Working with Graphs
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Working with Graphs

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.

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 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 Overview of built-in stages and their semantics):

  • Fan-out
  • Broadcast<T>(1 input, N outputs) given an input element emits to each output
  • Balance<T>(1 input, N outputs) given an input element emits to one of its output ports
  • 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)
  • UnZip<A,B>(1 input, 2 outputs) splits a stream of Pair<A,B> tuples into two streams, one of type A and one of type B
  • Fan-in
  • Merge<In>(N inputs , 1 output) picks randomly from inputs pushing them one by one to its output
  • MergePreferred<In> – like Merge but if elements are available on preferred port, it picks from it, otherwise randomly from others
  • 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
  • Zip<A,B>(2 inputs, 1 output) is a ZipWith specialised to zipping input streams of A and B into a Pair(A,B) tuple stream
  • 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:

../_images/simple-graph-example.png

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.

final Source<Integer, BoxedUnit> in = Source.from(Arrays.asList(1, 2, 3, 4, 5));
final Sink<List<String>, Future<List<String>>> sink = Sink.head();
final Sink<List<Integer>, Future<List<Integer>>> sink2 = Sink.head();
final Flow<Integer, Integer, BoxedUnit> f1 = Flow.of(Integer.class).map(elem -> elem + 10);
final Flow<Integer, Integer, BoxedUnit> f2 = Flow.of(Integer.class).map(elem -> elem + 20);
final Flow<Integer, String, BoxedUnit> f3 = Flow.of(Integer.class).map(elem -> elem.toString());
final Flow<Integer, Integer, BoxedUnit> f4 = Flow.of(Integer.class).map(elem -> elem + 30);

final RunnableGraph<Future<List<String>>> result =
  RunnableGraph.<Future<List<String>>>fromGraph(
    GraphDSL
      .create(
        sink,
        (builder, out) -> {
          final UniformFanOutShape<Integer, Integer> bcast = builder.add(Broadcast.create(2));
          final UniformFanInShape<Integer, Integer> merge = builder.add(Merge.create(2));

          final Outlet<Integer> source = builder.add(in).out();
          builder.from(source).via(builder.add(f1))
            .viaFanOut(bcast).via(builder.add(f2)).viaFanIn(merge)
            .via(builder.add(f3.grouped(1000))).to(out);
          builder.from(bcast).via(builder.add(f4)).toFanIn(merge);
          return ClosedShape.getInstance();
        }));

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).

By looking at the snippets above, it should be apparent that the builder object is mutable. 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 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 Stream 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:

final Sink<Integer, Future<Integer>> topHeadSink = Sink.head();
final Sink<Integer, Future<Integer>> bottomHeadSink = Sink.head();
final Flow<Integer, Integer, BoxedUnit> sharedDoubler = Flow.of(Integer.class).map(elem -> elem * 2);

final RunnableGraph<Pair<Future<Integer>, Future<Integer>>> g =
  RunnableGraph.<Pair<Future<Integer>, Future<Integer>>>fromGraph(
    GraphDSL.create(
      topHeadSink, // import this sink into the graph
      bottomHeadSink, // and this as well
      Keep.both(),
      (b, top, bottom) -> {
        final UniformFanOutShape<Integer, Integer> bcast =
          b.add(Broadcast.create(2));

        b.from(b.add(Source.single(1))).viaFanOut(bcast)
          .via(b.add(sharedDoubler)).to(top);
        b.from(bcast).via(b.add(sharedDoubler)).to(bottom);
        return ClosedShape.getInstance();
      }
    )
  );

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 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).

final Graph<FanInShape2<Integer, Integer, Integer>, BoxedUnit> zip =
  ZipWith.create((Integer left, Integer right) -> Math.max(left, right));

final Graph<UniformFanInShape<Integer, Integer>, BoxedUnit> pickMaxOfThree =
    GraphDSL.create(builder -> {
      final FanInShape2<Integer, Integer, Integer> zip1 = builder.add(zip);
      final FanInShape2<Integer, Integer, Integer> zip2 = builder.add(zip);
      
      builder.from(zip1.out()).toInlet(zip2.in0());
      // return the shape, which has three inputs and one output
      return new UniformFanInShape<Integer, Integer>(zip2.out(), 
          new Inlet[] {zip1.in0(), zip1.in1(), zip2.in1()});
    });

final Sink<Integer, Future<Integer>> resultSink = Sink.<Integer>head();

final RunnableGraph<Future<Integer>> g =
  RunnableGraph.<Future<Integer>>fromGraph(
    GraphDSL.create(resultSink, (builder, sink) -> {
      // import the partial flow graph explicitly
      final UniformFanInShape<Integer, Integer> pm = builder.add(pickMaxOfThree);
      
      builder.from(builder.add(Source.single(1))).toInlet(pm.in(0));
      builder.from(builder.add(Source.single(2))).toInlet(pm.in(1));
      builder.from(builder.add(Source.single(3))).toInlet(pm.in(2));
      builder.from(pm.out()).to(sink);
      return ClosedShape.getInstance();
    }));

final Future<Integer> max = g.run(mat);

As you can see, first we construct the partial graph that 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 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 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 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 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 Graph with a SourceShape. This is constructed using 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:

// first create an indefinite source of integer numbers
class Ints implements Iterator<Integer> {
  private int next = 0;
  @Override
  public boolean hasNext() {
    return true;
  }
  @Override
  public Integer next() {
    return next++;
  }
}
final Source<Integer, BoxedUnit> ints = Source.fromIterator(() -> new Ints());

final Source<Pair<Integer, Integer>, BoxedUnit> pairs = Source.fromGraph(
  GraphDSL.create(
    builder -> {
      final FanInShape2<Integer, Integer, Pair<Integer, Integer>> zip =
          builder.add(Zip.create());

      builder.from(builder.add(ints.filter(i -> i % 2 == 0))).toInlet(zip.in0());
      builder.from(builder.add(ints.filter(i -> i % 2 == 1))).toInlet(zip.in1());
      
      return SourceShape.of(zip.out());
    }));

final Future<Pair<Integer, Integer>> firstPair = 
    pairs.runWith(Sink.<Pair<Integer, Integer>>head(), mat);

Similarly the same can be done for a Sink<T> using SinkShape.of in which case the provided value must be an Inlet<T>. For defining a Flow<T> we need to expose both an undefined source and sink:

final Flow<Integer, Pair<Integer, String>, BoxedUnit> pairs = Flow.fromGraph(GraphDSL.create(
    b -> {
      final UniformFanOutShape<Integer, Integer> bcast = b.add(Broadcast.create(2));
      final FanInShape2<Integer, String, Pair<Integer, String>> zip =
          b.add(Zip.create());

      b.from(bcast).toInlet(zip.in0());
      b.from(bcast).via(b.add(Flow.of(Integer.class).map(i -> i.toString()))).toInlet(zip.in1());
      
      return FlowShape.of(bcast.in(), zip.out());
    }));

Source.single(1).via(pairs).runWith(Sink.<Pair<Integer, String>>head(), mat);

Combining Sources and Sinks with simplified API

There is simplified API you can use to combine sources and sinks with junctions like: 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):

Source<Integer, BoxedUnit> source1 = Source.single(1);
Source<Integer, BoxedUnit> source2 = Source.single(2);

final Source<Integer, BoxedUnit> sources = Source.combine(source1, source2, new ArrayList<>(),
        i -> Merge.<Integer>create(i));
sources.runWith(Sink.<Integer, Integer>fold(0, (a,b) -> a + b), mat);

The same can be done for a Sink but in this case it will be fan-out:

Sink<Integer, BoxedUnit> sendRmotely = Sink.actorRef(actorRef, "Done");
Sink<Integer, Future<BoxedUnit>> localProcessing = Sink.<Integer>foreach(a -> { /*do something useful*/ } );
Sink<Integer, BoxedUnit> sinks = Sink.combine(sendRmotely,localProcessing, new ArrayList<>(), a -> Broadcast.create(a));

Source.<Integer>from(Arrays.asList(new Integer[]{0, 1, 2})).runWith(sinks, mat);

Bidirectional Flows

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:

/**
 * A bidirectional flow of elements that consequently has two inputs and two
 * outputs, arranged like this:
 *
 * {{{
 *        +------+
 *  In1 ~>|      |~> Out1
 *        | bidi |
 * Out2 <~|      |<~ In2
 *        +------+
 * }}}
 */
final case class BidiShape[-In1, +Out1, -In2, +Out2](in1: Inlet[In1 @uncheckedVariance],
                                                     out1: Outlet[Out1 @uncheckedVariance],
                                                     in2: Inlet[In2 @uncheckedVariance],
                                                     out2: Outlet[Out2 @uncheckedVariance]) extends Shape {
  // implementation details elided ...
}

A bidirectional flow is defined just like a unidirectional Flow as demonstrated for the codec mentioned above:

static interface Message {}
static class Ping implements Message {
  final int id;
  public Ping(int id) { this.id = id; }
  @Override
  public boolean equals(Object o) {
    if (o instanceof Ping) {
      return ((Ping) o).id == id;
    } else return false;
  }
  @Override
  public int hashCode() {
    return id;
  }
}
static class Pong implements Message {
  final int id;
  public Pong(int id) { this.id = id; }
  @Override
  public boolean equals(Object o) {
    if (o instanceof Pong) {
      return ((Pong) o).id == id;
    } else return false;
  }
  @Override
  public int hashCode() {
    return id;
  }
}

public static ByteString toBytes(Message msg) {
  // implementation details elided ...
}

public static Message fromBytes(ByteString bytes) {
  // implementation details elided ...
}

public final BidiFlow<Message, ByteString, ByteString, Message, BoxedUnit> codecVerbose =
    BidiFlow.fromGraph(GraphDSL.create(b -> {
      final FlowShape<Message, ByteString> top =
              b.add(Flow.of(Message.class).map(BidiFlowDocTest::toBytes));
      final FlowShape<ByteString, Message> bottom =
              b.add(Flow.of(ByteString.class).map(BidiFlowDocTest::fromBytes));
      return BidiShape.fromFlows(top, bottom);
    }));

public final BidiFlow<Message, ByteString, ByteString, Message, BoxedUnit> codec =
    BidiFlow.fromFunctions(BidiFlowDocTest::toBytes, BidiFlowDocTest::fromBytes);

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:

public static ByteString toBytes(Message msg) {
  if (msg instanceof Ping) {
    final int id = ((Ping) msg).id;
    return new ByteStringBuilder().putByte((byte) 1)
        .putInt(id, ByteOrder.LITTLE_ENDIAN).result();
  } else {
    final int id = ((Pong) msg).id;
    return new ByteStringBuilder().putByte((byte) 2)
        .putInt(id, ByteOrder.LITTLE_ENDIAN).result();
  }
}

public static Message fromBytes(ByteString bytes) {
  final ByteIterator it = bytes.iterator();
  switch(it.getByte()) {
  case 1:
    return new Ping(it.getInt(ByteOrder.LITTLE_ENDIAN));
  case 2:
    return new Pong(it.getInt(ByteOrder.LITTLE_ENDIAN));
  default:
    throw new RuntimeException("message format error");
  }
}

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 Custom processing with GraphStage).

public static ByteString addLengthHeader(ByteString bytes) {
  final int len = bytes.size();
  return new ByteStringBuilder()
    .putInt(len, ByteOrder.LITTLE_ENDIAN)
    .append(bytes)
    .result();
}

public static class FrameParser extends PushPullStage<ByteString, ByteString> {
  // this holds the received but not yet parsed bytes
  private ByteString stash = ByteString.empty();
  // this holds the current message length or -1 if at a boundary
  private int needed = -1;
  
  @Override
  public SyncDirective onPull(Context<ByteString> ctx) {
    return run(ctx);
  }

  @Override
  public SyncDirective onPush(ByteString bytes, Context<ByteString> ctx) {
    stash = stash.concat(bytes);
    return run(ctx);
  }
  
  @Override
  public TerminationDirective onUpstreamFinish(Context<ByteString> ctx) {
    if (stash.isEmpty()) return ctx.finish();
    else return ctx.absorbTermination(); // we still have bytes to emit
  }
  
  private SyncDirective run(Context<ByteString> ctx) {
    if (needed == -1) {
      // are we at a boundary? then figure out next length
      if (stash.size() < 4) return pullOrFinish(ctx);
      else {
        needed = stash.iterator().getInt(ByteOrder.LITTLE_ENDIAN);
        stash = stash.drop(4);
        return run(ctx); // cycle back to possibly already emit the next chunk
      }
    } else if (stash.size() < needed) {
      // we are in the middle of a message, need more bytes
      return pullOrFinish(ctx);
    } else {
      // we have enough to emit at least one message, so do it
      final ByteString emit = stash.take(needed);
      stash = stash.drop(needed);
      needed = -1;
      return ctx.push(emit);
    }
  }
  
  /*
   * After having called absorbTermination() we cannot pull any more, so if we need
   * more data we will just have to give up.
   */
  private SyncDirective pullOrFinish(Context<ByteString> ctx) {
    if (ctx.isFinishing()) return ctx.finish();
    else return ctx.pull();
  }
}

public final BidiFlow<ByteString, ByteString, ByteString, ByteString, BoxedUnit> framing =
    BidiFlow.fromGraph(GraphDSL.create(b -> {
      final FlowShape<ByteString, ByteString> top =
              b.add(Flow.of(ByteString.class).map(BidiFlowDocTest::addLengthHeader));
      final FlowShape<ByteString, ByteString> bottom =
              b.add(Flow.of(ByteString.class).transform(() -> new FrameParser()));
      return BidiShape.fromFlows(top, bottom);
    }));

With these implementations we can build a protocol stack and test it:

/* construct protocol stack
 *         +------------------------------------+
 *         | stack                              |
 *         |                                    |
 *         |  +-------+            +---------+  |
 *    ~>   O~~o       |     ~>     |         o~~O    ~>
 * Message |  | codec | ByteString | framing |  | ByteString
 *    <~   O~~o       |     <~     |         o~~O    <~
 *         |  +-------+            +---------+  |
 *         +------------------------------------+
 */
final BidiFlow<Message, ByteString, ByteString, Message, BoxedUnit> stack =
    codec.atop(framing);

// test it by plugging it into its own inverse and closing the right end
final Flow<Message, Message, BoxedUnit> pingpong = 
    Flow.of(Message.class).collect(new PFBuilder<Message, Message>()
        .match(Ping.class, p -> new Pong(p.id))
        .build()
        );
final Flow<Message, Message, BoxedUnit> flow =
    stack.atop(stack.reversed()).join(pingpong);
final Future<List<Message>> result = Source
    .from(Arrays.asList(0, 1, 2))
    .<Message> map(id -> new Ping(id))
    .via(flow)
    .grouped(10)
    .runWith(Sink.<List<Message>> head(), mat);
final FiniteDuration oneSec = Duration.create(1, TimeUnit.SECONDS);
assertArrayEquals(
    new Message[] { new Pong(0), new Pong(1), new Pong(2) },
    Await.result(result, oneSec).toArray(new Message[0]));

This example demonstrates how BidiFlow subgraphs can be hooked together and also turned around with the .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.

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.

final Sink<Integer, Future<Integer>> foldSink = Sink.<Integer, Integer> fold(0, (a, b) -> {
  return a + b;
});

final Flow<Future<Integer>, Integer, BoxedUnit> flatten = Flow.<Future<Integer>>create()
  .mapAsync(4, x -> {
    return x;
  });

final Flow<Integer, Integer, Future<Integer>> foldingFlow = Flow.fromGraph(
  GraphDSL.create(foldSink,
  (b, fold) -> {
    return FlowShape.of(
      fold.in(),
      b.from(b.materializedValue()).via(b.add(flatten)).out());
  }));

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 Future of a fold is fed back to the fold itself.

// This cannot produce any value:
final Source<Integer, Future<Integer>> cyclicSource = Source.fromGraph(
  GraphDSL.create(foldSink,
  (b, fold) -> {
    // - Fold cannot complete until its upstream mapAsync completes
    // - mapAsync cannot complete until the materialized Future produced by
    //   fold completes
    // As a result this Source will never emit anything, and its materialited
    // Future will never complete
    b.from(b.materializedValue()).via(b.add(flatten)).to(fold);
    return SourceShape.of(b.from(b.materializedValue()).via(b.add(flatten)).out());
  }));

Graph cycles, liveness and deadlocks

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.

The first example demonstrates a graph that contains a naive 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 via a Merge junction.

// WARNING! The graph below deadlocks!
final Flow<Integer, Integer, BoxedUnit> printFlow =
  Flow.of(Integer.class).map(s -> {
    System.out.println(s);
    return s;
  });

RunnableGraph.fromGraph(GraphDSL.create(b -> {
  final UniformFanInShape<Integer, Integer> merge = b.add(Merge.create(2));
  final UniformFanOutShape<Integer, Integer> bcast = b.add(Broadcast.create(2));
  final Outlet<Integer> src = b.add(source).out();
  final FlowShape<Integer, Integer> printer = b.add(printFlow);
  final SinkShape<Integer> ignore = b.add(Sink.ignore());
  
  b.from(src).viaFanIn(merge).via(printer).viaFanOut(bcast).to(ignore);
                  b.to(merge)            .fromFanOut(bcast);
  return ClosedShape.getInstance();
}));

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.

// WARNING! The graph below stops consuming from "source" after a few steps
RunnableGraph.fromGraph(GraphDSL.create(b -> {
  final MergePreferredShape<Integer> merge = b.add(MergePreferred.create(1));
  final UniformFanOutShape<Integer, Integer> bcast = b.add(Broadcast.create(2));
  final Outlet<Integer> src = b.add(source).out();
  final FlowShape<Integer, Integer> printer = b.add(printFlow);
  final SinkShape<Integer> ignore = b.add(Sink.ignore());
  
  b.from(src).viaFanIn(merge).via(printer).viaFanOut(bcast).to(ignore);
                  b.to(merge.preferred()).fromFanOut(bcast);
  return ClosedShape.getInstance();
}));

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.

RunnableGraph.fromGraph(GraphDSL.create(b -> {
  final UniformFanInShape<Integer, Integer> merge = b.add(Merge.create(2));
  final UniformFanOutShape<Integer, Integer> bcast = b.add(Broadcast.create(2));
  final FlowShape<Integer, Integer> droppyFlow = b.add(
    Flow.of(Integer.class).buffer(10, OverflowStrategy.dropHead()));
  final Outlet<Integer> src = b.add(source).out();
  final FlowShape<Integer, Integer> printer = b.add(printFlow);
  final SinkShape<Integer> ignore = b.add(Sink.ignore());
  
  b.from(src).viaFanIn(merge).via(printer).viaFanOut(bcast).to(ignore);
               b.to(merge).via(droppyFlow).fromFanOut(bcast);
  return ClosedShape.getInstance();
}));

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.

// WARNING! The graph below never processes any elements
RunnableGraph.fromGraph(GraphDSL.create(b -> {
  final FanInShape2<Integer, Integer, Integer> zip =
    b.add(ZipWith.create((Integer left, Integer right) -> left));
  final UniformFanOutShape<Integer, Integer> bcast = b.add(Broadcast.create(2));
  final FlowShape<Integer, Integer> printer = b.add(printFlow);
  final SinkShape<Integer> ignore = b.add(Sink.ignore());

  b.from(b.add(source)).toInlet(zip.in0());
  b.from(zip.out()).via(printer).viaFanOut(bcast).to(ignore);
    b.to(zip.in1())            .fromFanOut(bcast);
  return ClosedShape.getInstance();
}));

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.

RunnableGraph.fromGraph(GraphDSL.create(b -> {
  final FanInShape2<Integer, Integer, Integer> zip =
    b.add(ZipWith.create((Integer left, Integer right) -> left));
  final UniformFanOutShape<Integer, Integer> bcast = b.add(Broadcast.create(2));
  final UniformFanInShape<Integer, Integer> concat = b.add(Concat.create());
  final FlowShape<Integer, Integer> printer = b.add(printFlow);
  final SinkShape<Integer> ignore = b.add(Sink.ignore());

  b.from(b.add(source)).toInlet(zip.in0());
  b.from(zip.out()).via(printer).viaFanOut(bcast).to(ignore);
    b.to(zip.in1()).viaFanIn(concat).from(b.add(Source.single(1)));
                        b.to(concat).fromFanOut(bcast);
  return ClosedShape.getInstance();
}));

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.

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