More work on Dataflow docs

akka-docs/scala/dataflow.rst

@@ -4,9 +4,13 @@ Dataflow Concurrency (Scala)
 Description
 -----------
 
-Akka implements `Oz-style dataflow concurrency <http://www.mozart-oz.org/documentation/tutorial/node8.html#chapter.concurrency>`_ by using a special API for :ref:`futures-scala` that allows single assignment variables and multiple lightweight (event-based) processes/threads.
+Akka implements `Oz-style dataflow concurrency <http://www.mozart-oz.org/documentation/tutorial/node8.html#chapter.concurrency>`_ by using a special API for :ref:`futures-scala` that enables a complimentary way of writing synchronous-looking code that in reality is asynchronous.
 
-Dataflow concurrency is deterministic. This means that it will always behave the same. If you run it once and it yields output 5 then it will do that **every time**, run it 10 million times, same result. If it on the other hand deadlocks the first time you run it, then it will deadlock **every single time** you run it. Also, there is **no difference** between sequential code and concurrent code. These properties makes it very easy to reason about concurrency. The limitation is that the code needs to be side-effect free, e.g. deterministic. You can't use exceptions, time, random etc., but need to treat the part of your program that uses dataflow concurrency as a pure function with input and output.
+The benefit of Dataflow concurrency is that it is deterministic; that means that it will always behave the same.
+If you run it once and it yields output 5 then it will do that **every time**, run it 10 million times - same result.
+If it on the other hand deadlocks the first time you run it, then it will deadlock **every single time** you run it.
+Also, there is **no difference** between sequential code and concurrent code. These properties makes it very easy to reason about concurrency.
+The limitation is that the code needs to be side-effect free, e.g. deterministic. You can't use exceptions, time, random etc., but need to treat the part of your program that uses dataflow concurrency as a pure function with input and output.
 
 The best way to learn how to program with dataflow variables is to read the fantastic book `Concepts, Techniques, and Models of Computer Programming <http://www.info.ucl.ac.be/%7Epvr/book.html>`_. By Peter Van Roy and Seif Haridi.
 
@@ -26,21 +30,77 @@ You will also need to include a dependency on akka-dataflow
 
 .. code-block:: scala
 
-  "com.typesafe.akka" % "akka-dataflow" % "2.1-SNAPSHOT"
-
-The flow
---------
-
-The ``flow`` construct acts as the delimeter of dataflow expressions (this also neatly aligns with the concept of delimited continuations),
-and flow-expressions compose. At this point you might wonder what the ``flow``-construct brings to the table that for-comprehensions don't,
-and that is the use of the CPS plugin that makes the look _look like_ it is synchronous, but it indeed isn't when it gets executed.
-
-import scala.concurrent.ExecutionContext.Implicit._
-flow { 5 } onComplete println
+  "com.typesafe.akka" %% "akka-dataflow" % "2.1-SNAPSHOT" cross CrossVersion.full
 
 Dataflow variables
 ------------------
 
 A Dataflow variable can be read any number of times but only be written to once, which maps very well to the concept of Futures :ref:`futures-scala`.
-Conversion from ``Future`` and ``Promise`` to Dataflow is implicit and is invisible to the user.
+Conversion from ``Future`` and ``Promise`` to Dataflow Variables is implicit and is invisible to the user (after importing akka.dataflow._).
 
+The mapping from ``Promise`` and ``Future`` is as follows:
+
+  - Futures are readable-any, using the ``apply`` method, inside ``flow`` blocks.
+  - Promises are readable-any, just like Futures.
+  - Promises are writable-once, using the ``<<`` operator, inside ``flow`` blocks.
+    Writing to an already written Promise throws a ``java.lang.IllegalStateException``,
+    this has the effect that races to write a promise will be deterministic,
+    only one of the writers will succeed and the others will fail.
+
+
+The flow
+--------
+
+The ``flow`` method acts as the delimiter of dataflow expressions (this also neatly aligns with the concept of delimited continuations),
+and flow-expressions compose. At this point you might wonder what the ``flow``-construct brings to the table that for-comprehensions don't,
+and that is the use of the CPS plugin that makes the look _look like_ it is synchronous, but it indeed isn't when it gets executed.
+The result of a call to ``flow`` is a Future with the resulting value of the flow.
+
+To be able to use the ``flow`` method, you need to import:
+
+.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
+   :include: import-akka-dataflow
+
+The ``flow`` method will, just like Futures and Promises, require an implicit ``ExecutionContext`` in scope. for the examples here we will use:
+
+.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
+   :include: import-global-implicit
+
+Using flow
+~~~~~~~~~~
+
+First off we have the obligatory "Hello world!":
+
+.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
+   :include: simplest-hello-world
+
+You can also refer to the results of other flows within flows:
+
+.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
+   :include: nested-hello-world-a
+
+… or:
+
+.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
+   :include: nested-hello-world-b
+
+Working with variables
+~~~~~~~~~~~~~~~~~~~~~~
+
+Inside the flow method you can use Promises as Dataflow variables:
+
+.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
+   :include: #dataflow-variable-a
+
+Flow compared to for
+--------------------
+
+Should I use Dataflow or for-comprehensions?
+
+.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
+   :include: #for-vs-flow
+
+Conclusions:
+
+ - Dataflow has a smaller code footprint and arguably is easier to reason about.
+ - For-comprehensions are more general than Dataflow, and can operate on a wide array of types.