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Working with Graphs

Dependency

@@dependency[sbt,Maven,Gradle] { group="com.typesafe.akka" artifact="akka-stream_\$scala.binary_version\$" version="\$akka.version\$" }

Introduction

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 operators 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.

Constructing Graphs

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 behavior and making them explicit elements these elements should be rather straightforward to use.

Akka Streams currently provide these junctions (for a detailed list see the @refoperator index):

• 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 type `A` and one of type `B`
• 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>`] – like `Merge` but if elements are available on `preferred` port, it picks from it, otherwise randomly from `others`
• @scala[`MergePrioritized[In]`]@java[`MergePrioritized<In>`] – like `Merge` but if elements are available on all input ports, it picks from them randomly based on their `priority`
• @scala[`MergeLatest[In]`]@java[`MergeLatest<In>`] – (N inputs, 1 output) emits `List[In]`, when i-th input stream emits element, then i-th element in emitted list is updated
• @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 a `ZipWith` specialised to zipping input streams of `A` and `B` 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)

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 GraphDSLTest.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 operators—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 GraphDSLTest.java { #graph-dsl-reusing-a-flow }

In some cases we may have a list of graph elements, for example if they are dynamically created. If these graphs have similar signatures, we can construct a graph collecting all their materialized values as a collection:

Scala : @@snip GraphOpsIntegrationSpec.scala { #graph-from-list }

Java : @@snip GraphDSLTest.java { #graph-from-list }

Constructing and combining Partial Graphs

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 }

@@@ note

While the above example shows composing two 2-input `ZipWith`s, in reality ZipWith already provides numerous overloads including a 3 (and many more) parameter versions. So this could be implemented using one ZipWith using the 3 parameter version, like this: @scala[`ZipWith((a, b, c) => out)`]@java[`ZipWith.create((a, b, c) -> out)`]. (The ZipWith with N input has N+1 type parameter; the last type param is the output type.)

@@@

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`.

@@@

Constructing Sources, Sinks and Flows from Partial Graphs

Instead of treating a @scala[partial graph]@java[`Graph`] as 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 expressed as special cases of a partially connected graph:

• `Source` is a partial graph with exactly one output, that is it returns a `SourceShape`.
• `Sink` is a partial graph with exactly one input, that is it returns a `SinkShape`.
• `Flow` is a partial graph with exactly one input and exactly one output, that is it returns a `FlowShape`.

Being able to hide complex graphs inside of simple elements such as Sink / Source / Flow enables you to create one complex element and from there on treat it as simple compound operator 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 }

Combining Sources and Sinks with simplified API

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 }

Building reusable Graph components

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 }

Predefined shapes

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` and `UniformFanOutShape` 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 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 }

Bidirectional Flows

A graph topology that is often useful is that of two flows going in opposite directions. Take for example a codec operator that serializes outgoing messages and deserializes incoming octet streams. Another such operator 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 operators 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 can integrate any other serialization library that turns an object into a sequence of bytes.

The other operator 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 @ref`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 be connected directly.

Accessing the materialized value inside the Graph

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 GraphDSLTest.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 GraphDSLTest.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 were an infinite buffer in the loop, or vice versa, if the elements in the cycle were 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.