pekko/akka-docs/rst/java/io-codec.rst

.. _io-java-codec:

Encoding and decoding binary data
=================================

.. warning::

  The IO implementation is marked as **“experimental”** as of its introduction
  in Akka 2.2.0. We will continue to improve this API based on our users’
  feedback, which implies that while we try to keep incompatible changes to a
  minimum the binary compatibility guarantee for maintenance releases does not
  apply to the contents of the `akka.io` package.

Akka adopted and adapted the implementation of data processing pipelines found
in the ``spray-io`` module. The idea is that encoding and decoding often
go hand in hand and keeping the code pertaining to one protocol layer together
is deemed more important than writing down the complete read side—say—in the
iteratee style in one go; pipelines encourage packaging the stages in a form
which lends itself better to reuse in a protocol stack. Another reason for
choosing this abstraction is that it is at times necessary to change the
behavior of encoding and decoding within a stage based on a message stream’s
state, and pipeline stages allow communication between the read and write
halves quite naturally.

The actual byte-fiddling can be done within pipeline stages, for example using
the rich API of :class:`ByteIterator` and :class:`ByteStringBuilder` as shown
below. All these activities are synchronous transformations which benefit
greatly from CPU affinity to make good use of those data caches. Therefore the
design of the pipeline infrastructure is completely synchronous, every stage’s
handler code can only directly return the events and/or commands resulting from
an input, there are no callbacks. Exceptions thrown within a pipeline stage
will abort processing of the whole pipeline under the assumption that
recoverable error conditions will be signaled in-band to the next stage instead
of raising an exception.

An overall “logical” pipeline can span multiple execution contexts, for example
starting with the low-level protocol layers directly within an actor handling
the reads and writes to a TCP connection and then being passed to a number of
higher-level actors which do the costly application level processing. This is
supported by feeding the generated events into a sink which sends them to
another actor, and that other actor will then upon reception feed them into its
own pipeline.

Introducing the Sample Protocol
-------------------------------

In the following the process of implementing a protocol stack using pipelines
is demonstrated on the following simple example:

.. code-block:: text

  frameLen: Int
  persons: Int
  persons times {
    first: String
    last: String
  }
  points: Int
  points times Double

mapping to the following data type:

.. includecode:: code/docs/io/japi/Message.java#message

We will split the handling of this protocol into two parts: the frame-length
encoding handles the buffering necessary on the read side and the actual
encoding of the frame contents is done in a separate stage.

Building a Pipeline Stage
-------------------------

As a common example, which is also included in the ``akka-actor`` package, let
us look at a framing protocol which works by prepending a length field to each
message (the following is a simplified version for demonstration purposes, the
real implementation is more configurable and implemented in Scala).

.. includecode:: code/docs/io/japi/LengthFieldFrame.java
   :include: frame

In the end a pipeline stage is nothing more than a set of three methods: one
transforming commands arriving from above, one transforming events arriving
from below and the third transforming incoming management commands (not shown
here, see below for more information). The result of the transformation can in
either case be a sequence of commands flowing downwards or events flowing
upwards (or a combination thereof).

In the case above the data type for commands and events are equal as both
functions operate only on ``ByteString``, and the transformation does not
change that type because it only adds or removes four octets at the front.

The pair of command and event transformation functions is represented by an
object of type :class:`AbstractPipePair`, or in this case a
:class:`AbstractSymmetricPipePair`.  This object could benefit from knowledge
about the context it is running in, for example an :class:`Actor`, and this
context is introduced by making a :class:`PipelineStage` be a factory for
producing a :class:`PipePair`. The factory method is called :meth:`apply` (a
Scala tradition) and receives the context object as its argument. The
implementation of this factory method could now make use of the context in
whatever way it sees fit, you will see an example further down.

Manipulating ByteStrings
------------------------

The second stage of our sample protocol stack illustrates in more depth what
showed only a little in the pipeline stage built above: constructing and
deconstructing byte strings. Let us first take a look at the encoder:

.. includecode:: code/docs/io/japi/MessageStage.java
   :include: format
   :exclude: decoding-omitted,omitted

Note how the byte order to be used by this stage is fixed in exactly one place,
making it impossible get wrong between commands and events; the way how the
byte order is passed into the stage demonstrates one possible use for the
stage’s ``context`` parameter. 

The basic tool for constucting a :class:`ByteString` is a
:class:`ByteStringBuilder`. This builder is specialized for concatenating byte
representations of the primitive data types like ``Int`` and ``Double`` or
arrays thereof.  Encoding a ``String`` requires a bit more work because not
only the sequence of bytes needs to be encoded but also the length, otherwise
the decoding stage would not know where the ``String`` terminates. When all
values making up the :class:`Message` have been appended to the builder, we
simply pass the resulting :class:`ByteString` on to the next stage as a command
using the optimized :meth:`singleCommand` facility.

.. warning::

  The :meth:`singleCommand` and :meth:`singleEvent` methods provide a way to
  generate responses which transfer exactly one result from one pipeline stage
  to the next without suffering the overhead of object allocations. This means
  that the returned collection object will not work for anything else (you will
  get :class:`ClassCastExceptions`!) and this facility can only be used *EXACTLY
  ONCE* during the processing of one input (command or event).

Now let us look at the decoder side:

.. includecode:: code/docs/io/japi/MessageStage.java
   :include: decoding

The decoding side does the same things that the encoder does in the same order,
it just uses a :class:`ByteIterator` to retrieve primitive data types or arrays
of those from the underlying :class:`ByteString`. And in the end it hands the
assembled :class:`Message` as an event to the next stage using the optimized
:meth:`singleEvent` facility (see warning above).

Building a Pipeline
-------------------

Given the two pipeline stages introduced in the sections above we can now put
them to some use. First we define some message to be encoded:

.. includecode:: code/docs/io/japi/PipelineTest.java
   :include: message

Then we need to create a pipeline context which satisfies our declared needs:

.. includecode:: code/docs/io/japi/PipelineTest.java
   :include: byteorder

Building the pipeline and encoding this message then is quite simple:

.. includecode:: code/docs/io/japi/PipelineTest.java
   :include: build-sink

First we *sequence* the two stages, i.e. attach them such that the output of
one becomes the input of the other. Then we create a :class:`PipelineSink`
which is essentially a callback interface for what shall happen with the
encoded commands or decoded events, respectively. Then we build the pipeline
using the :class:`PipelineFactory`, which returns an interface for feeding
commands and events into this pipeline instance. As a demonstration of how to
use this, we simply encode the message shown above and the resulting
:class:`ByteString` will then be sent to the ``commandHandler`` actor. Decoding
works in the same way, only using :meth:`injectEvent`.

Injecting into a pipeline using a :class:`PipelineInjector` will catch
exceptions resulting from processing the input, in which case the exception
(there can only be one per injection) is passed into the respective sink. The
default implementation of :meth:`onCommandFailure` and :meth:`onEventFailure`
will re-throw the exception (whence originates the ``throws`` declaration of
the ``inject*`` method).

Using the Pipeline’s Context
----------------------------

Up to this point there was always a parameter ``ctx`` which was used when
constructing a pipeline, but it was not explained in full. The context is a
piece of information which is made available to all stages of a pipeline. The
context may also carry behavior, provide infrastructure or helper methods etc.
It should be noted that the context is bound to the pipeline and as such must
not be accessed concurrently from different threads unless care is taken to
properly synchronize such access. Since the context will in many cases be
provided by an actor it is not recommended to share this context with code
executing outside of the actor’s message handling.

.. warning::

  A PipelineContext instance *MUST NOT* be used by two different pipelines
  since it contains mutable fields which are used during message processing.

Using Management Commands
-------------------------

Since pipeline stages do not have any reference to the pipeline or even to
their neighbors they cannot directly effect the injection of commands or events
outside of their normal processing. But sometimes things need to happen driven
by a timer, for example. In this case the timer would need to cause sending
tick messages to the whole pipeline, and those stages which wanted to receive
them would act upon those. In order to keep the type signatures for events and
commands useful, such external triggers are sent out-of-band, via a different
channel—the management port. One example which makes use of this facility is
the :class:`TickGenerator` which comes included with ``akka-actor`` (this is a
transcription of the Scala version which is actually included in the
``akka-actor`` JAR):

.. includecode:: code/docs/io/japi/HasActorContext.java#actor-context

.. includecode:: code/docs/io/japi/TickGenerator.java#tick-generator

This pipeline stage is to be used within an actor, and it will make use of this
context in order to schedule the delivery of ``Tick`` messages; the actor is
then supposed to feed these messages into the management port of the pipeline.
An example could look like this:

.. includecode:: code/docs/io/japi/Processor.java
   :include: actor
   :exclude: omitted

This actor extends our well-known pipeline with the tick generator and attaches
the outputs to functions which send commands and events to actors for further
processing. The pipeline stages will then all receive on ``Tick`` per second
which can be used like so:

.. includecode:: code/docs/io/japi/MessageStage.java
   :include: mgmt-ticks
   :exclude: omitted

.. note::

  Management commands are delivered to all stages of a pipeline “effectively
  parallel”, like on a broadcast medium. No code will actually run concurrently
  since a pipeline is strictly single-threaded, but the order in which these
  commands are processed is not specified.

The intended purpose of management commands is for each stage to define its
special command types and then listen only to those (where the aforementioned
``Tick`` message is a useful counter-example), exactly like sending packets on
a wifi network where every station receives all traffic but reacts only to
those messages which are destined for it.

If you need all stages to react upon something in their defined order, then
this must be modeled either as a command or event, i.e. it will be part of the
“business” type of the pipeline.