
.
Being able to hide complex graphs inside of simple elements such as Sink / Source / Flow enables you to easily create one complex element and from there on treat it as simple compound stage for linear computations.
In order to create a Source from a graph the method Source.fromGraph
is used, to use it we must have a
@scala[Graph[SourceShape, T]
]@java[Graph
with a SourceShape
]. This is constructed using
@scala[GraphDSL.create
and returning a SourceShape
from the function passed in]@java[GraphDSL.create
and providing building a SourceShape
graph].
The single outlet must be provided to the SourceShape.of
method and will become
“the sink that must be attached before this Source can run”.
Refer to the example below, in which we create a Source that zips together two numbers, to see this graph construction in action:
Scala : @@snip StreamPartialGraphDSLDocSpec.scala { #source-from-partial-graph-dsl }
Java : @@snip StreamPartialGraphDSLDocTest.java { #source-from-partial-graph-dsl }
Similarly the same can be done for a @scala[Sink[T]
]@java[Sink<T>
], using SinkShape.of
in which case the provided value
must be an @scala[Inlet[T]
]@java[Inlet<T>
]. For defining a @scala[Flow[T]
]@java[Flow<T>
] we need to expose both an @scala[inlet and an outlet]@java[undefined source and sink]:
Scala : @@snip StreamPartialGraphDSLDocSpec.scala { #flow-from-partial-graph-dsl }
Java : @@snip StreamPartialGraphDSLDocTest.java { #flow-from-partial-graph-dsl }
There is a simplified API you can use to combine sources and sinks with junctions like:
@scala[Broadcast[T]
, Balance[T]
, Merge[In]
and Concat[A]
]@java[Broadcast<T>
, Balance<T>
, Merge<In>
and Concat<A>
]
without the need for using the Graph DSL. The combine method takes care of constructing
the necessary graph underneath. In following example we combine two sources into one (fan-in):
Scala : @@snip StreamPartialGraphDSLDocSpec.scala { #source-combine }
Java : @@snip StreamPartialGraphDSLDocTest.java { #source-combine }
The same can be done for a @scala[Sink[T]
]@java[Sink
] but in this case it will be fan-out:
Scala : @@snip StreamPartialGraphDSLDocSpec.scala { #sink-combine }
Java : @@snip StreamPartialGraphDSLDocTest.java { #sink-combine }
It is possible to build reusable, encapsulated components of arbitrary input and output ports using the graph DSL.
As an example, we will build a graph junction that represents a pool of workers, where a worker is expressed
as a @scala[Flow[I,O,_]
]@java[Flow<I,O,M>
], i.e. a simple transformation of jobs of type I
to results of type O
(as you have seen
already, this flow can actually contain a complex graph inside). Our reusable worker pool junction will
not preserve the order of the incoming jobs (they are assumed to have a proper ID field) and it will use a Balance
junction to schedule jobs to available workers. On top of this, our junction will feature a "fastlane", a dedicated port
where jobs of higher priority can be sent.
Altogether, our junction will have two input ports of type I
(for the normal and priority jobs) and an output port
of type O
. To represent this interface, we need to define a custom Shape
. The following lines show how to do that.
Scala : @@snip GraphDSLDocSpec.scala { #graph-dsl-components-shape }
In general a custom Shape
needs to be able to provide all its input and output ports, be able to copy itself, and also be
able to create a new instance from given ports. There are some predefined shapes provided to avoid unnecessary
boilerplate:
SourceShape
,SinkShape
,FlowShape
for simpler shapes,UniformFanInShape
andUniformFanOutShape
for junctions with multiple input (or output) ports of the same type,FanInShape1
,FanInShape2
, ...,FanOutShape1
,FanOutShape2
, ... for junctions with multiple input (or output) ports of different types.
Since our shape has two input ports and one output port, we can just use the FanInShape
DSL to define
our custom shape:
Scala : @@snip GraphDSLDocSpec.scala { #graph-dsl-components-shape2 }
Now that we have a Shape
we can wire up a Graph that represents
our worker pool. First, we will merge incoming normal and priority jobs using MergePreferred
, then we will send the jobs
to a Balance
junction which will fan-out to a configurable number of workers (flows), finally we merge all these
results together and send them out through our only output port. This is expressed by the following code:
Scala : @@snip GraphDSLDocSpec.scala { #graph-dsl-components-create }
All we need to do now is to use our custom junction in a graph. The following code simulates some simple workers
and jobs using plain strings and prints out the results. Actually we used two instances of our worker pool junction
using add()
twice.
Scala : @@snip GraphDSLDocSpec.scala { #graph-dsl-components-use }
A graph topology that is often useful is that of two flows going in opposite
directions. Take for example a codec stage that serializes outgoing messages
and deserializes incoming octet streams. Another such stage could add a framing
protocol that attaches a length header to outgoing data and parses incoming
frames back into the original octet stream chunks. These two stages are meant
to be composed, applying one atop the other as part of a protocol stack. For
this purpose exists the special type BidiFlow
which is a graph that
has exactly two open inlets and two open outlets. The corresponding shape is
called BidiShape
and is defined like this:
@@snip Shape.scala { #bidi-shape }
A bidirectional flow is defined just like a unidirectional Flow
as
demonstrated for the codec mentioned above:
Scala : @@snip BidiFlowDocSpec.scala { #codec }
Java : @@snip BidiFlowDocTest.java { #codec }
The first version resembles the partial graph constructor, while for the simple case of a functional 1:1 transformation there is a concise convenience method as shown on the last line. The implementation of the two functions is not difficult either:
Scala : @@snip BidiFlowDocSpec.scala { #codec-impl }
Java : @@snip BidiFlowDocTest.java { #codec-impl }
In this way you could easily integrate any other serialization library that turns an object into a sequence of bytes.
The other stage that we talked about is a little more involved since reversing
a framing protocol means that any received chunk of bytes may correspond to
zero or more messages. This is best implemented using a GraphStage
(see also @refCustom processing with GraphStage).
Scala : @@snip BidiFlowDocSpec.scala { #framing }
Java : @@snip BidiFlowDocTest.java { #framing }
With these implementations we can build a protocol stack and test it:
Scala : @@snip BidiFlowDocSpec.scala { #compose }
Java : @@snip BidiFlowDocTest.java { #compose }
This example demonstrates how BidiFlow
subgraphs can be hooked
together and also turned around with the @scala[.reversed
]@java[.reversed()
] method. The test
simulates both parties of a network communication protocol without actually
having to open a network connection—the flows can just be connected directly.
In certain cases it might be necessary to feed back the materialized value of a Graph (partial, closed or backing a
Source, Sink, Flow or BidiFlow). This is possible by using builder.materializedValue
which gives an Outlet
that
can be used in the graph as an ordinary source or outlet, and which will eventually emit the materialized value.
If the materialized value is needed at more than one place, it is possible to call materializedValue
any number of
times to acquire the necessary number of outlets.
Scala : @@snip GraphDSLDocSpec.scala { #graph-dsl-matvalue }
Java : @@snip GraphDSLDocTest.java { #graph-dsl-matvalue }
Be careful not to introduce a cycle where the materialized value actually contributes to the materialized value.
The following example demonstrates a case where the materialized @scala[Future
]@java[CompletionStage
] of a fold is fed back to the fold itself.
Scala : @@snip GraphDSLDocSpec.scala { #graph-dsl-matvalue-cycle }
Java : @@snip GraphDSLDocTest.java { #graph-dsl-matvalue-cycle }
Cycles in bounded stream topologies need special considerations to avoid potential deadlocks and other liveness issues. This section shows several examples of problems that can arise from the presence of feedback arcs in stream processing graphs.
In the following examples runnable graphs are created but do not run because each have some issue and will deadlock after start.
Source
variable is not defined as the nature and number of element does not matter for described problems.
The first example demonstrates a graph that contains a naïve cycle.
The graph takes elements from the source, prints them, then broadcasts those elements
to a consumer (we just used Sink.ignore
for now) and to a feedback arc that is merged back into the main stream via
a Merge
junction.
@@@ note
The graph DSL allows the connection arrows to be reversed, which is particularly handy when writing cycles—as we will see there are cases where this is very helpful.
@@@
Scala : @@snip GraphCyclesSpec.scala { #deadlocked }
Java : @@snip GraphCyclesDocTest.java { #deadlocked }
Running this we observe that after a few numbers have been printed, no more elements are logged to the console - all processing stops after some time. After some investigation we observe that:
- through merging from
source
we increase the number of elements flowing in the cycle - by broadcasting back to the cycle we do not decrease the number of elements in the cycle
Since Akka Streams (and Reactive Streams in general) guarantee bounded processing (see the "Buffering" section for more
details) it means that only a bounded number of elements are buffered over any time span. Since our cycle gains more and
more elements, eventually all of its internal buffers become full, backpressuring source
forever. To be able
to process more elements from source
elements would need to leave the cycle somehow.
If we modify our feedback loop by replacing the Merge
junction with a MergePreferred
we can avoid the deadlock.
MergePreferred
is unfair as it always tries to consume from a preferred input port if there are elements available
before trying the other lower priority input ports. Since we feed back through the preferred port it is always guaranteed
that the elements in the cycles can flow.
Scala : @@snip GraphCyclesSpec.scala { #unfair }
Java : @@snip GraphCyclesDocTest.java { #unfair }
If we run the example we see that the same sequence of numbers are printed
over and over again, but the processing does not stop. Hence, we avoided the deadlock, but source
is still
back-pressured forever, because buffer space is never recovered: the only action we see is the circulation of a couple
of initial elements from source
.
@@@ note
What we see here is that in certain cases we need to choose between boundedness and liveness. Our first example would not deadlock if there would be an infinite buffer in the loop, or vice versa, if the elements in the cycle would be balanced (as many elements are removed as many are injected) then there would be no deadlock.
@@@
To make our cycle both live (not deadlocking) and fair we can introduce a dropping element on the feedback arc. In this
case we chose the buffer()
operation giving it a dropping strategy OverflowStrategy.dropHead
.
Scala : @@snip GraphCyclesSpec.scala { #dropping }
Java : @@snip GraphCyclesDocTest.java { #dropping }
If we run this example we see that
- The flow of elements does not stop, there are always elements printed
- We see that some of the numbers are printed several times over time (due to the feedback loop) but on average the numbers are increasing in the long term
This example highlights that one solution to avoid deadlocks in the presence of potentially unbalanced cycles
(cycles where the number of circulating elements are unbounded) is to drop elements. An alternative would be to
define a larger buffer with OverflowStrategy.fail
which would fail the stream instead of deadlocking it after
all buffer space has been consumed.
As we discovered in the previous examples, the core problem was the unbalanced nature of the feedback loop. We
circumvented this issue by adding a dropping element, but now we want to build a cycle that is balanced from
the beginning instead. To achieve this we modify our first graph by replacing the Merge
junction with a ZipWith
.
Since ZipWith
takes one element from source
and from the feedback arc to inject one element into the cycle,
we maintain the balance of elements.
Scala : @@snip GraphCyclesSpec.scala { #zipping-dead }
Java : @@snip GraphCyclesDocTest.java { #zipping-dead }
Still, when we try to run the example it turns out that no element is printed at all! After some investigation we realize that:
- In order to get the first element from
source
into the cycle we need an already existing element in the cycle - In order to get an initial element in the cycle we need an element from
source
These two conditions are a typical "chicken-and-egg" problem. The solution is to inject an initial
element into the cycle that is independent from source
. We do this by using a Concat
junction on the backwards
arc that injects a single element using Source.single
.
Scala : @@snip GraphCyclesSpec.scala { #zipping-live }
Java : @@snip GraphCyclesDocTest.java { #zipping-live }
When we run the above example we see that processing starts and never stops. The important takeaway from this example is that balanced cycles often need an initial "kick-off" element to be injected into the cycle.