The idea is to filter the sources, replacing @<var>@ occurrences with the mapping for <var> (which is currently hard-coded). @@ -> @. In order to make this work, I had to move the doc sources one directory down (into akka-docs/rst) so that the filtered result could be in a sibling directory so that relative links (to _sphinx plugins or real code) would continue to work. While I was at it I also changed it so that WARNINGs and ERRORs are not swallowed into the debug dump anymore but printed at [warn] level (minimum). One piece of fallout is that the (online) html build is now run after the normal one, not in parallel.
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Dataflow Concurrency (Scala)
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============================
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Description
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-----------
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Akka implements `Oz-style dataflow concurrency <http://www.mozart-oz.org/documentation/tutorial/node8.html#chapter.concurrency>`_
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by using a special API for :ref:`futures-scala` that enables a complimentary way of writing synchronous-looking code that in reality is asynchronous.
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The benefit of Dataflow concurrency is that it is deterministic; that means that it will always behave the same.
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If you run it once and it yields output 5 then it will do that **every time**, run it 10 million times - same result.
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If it on the other hand deadlocks the first time you run it, then it will deadlock **every single time** you run it.
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Also, there is **no difference** between sequential code and concurrent code. These properties makes it very easy to reason about concurrency.
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The limitation is that the code needs to be side-effect free, i.e. deterministic.
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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.
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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.
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Getting Started (SBT)
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---------------------
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Scala's Delimited Continuations plugin is required to use the Dataflow API. To enable the plugin when using sbt, your project must inherit the ``AutoCompilerPlugins`` trait and contain a bit of configuration as is seen in this example:
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.. code-block:: scala
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autoCompilerPlugins := true,
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libraryDependencies <+= scalaVersion {
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v => compilerPlugin("org.scala-lang.plugins" % "continuations" % <scalaVersion>)
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},
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scalacOptions += "-P:continuations:enable",
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You will also need to include a dependency on ``akka-dataflow``:
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.. code-block:: scala
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"com.typesafe.akka" %% "akka-dataflow" % "2.1-SNAPSHOT" cross CrossVersion.full
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Dataflow variables
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------------------
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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/Promises :ref:`futures-scala`.
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Conversion from ``Future`` and ``Promise`` to Dataflow Variables is implicit and is invisible to the user (after importing akka.dataflow._).
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The mapping from ``Promise`` and ``Future`` is as follows:
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- Futures are readable-many, using the ``apply`` method, inside ``flow`` blocks.
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- Promises are readable-many, just like Futures.
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- Promises are writable-once, using the ``<<`` operator, inside ``flow`` blocks.
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Writing to an already written Promise throws a ``java.lang.IllegalStateException``,
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this has the effect that races to write a promise will be deterministic,
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only one of the writers will succeed and the others will fail.
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The flow
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--------
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The ``flow`` method acts as the delimiter of dataflow expressions (this also neatly aligns with the concept of delimited continuations),
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and flow-expressions compose. At this point you might wonder what the ``flow``-construct brings to the table that for-comprehensions don't,
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and that is the use of the CPS plugin that makes the *look like* it is synchronous, but in reality is asynchronous and non-blocking.
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The result of a call to ``flow`` is a Future with the resulting value of the flow.
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To be able to use the ``flow`` method, you need to import:
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.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
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:include: import-akka-dataflow
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The ``flow`` method will, just like Futures and Promises, require an implicit ``ExecutionContext`` in scope.
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For the examples here we will use:
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.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
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:include: import-global-implicit
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Using flow
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~~~~~~~~~~
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First off we have the obligatory "Hello world!":
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.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
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:include: simplest-hello-world
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You can also refer to the results of other flows within flows:
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.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
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:include: nested-hello-world-a
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… or:
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.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
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:include: nested-hello-world-b
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Working with variables
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~~~~~~~~~~~~~~~~~~~~~~
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Inside the flow method you can use Promises as Dataflow variables:
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.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
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:include: dataflow-variable-a
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Flow compared to for
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--------------------
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Should I use Dataflow or for-comprehensions?
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.. includecode:: code/docs/dataflow/DataflowDocSpec.scala
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:include: for-vs-flow
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Conclusions:
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- Dataflow has a smaller code footprint and arguably is easier to reason about.
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- For-comprehensions are more general than Dataflow, and can operate on a wide array of types.
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