Merge pull request #110 from goodboy/init_sphinx_docs

Initial sphinx docs
2020-02-10 18:07:37 -06:00 · 2020-02-10 18:07:37 -06:00 · 2b2cf2e001
parent 3b3d563ac9 d62610c44e
commit 2b2cf2e001
8 changed files with 779 additions and 797 deletions
--- a/README.rst
+++ b/README.rst
@ -1,28 +1,20 @@
 tractor
 =======
-An async-native "`actor model`_" built on trio_ and multiprocessing_.
+A `structured concurrent`_, async-native "`actor model`_" built on trio_ and multiprocessing_.
-
+|travis| |docs|
 |travis|
 .. |travis| image:: https://img.shields.io/travis/goodboy/tractor/master.svg
    :target: https://travis-ci.org/goodboy/tractor
 .. _actor model: https://en.wikipedia.org/wiki/Actor_model
 .. _trio: https://github.com/python-trio/trio
 .. _multiprocessing: https://en.wikipedia.org/wiki/Multiprocessing
 .. _trionic: https://trio.readthedocs.io/en/latest/design.html#high-level-design-principles
 .. _async sandwich: https://trio.readthedocs.io/en/latest/tutorial.html#async-sandwich
-.. _always propagate: https://trio.readthedocs.io/en/latest/design.html#exceptions-always-propagate
+.. _structured concurrent: https://trio.discourse.group/t/concise-definition-of-structured-concurrency/228
 .. _causality: https://vorpus.org/blog/some-thoughts-on-asynchronous-api-design-in-a-post-asyncawait-world/#c-c-c-c-causality-breaker
 .. _shared nothing architecture: https://en.wikipedia.org/wiki/Shared-nothing_architecture
 .. _cancellation: https://trio.readthedocs.io/en/latest/reference-core.html#cancellation-and-timeouts
 .. _channels: https://en.wikipedia.org/wiki/Channel_(programming)
 .. _chaos engineering: http://principlesofchaos.org/
 ``tractor`` is an attempt to bring trionic_ `structured concurrency`_ to
-distributed multi-core Python.
+distributed multi-core Python; it aims to be the Python multi-processing
 framework *you always wanted*.
 ``tractor`` lets you spawn ``trio`` *"actors"*: processes which each run
 a ``trio`` scheduled task tree (also known as an `async sandwich`_).
@ -31,22 +23,6 @@ sharing any state. This model allows for highly distributed software
 architecture which works just as well on multiple cores as it does over
 many hosts.
 ``tractor`` is an actor-model-*like* system in the sense that it adheres
 to the `3 axioms`_ but does not (yet) fulfil all "unrequirements_" in
 practise. It is an experiment in applying `structured concurrency`_
 constraints on a parallel processing system where multiple Python
 processes exist over many hosts but no process can outlive its parent.
 In `erlang` parlance, it is an architecture where every process has
 a mandatory supervisor enforced by the type system. The API design is
 almost exclusively inspired by trio_'s concepts and primitives (though
 we often lag a little). As a distributed computing system `tractor`
 attempts to place sophistication at the correct layer such that
 concurrency primitives are powerful yet simple, making it easy to build
 complex systems (you can build a "worker pool" architecture but it's
 definitely not required). There is first class support for inter-actor
 streaming using `async generators`_ and ongoing work toward a functional
 reactive style for IPC.
 The first step to grok ``tractor`` is to get the basics of ``trio`` down.
 A great place to start is the `trio docs`_ and this `blog post`_.
@ -59,32 +35,6 @@ A great place to start is the `trio docs`_ and this `blog post`_.
 .. _async generators: https://www.python.org/dev/peps/pep-0525/
 .. contents::
 Philosophy
 ----------
 ``tractor`` aims to be the Python multi-processing framework *you always wanted*.
 Its tenets non-comprehensively include:
 - strict adherence to the `concept-in-progress`_ of *structured concurrency*
 - no spawning of processes *willy-nilly*; causality_ is paramount!
 - (remote) errors `always propagate`_ back to the parent supervisor
 - verbatim support for ``trio``'s cancellation_ system
 - `shared nothing architecture`_
 - no use of *proxy* objects or shared references between processes
 - an immersive debugging experience
 - anti-fragility through `chaos engineering`_
 .. warning:: ``tractor`` is in alpha-alpha and is expected to change rapidly!
    Expect nothing to be set in stone. Your ideas about where it should go
    are greatly appreciated!
 .. _concept-in-progress: https://trio.discourse.group/t/structured-concurrency-kickoff/55
 Install
 -------
 No PyPi release yet!
@ -94,742 +44,6 @@ No PyPi release yet!
    pip install git+git://github.com/goodboy/tractor.git
 Examples
 --------
 Note, if you are on Windows please be sure to see the gotchas section
 before trying these.
 A trynamic first scene
 **********************
 Let's direct a couple *actors* and have them run their lines for
 the hip new film we're shooting:
 .. code:: python
    import tractor
    _this_module = __name__
    the_line = 'Hi my name is {}'
    async def hi():
        return the_line.format(tractor.current_actor().name)
    async def say_hello(other_actor):
        async with tractor.wait_for_actor(other_actor) as portal:
            return await portal.run(_this_module, 'hi')
    async def main():
        """Main tractor entry point, the "master" process (for now
        acts as the "director").
        """
        async with tractor.open_nursery() as n:
            print("Alright... Action!")
            donny = await n.run_in_actor(
                'donny',
                say_hello,
                # arguments are always named
                other_actor='gretchen',
            )
            gretchen = await n.run_in_actor(
                'gretchen',
                say_hello,
                other_actor='donny',
            )
            print(await gretchen.result())
            print(await donny.result())
            print("CUTTTT CUUTT CUT!!! Donny!! You're supposed to say...")
    if __name__ == '__main__':
        tractor.run(main)
 We spawn two *actors*, *donny* and *gretchen*.
 Each actor starts up and executes their *main task* defined by an
 async function, ``say_hello()``.  The function instructs each actor
 to find their partner and say hello by calling their partner's
 ``hi()`` function using something called a *portal*. Each actor
 receives a response and relays that back to the parent actor (in
 this case our "director" executing ``main()``).
 Actor spawning and causality
 ****************************
 ``tractor`` tries to take ``trio``'s concept of causal task lifetimes
 to multi-process land. Accordingly, ``tractor``'s *actor nursery* behaves
 similar to ``trio``'s nursery_. That is, ``tractor.open_nursery()``
 opens an ``ActorNursery`` which **must** wait on spawned *actors* to complete
 (or error) in the same causal_ way ``trio`` waits on spawned subtasks.
 This includes errors from any one actor causing all other actors
 spawned by the same nursery to be cancelled_.
 To spawn an actor and run a function in it, open a *nursery block*
 and use the ``run_in_actor()`` method:
 .. code:: python
    import tractor
    def cellar_door():
       return "Dang that's beautiful"
    async def main():
        """The main ``tractor`` routine.
        """
        async with tractor.open_nursery() as n:
            portal = await n.run_in_actor('some_linguist', cellar_door)
        # The ``async with`` will unblock here since the 'some_linguist'
        # actor has completed its main task ``cellar_door``.
        print(await portal.result())
    if __name__ == '__main__':
        tractor.run(main)
 What's going on?
 - an initial *actor* is started with ``tractor.run()`` and told to execute
  its main task_: ``main()``
 - inside ``main()`` an actor is *spawned* using an ``ActorNusery`` and is told
  to run a single function: ``cellar_door()``
 - a ``portal`` instance (we'll get to what it is shortly)
  returned from ``nursery.run_in_actor()`` is used to communicate with
  the newly spawned *sub-actor*
 - the second actor, *some_linguist*, in a new *process* running a new ``trio`` task_
  then executes ``cellar_door()`` and returns its result over a *channel* back
  to the parent actor
 - the parent actor retrieves the subactor's *final result* using ``portal.result()``
  much like you'd expect from a future_.
 This ``run_in_actor()`` API should look very familiar to users of
 ``asyncio``'s `run_in_executor()`_ which uses a ``concurrent.futures`` Executor_.
 Since you might also want to spawn long running *worker* or *daemon*
 actors, each actor's *lifetime* can be determined based on the spawn
 method:
 - if the actor is spawned using ``run_in_actor()`` it terminates when
  its *main* task completes (i.e. when the (async) function submitted
  to it *returns*). The ``with tractor.open_nursery()`` exits only once
  all actors' main function/task complete (just like the nursery_ in ``trio``)
 - actors can be spawned to *live forever* using the ``start_actor()``
  method and act like an RPC daemon that runs indefinitely (the
  ``with tractor.open_nursery()`` won't exit) until cancelled_
 Here is a similar example using the latter method:
 .. code:: python
    def movie_theatre_question():
        """A question asked in a dark theatre, in a tangent
        (errr, I mean different) process.
        """
        return 'have you ever seen a portal?'
    async def main():
        """The main ``tractor`` routine.
        """
        async with tractor.open_nursery() as n:
            portal = await n.start_actor(
                'frank',
                # enable the actor to run funcs from this current module
                rpc_module_paths=[__name__],
            )
            print(await portal.run(__name__, 'movie_theatre_question'))
            # call the subactor a 2nd time
            print(await portal.run(__name__, 'movie_theatre_question'))
            # the async with will block here indefinitely waiting
            # for our actor "frank" to complete, but since it's an
            # "outlive_main" actor it will never end until cancelled
            await portal.cancel_actor()
 The ``rpc_module_paths`` `kwarg` above is a list of module path
 strings that will be loaded and made accessible for execution in the
 remote actor through a call to ``Portal.run()``. For now this is
 a simple mechanism to restrict the functionality of the remote
 (and possibly daemonized) actor and uses Python's module system to
 limit the allowed remote function namespace(s).
 ``tractor`` is opinionated about the underlying threading model used for
 each *actor*. Since Python has a GIL and an actor model by definition
 shares no state between actors, it fits naturally to use a multiprocessing_
 ``Process``. This allows ``tractor`` programs to leverage not only multi-core
 hardware but also distribute over many hardware hosts (each *actor* can talk
 to all others with ease over standard network protocols).
 .. _task: https://trio.readthedocs.io/en/latest/reference-core.html#tasks-let-you-do-multiple-things-at-once
 .. _nursery: https://trio.readthedocs.io/en/latest/reference-core.html#nurseries-and-spawning
 .. _causal: https://vorpus.org/blog/some-thoughts-on-asynchronous-api-design-in-a-post-asyncawait-world/#causality
 .. _cancelled: https://trio.readthedocs.io/en/latest/reference-core.html#child-tasks-and-cancellation
 .. _run_in_executor(): https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor
 .. _Executor: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Executor
 Cancellation
 ************
 ``tractor`` supports ``trio``'s cancellation_ system verbatim.
 Cancelling a nursery block cancels all actors spawned by it.
 Eventually ``tractor`` plans to support different `supervision strategies`_ like ``erlang``.
 .. _supervision strategies: http://erlang.org/doc/man/supervisor.html#sup_flags
 Remote error propagation
 ************************
 Any task invoked in a remote actor should ship any error(s) back to the calling
 actor where it is raised and expected to be dealt with. This way remote actors
 are never cancelled unless explicitly asked or there's a bug in ``tractor`` itself.
 .. code:: python
    async def assert_err():
        assert 0
    async def main():
        async with tractor.open_nursery() as n:
            real_actors = []
            for i in range(3):
                real_actors.append(await n.start_actor(
                    f'actor_{i}',
                    rpc_module_paths=[__name__],
                ))
            # start one actor that will fail immediately
            await n.run_in_actor('extra', assert_err)
        # should error here with a ``RemoteActorError`` containing
        # an ``AssertionError`` and all the other actors have been cancelled
    try:
        # also raises
        tractor.run(main)
    except tractor.RemoteActorError:
        print("Look Maa that actor failed hard, hehhh!")
 You'll notice the nursery cancellation conducts a *one-cancels-all*
 supervisory strategy `exactly like trio`_. The plan is to add more
 `erlang strategies`_ in the near future by allowing nurseries to accept
 a ``Supervisor`` type.
 .. _exactly like trio: https://trio.readthedocs.io/en/latest/reference-core.html#cancellation-semantics
 .. _erlang strategies: http://learnyousomeerlang.com/supervisors
 IPC using *portals*
 *******************
 ``tractor`` introduces the concept of a *portal* which is an API
 borrowed_ from ``trio``. A portal may seem similar to the idea of
 a RPC future_ except a *portal* allows invoking remote *async* functions and
 generators and intermittently blocking to receive responses. This allows
 for fully async-native IPC between actors.
 When you invoke another actor's routines using a *portal* it looks as though
 it was called locally in the current actor. So when you see a call to
 ``await portal.run()`` what you get back is what you'd expect
 to if you'd called the function directly in-process. This approach avoids
 the need to add any special RPC *proxy* objects to the library by instead just
 relying on the built-in (async) function calling semantics and protocols of Python.
 Depending on the function type ``Portal.run()`` tries to
 correctly interface exactly like a local version of the remote
 built-in Python *function type*. Currently async functions, generators,
 and regular functions are supported. Inspiration for this API comes
 `remote function execution`_ but without the client code being
 concerned about the underlying channels_ system or shipping code
 over the network.
 This *portal* approach turns out to be paricularly exciting with the
 introduction of `asynchronous generators`_ in Python 3.6! It means that
 actors can compose nicely in a data streaming pipeline.
 .. _exactly like trio: https://trio.readthedocs.io/en/latest/reference-core.html#cancellation-semantics
 Streaming
 *********
 By now you've figured out that ``tractor`` lets you spawn process based
 *actors* that can invoke cross-process (async) functions and all with
 structured concurrency built in. But the **real cool stuff** is the
 native support for cross-process *streaming*.
 Asynchronous generators
 +++++++++++++++++++++++
 The default streaming function is simply an async generator definition.
 Every value *yielded* from the generator is delivered to the calling
 portal exactly like if you had invoked the function in-process meaning
 you can ``async for`` to receive each value on the calling side.
 As an example here's a parent actor that streams for 1 second from a
 spawned subactor:
 .. code:: python
    from itertools import repeat
    import trio
    import tractor
    async def stream_forever():
        for i in repeat("I can see these little future bubble things"):
            # each yielded value is sent over the ``Channel`` to the
            # parent actor
            yield i
            await trio.sleep(0.01)
    async def main():
        # stream for at most 1 seconds
        with trio.move_on_after(1) as cancel_scope:
            async with tractor.open_nursery() as n:
                portal = await n.start_actor(
                    f'donny',
                    rpc_module_paths=[__name__],
                )
                # this async for loop streams values from the above
                # async generator running in a separate process
                async for letter in await portal.run(__name__, 'stream_forever'):
                    print(letter)
        # we support trio's cancellation system
        assert cancel_scope.cancelled_caught
        assert n.cancelled
    tractor.run(main)
 By default async generator functions are treated as inter-actor
 *streams* when invoked via a portal (how else could you really interface
 with them anyway) so no special syntax to denote the streaming *service*
 is necessary.
 Channels and Contexts
 +++++++++++++++++++++
 If you aren't fond of having to write an async generator to stream data
 between actors (or need something more flexible) you can instead use
 a ``Context``. A context wraps an actor-local spawned task and
 a ``Channel`` so that tasks executing across multiple processes can
 stream data to one another using a low level, request oriented API.
 A ``Channel`` wraps an underlying *transport* and *interchange* format
 to enable *inter-actor-communication*. In its present state ``tractor``
 uses TCP and msgpack_.
 As an example if you wanted to create a streaming server without writing
 an async generator that *yields* values you instead define a decorated
 async function:
 .. code:: python
   @tractor.stream
   async def streamer(ctx: tractor.Context, rate: int = 2) -> None:
      """A simple web response streaming server.
      """
      while True:
         val = await web_request('http://data.feed.com')
         # this is the same as ``yield`` in the async gen case
         await ctx.send_yield(val)
         await trio.sleep(1 / rate)
 You must decorate the function with ``@tractor.stream`` and declare
 a ``ctx`` argument as the first in your function signature and then
 ``tractor`` will treat the async function like an async generator - as
 a stream from the calling/client side.
 This turns out to be handy particularly if you have multiple tasks
 pushing responses concurrently:
 .. code:: python
   async def streamer(
      ctx: tractor.Context,
      rate: int = 2
   ) -> None:
      """A simple web response streaming server.
      """
      while True:
         val = await web_request(url)
         # this is the same as ``yield`` in the async gen case
         await ctx.send_yield(val)
         await trio.sleep(1 / rate)
   @tractor.stream
   async def stream_multiple_sources(
      ctx: tractor.Context,
      sources: List[str]
   ) -> None:
      async with trio.open_nursery() as n:
         for url in sources:
            n.start_soon(streamer, ctx, url)
 The context notion comes from the context_ in nanomsg_.
 .. _context: https://nanomsg.github.io/nng/man/tip/nng_ctx.5
 .. _msgpack: https://en.wikipedia.org/wiki/MessagePack
 A full fledged streaming service
 ++++++++++++++++++++++++++++++++
 Alright, let's get fancy.
 Say you wanted to spawn two actors which each pull data feeds from
 two different sources (and wanted this work spread across 2 cpus).
 You also want to aggregate these feeds, do some processing on them and then
 deliver the final result stream to a client (or in this case parent) actor
 and print the results to your screen:
 .. code:: python
   import time
   import trio
   import tractor
   # this is the first 2 actors, streamer_1 and streamer_2
   async def stream_data(seed):
       for i in range(seed):
           yield i
           await trio.sleep(0)  # trigger scheduler
   # this is the third actor; the aggregator
   async def aggregate(seed):
       """Ensure that the two streams we receive match but only stream
       a single set of values to the parent.
       """
       async with tractor.open_nursery() as nursery:
           portals = []
           for i in range(1, 3):
               # fork point
               portal = await nursery.start_actor(
                   name=f'streamer_{i}',
                   rpc_module_paths=[__name__],
               )
               portals.append(portal)
           send_chan, recv_chan = trio.open_memory_channel(500)
           async def push_to_chan(portal, send_chan):
               async with send_chan:
                   async for value in await portal.run(
                       __name__, 'stream_data', seed=seed
                   ):
                       # leverage trio's built-in backpressure
                       await send_chan.send(value)
               print(f"FINISHED ITERATING {portal.channel.uid}")
           # spawn 2 trio tasks to collect streams and push to a local queue
           async with trio.open_nursery() as n:
               for portal in portals:
                   n.start_soon(push_to_chan, portal, send_chan.clone())
               # close this local task's reference to send side
               await send_chan.aclose()
               unique_vals = set()
               async with recv_chan:
                   async for value in recv_chan:
                       if value not in unique_vals:
                           unique_vals.add(value)
                           # yield upwards to the spawning parent actor
                           yield value
                   assert value in unique_vals
               print("FINISHED ITERATING in aggregator")
           await nursery.cancel()
           print("WAITING on `ActorNursery` to finish")
       print("AGGREGATOR COMPLETE!")
   # this is the main actor and *arbiter*
   async def main():
       # a nursery which spawns "actors"
       async with tractor.open_nursery() as nursery:
           seed = int(1e3)
           import time
           pre_start = time.time()
           portal = await nursery.run_in_actor(
               'aggregator',
               aggregate,
               seed=seed,
           )
           start = time.time()
           # the portal call returns exactly what you'd expect
           # as if the remote "aggregate" function was called locally
           result_stream = []
           async for value in await portal.result():
               result_stream.append(value)
           print(f"STREAM TIME = {time.time() - start}")
           print(f"STREAM + SPAWN TIME = {time.time() - pre_start}")
           assert result_stream == list(range(seed))
           return result_stream
   final_stream = tractor.run(main, arbiter_addr=('127.0.0.1', 1616))
 Here there's four actors running in separate processes (using all the
 cores on you machine). Two are streaming by *yielding* values from the
 ``stream_data()`` async generator, one is aggregating values from
 those two in ``aggregate()`` (also an async generator) and shipping the
 single stream of unique values up the parent actor (the ``'MainProcess'``
 as ``multiprocessing`` calls it) which is running ``main()``. 
 .. _future: https://en.wikipedia.org/wiki/Futures_and_promises
 .. _borrowed:
    https://trio.readthedocs.io/en/latest/reference-core.html#getting-back-into-the-trio-thread-from-another-thread
 .. _asynchronous generators: https://www.python.org/dev/peps/pep-0525/
 .. _remote function execution: https://codespeak.net/execnet/example/test_info.html#remote-exec-a-function-avoiding-inlined-source-part-i
 Actor local variables
 *********************
 Although ``tractor`` uses a *shared-nothing* architecture between processes
 you can of course share state between tasks running *within* an actor.
 ``trio`` tasks spawned via multiple RPC calls to an actor can access global
 state using the per actor ``statespace`` dictionary:
 .. code:: python
        statespace = {'doggy': 10}
        def check_statespace():
            # Remember this runs in a new process so no changes
            # will propagate back to the parent actor
            assert tractor.current_actor().statespace == statespace
        async def main():
            async with tractor.open_nursery() as n:
                await n.run_in_actor(
                    'checker',
                    check_statespace,
                    statespace=statespace
                )
 Of course you don't have to use the ``statespace`` variable (it's mostly
 a convenience for passing simple data to newly spawned actors); building
 out a state sharing system per-actor is totally up to you.
 Service Discovery
 *****************
 Though it will be built out much more in the near future, ``tractor``
 currently keeps track of actors by ``(name: str, id: str)`` using a
 special actor called the *arbiter*. Currently the *arbiter* must exist
 on a host (or it will be created if one can't be found) and keeps a
 simple ``dict`` of actor names to sockets for discovery by other actors.
 Obviously this can be made more sophisticated (help me with it!) but for
 now it does the trick.
 To find the arbiter from the current actor use the ``get_arbiter()`` function and to
 find an actor's socket address by name use the ``find_actor()`` function:
 .. code:: python
    import tractor
    async def main(service_name):
        async with tractor.get_arbiter() as portal:
            print(f"Arbiter is listening on {portal.channel}")
        async with tractor.find_actor(service_name) as sockaddr:
            print(f"my_service is found at {my_service}")
    tractor.run(main, 'some_actor_name')
 The ``name`` value you should pass to ``find_actor()`` is the one you passed as the
 *first* argument to either ``tractor.run()`` or ``ActorNursery.start_actor()``.
 Running actors standalone
 *************************
 You don't have to spawn any actors using ``open_nursery()`` if you just
 want to run a single actor that connects to an existing cluster.
 All the comms and arbiter registration stuff still works. This can
 somtimes turn out being handy when debugging mult-process apps when you
 need to hop into a debugger. You just need to pass the existing
 *arbiter*'s socket address you'd like to connect to:
 .. code:: python
    tractor.run(main, arbiter_addr=('192.168.0.10', 1616))
 Choosing a process spawning backend
 ***********************************
 ``tractor`` is architected to support multiple actor (sub-process)
 spawning backends. Specific defaults are chosen based on your system
 but you can also explicitly select a backend of choice at startup
 via a ``start_method`` kwarg to ``tractor.run()``.
 Currently the options available are:
 - ``trio_run_in_process``: a ``trio``-native spawner from the `Ethereum community`_
 - ``spawn``: one of the stdlib's ``multiprocessing`` `start methods`_
 - ``forkserver``: a faster ``multiprocessing`` variant that is Unix only
 .. _start methods: https://docs.python.org/3.8/library/multiprocessing.html#contexts-and-start-methods
 .. _Ethereum community : https://github.com/ethereum/trio-run-in-process
 ``trio-run-in-process``
 +++++++++++++++++++++++
 `trio-run-in-process`_ is a young "pure ``trio``" process spawner
 which utilizes the native `trio subprocess APIs`_. It has shown great
 reliability under testing for predictable teardown when launching
 recursive pools of actors (multiple nurseries deep) and as such has been
 chosen as the default backend on \*nix systems.
 .. _trio-run-in-process: https://github.com/ethereum/trio-run-in-process
 .. _trio subprocess APIs : https://trio.readthedocs.io/en/stable/reference-io.html#spawning-subprocesses
 ``multiprocessing``
 +++++++++++++++++++
 There is support for the stdlib's ``multiprocessing`` `start methods`_.
 Note that on Windows *spawn* it the only supported method and on \*nix
 systems *forkserver* is the best method for speed but has the caveat
 that it will break easily (hangs due to broken pipes) if spawning actors
 using nested nurseries.
 In general, the ``multiprocessing`` backend **has not proven reliable**
 for handling errors from actors more then 2 nurseries *deep* (see `#89`_).
 If you for some reason need this consider sticking with alternative
 backends.
 .. _#89: https://github.com/goodboy/tractor/issues/89
 Windows "gotchas"
 ^^^^^^^^^^^^^^^^^
 On Windows (which requires the use of the stdlib's `multiprocessing`
 package) there are some gotchas. Namely, the need for calling
 `freeze_support()`_ inside the ``__main__`` context.  Additionally you
 may need place you `tractor` program entry point in a seperate
 `__main__.py` module in your package in order to avoid an error like the
 following ::
    Traceback (most recent call last):
      File "C:\ProgramData\Miniconda3\envs\tractor19030601\lib\site-packages\tractor\_actor.py", line 234, in _get_rpc_func
        return getattr(self._mods[ns], funcname)
    KeyError: '__mp_main__'
 To avoid this, the following is the **only code** that should be in your
 main python module of the program:
 .. code:: python
    # application/__main__.py
    import tractor
    import multiprocessing
    from . import tractor_app
    if __name__ == '__main__':
        multiprocessing.freeze_support()
        tractor.run(tractor_app.main)
 And execute as::
    python -m application
 See `#61`_ and `#79`_ for further details.
 .. _freeze_support(): https://docs.python.org/3/library/multiprocessing.html#multiprocessing.freeze_support
 .. _#61: https://github.com/goodboy/tractor/pull/61#issuecomment-470053512
 .. _#79: https://github.com/goodboy/tractor/pull/79
 Enabling logging
 ****************
 Considering how complicated distributed software can become it helps to know
 what exactly it's doing (even at the lowest levels). Luckily ``tractor`` has
 tons of logging throughout the core. ``tractor`` isn't opinionated on
 how you use this information and users are expected to consume log messages in
 whichever way is appropriate for the system at hand. That being said, when hacking
 on ``tractor`` there is a prettified console formatter which you can enable to
 see what the heck is going on. Just put the following somewhere in your code:
 .. code:: python
    from tractor.log import get_console_log
    log = get_console_log('trace')
 What the future holds
 ---------------------
 Stuff I'd like to see ``tractor`` do real soon:
 - TLS_, duh.
 - erlang-like supervisors_
 - native support for `nanomsg`_ as a channel transport
 - native `gossip protocol`_ support for service discovery and arbiter election
 - a distributed log ledger for tracking cluster behaviour
 - a slick multi-process aware debugger much like in celery_
  but with better `pdb++`_ support
 - an extensive `chaos engineering`_ test suite
 - support for reactive programming primitives and native support for asyncitertools_ like libs
 - introduction of a `capability-based security`_ model
 .. _TLS: https://trio.readthedocs.io/en/latest/reference-io.html#ssl-tls-support
 .. _supervisors: https://github.com/goodboy/tractor/issues/22
 .. _nanomsg: https://nanomsg.github.io/nng/index.html
 .. _gossip protocol: https://en.wikipedia.org/wiki/Gossip_protocol
 .. _celery: http://docs.celeryproject.org/en/latest/userguide/debugging.html
 .. _asyncitertools: https://github.com/vodik/asyncitertools
 .. _pdb++: https://github.com/antocuni/pdb
 .. _capability-based security: https://en.wikipedia.org/wiki/Capability-based_security
 Feel like saying hi?
 --------------------
 This project is very much coupled to the ongoing development of
@ -838,3 +52,10 @@ community). If you want to help, have suggestions or just want to
 say hi, please feel free to ping me on the `trio gitter channel`_!
 .. _trio gitter channel: https://gitter.im/python-trio/general
 .. |travis| image:: https://img.shields.io/travis/goodboy/tractor/master.svg
    :target: https://travis-ci.org/goodboy/tractor
 .. |docs| image:: https://readthedocs.org/projects/tractor/badge/?version=latest
    :target: https://tractor.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status
--- a/docs/Makefile
+++ b/docs/Makefile
@ -0,0 +1,20 @@
 # Minimal makefile for Sphinx documentation
 #
 # You can set these variables from the command line, and also
 # from the environment for the first two.
 SPHINXOPTS    ?=
 SPHINXBUILD   ?= sphinx-build
 SOURCEDIR     = .
 BUILDDIR      = _build
 # Put it first so that "make" without argument is like "make help".
 help:
 	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
 .PHONY: help Makefile
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
--- a/docs/conf.py
+++ b/docs/conf.py
@ -0,0 +1,89 @@
 # Configuration file for the Sphinx documentation builder.
 #
 # This file only contains a selection of the most common options. For a full
 # list see the documentation:
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 # -- Path setup --------------------------------------------------------------
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 # import os
 # import sys
 # sys.path.insert(0, os.path.abspath('.'))
 # Warn about all references to unknown targets
 nitpicky = True
 # The master toctree document.
 master_doc = 'index'
 # -- Project information -----------------------------------------------------
 project = 'tractor'
 copyright = '2018, Tyler Goodlet'
 author = 'Tyler Goodlet'
 # The full version, including alpha/beta/rc tags
 release = '0.0.0a0.dev0'
 # -- General configuration ---------------------------------------------------
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
    'sphinx.ext.autodoc',
    'sphinx.ext.intersphinx',
    'sphinx.ext.todo',
 ]
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
 exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
 # -- Options for HTML output -------------------------------------------------
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
 html_theme = 'alabaster'
 pygments_style = 'sphinx'
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
 # documentation.
 html_theme_options = {
    'description': 'A trionic "actor model"',
    'github_user': 'goodboy',
    'github_repo': 'tractor',
    'github_button': 'true',
    'github_banner': 'true',
    'page_width': '1080px',
    'fixed_sidebar': 'false',
    # 'sidebar_width': '200px',
    'travis_button': 'true',
 }
 html_sidebars = {
    "**": ["about.html", "relations.html", "searchbox.html"]
 }
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ['_static']
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
    "python": ("https://docs.python.org/3", None),
    "pytest": ("https://docs.pytest.org/en/latest", None),
    "setuptools": ("https://setuptools.readthedocs.io/en/latest", None),
 }
--- a/docs/index.rst
+++ b/docs/index.rst
@ -0,0 +1,584 @@
 .. tractor documentation master file, created by
   sphinx-quickstart on Sun Feb  9 22:26:51 2020.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.
 tractor
 =======
 A `structured concurrent`_, async-native "`actor model`_" built on trio_ and multiprocessing_.
 .. toctree::
   :maxdepth: 2
   :caption: Contents:
 .. _actor model: https://en.wikipedia.org/wiki/Actor_model
 .. _trio: https://github.com/python-trio/trio
 .. _multiprocessing: https://en.wikipedia.org/wiki/Multiprocessing
 .. _trionic: https://trio.readthedocs.io/en/latest/design.html#high-level-design-principles
 .. _async sandwich: https://trio.readthedocs.io/en/latest/tutorial.html#async-sandwich
 .. _structured concurrent: https://trio.discourse.group/t/concise-definition-of-structured-concurrency/228
 ``tractor`` is an attempt to bring trionic_ `structured concurrency`_ to
 distributed multi-core Python; it aims to be the Python multi-processing
 framework *you always wanted*.
 ``tractor`` lets you spawn ``trio`` *"actors"*: processes which each run
 a ``trio`` scheduled task tree (also known as an `async sandwich`_).
 *Actors* communicate by exchanging asynchronous messages_ and avoid
 sharing any state. This model allows for highly distributed software
 architecture which works just as well on multiple cores as it does over
 many hosts.
 The first step to grok ``tractor`` is to get the basics of ``trio`` down.
 A great place to start is the `trio docs`_ and this `blog post`_.
 .. _messages: https://en.wikipedia.org/wiki/Message_passing
 .. _trio docs: https://trio.readthedocs.io/en/latest/
 .. _blog post: https://vorpus.org/blog/notes-on-structured-concurrency-or-go-statement-considered-harmful/
 .. _structured concurrency: https://vorpus.org/blog/notes-on-structured-concurrency-or-go-statement-considered-harmful/
 Install
 -------
 No PyPi release yet!
 ::
    pip install git+git://github.com/goodboy/tractor.git
 Feel like saying hi?
 --------------------
 This project is very much coupled to the ongoing development of
 ``trio`` (i.e. ``tractor`` gets all its ideas from that brilliant
 community). If you want to help, have suggestions or just want to
 say hi, please feel free to ping me on the `trio gitter channel`_!
 .. _trio gitter channel: https://gitter.im/python-trio/general
 .. contents::
 Philosophy
 ----------
 Our tenets non-comprehensively include:
 - strict adherence to the `concept-in-progress`_ of *structured concurrency*
 - no spawning of processes *willy-nilly*; causality_ is paramount!
 - (remote) errors `always propagate`_ back to the parent supervisor
 - verbatim support for ``trio``'s cancellation_ system
 - `shared nothing architecture`_
 - no use of *proxy* objects or shared references between processes
 - an immersive debugging experience
 - anti-fragility through `chaos engineering`_
 ``tractor`` is an actor-model-*like* system in the sense that it adheres
 to the `3 axioms`_ but does not (yet) fulfil all "unrequirements_" in
 practise. It is an experiment in applying `structured concurrency`_
 constraints on a parallel processing system where multiple Python
 processes exist over many hosts but no process can outlive its parent.
 In `erlang` parlance, it is an architecture where every process has
 a mandatory supervisor enforced by the type system. The API design is
 almost exclusively inspired by trio_'s concepts and primitives (though
 we often lag a little). As a distributed computing system `tractor`
 attempts to place sophistication at the correct layer such that
 concurrency primitives are powerful yet simple, making it easy to build
 complex systems (you can build a "worker pool" architecture but it's
 definitely not required). There is first class support for inter-actor
 streaming using `async generators`_ and ongoing work toward a functional
 reactive style for IPC.
 .. warning:: ``tractor`` is in alpha-alpha and is expected to change rapidly!
    Expect nothing to be set in stone. Your ideas about where it should go
    are greatly appreciated!
 .. _concept-in-progress: https://trio.discourse.group/t/structured-concurrency-kickoff/55
 .. _3 axioms: https://en.wikipedia.org/wiki/Actor_model#Fundamental_concepts
 .. _unrequirements: https://en.wikipedia.org/wiki/Actor_model#Direct_communication_and_asynchrony
 .. _async generators: https://www.python.org/dev/peps/pep-0525/
 .. _always propagate: https://trio.readthedocs.io/en/latest/design.html#exceptions-always-propagate
 .. _causality: https://vorpus.org/blog/some-thoughts-on-asynchronous-api-design-in-a-post-asyncawait-world/#c-c-c-c-causality-breaker
 .. _shared nothing architecture: https://en.wikipedia.org/wiki/Shared-nothing_architecture
 .. _cancellation: https://trio.readthedocs.io/en/latest/reference-core.html#cancellation-and-timeouts
 .. _channels: https://en.wikipedia.org/wiki/Channel_(programming)
 .. _chaos engineering: http://principlesofchaos.org/
 Examples
 --------
 Note, if you are on Windows please be sure to see the :ref:`gotchas
 <windowsgotchas>` section before trying these.
 A trynamic first scene
 **********************
 Let's direct a couple *actors* and have them run their lines for
 the hip new film we're shooting:
 .. literalinclude:: ../examples/a_trynamic_first_scene.py
 We spawn two *actors*, *donny* and *gretchen*.
 Each actor starts up and executes their *main task* defined by an
 async function, ``say_hello()``.  The function instructs each actor
 to find their partner and say hello by calling their partner's
 ``hi()`` function using something called a *portal*. Each actor
 receives a response and relays that back to the parent actor (in
 this case our "director" executing ``main()``).
 Actor spawning and causality
 ****************************
 ``tractor`` tries to take ``trio``'s concept of causal task lifetimes
 to multi-process land. Accordingly, ``tractor``'s *actor nursery* behaves
 similar to ``trio``'s nursery_. That is, ``tractor.open_nursery()``
 opens an ``ActorNursery`` which **must** wait on spawned *actors* to complete
 (or error) in the same causal_ way ``trio`` waits on spawned subtasks.
 This includes errors from any one actor causing all other actors
 spawned by the same nursery to be cancelled_.
 To spawn an actor and run a function in it, open a *nursery block*
 and use the ``run_in_actor()`` method:
 .. literalinclude:: ../examples/actor_spawning_and_causality.py
 What's going on?
 - an initial *actor* is started with ``tractor.run()`` and told to execute
  its main task_: ``main()``
 - inside ``main()`` an actor is *spawned* using an ``ActorNusery`` and is told
  to run a single function: ``cellar_door()``
 - a ``portal`` instance (we'll get to what it is shortly)
  returned from ``nursery.run_in_actor()`` is used to communicate with
  the newly spawned *sub-actor*
 - the second actor, *some_linguist*, in a new *process* running a new ``trio`` task_
  then executes ``cellar_door()`` and returns its result over a *channel* back
  to the parent actor
 - the parent actor retrieves the subactor's *final result* using ``portal.result()``
  much like you'd expect from a future_.
 This ``run_in_actor()`` API should look very familiar to users of
 ``asyncio``'s `run_in_executor()`_ which uses a ``concurrent.futures`` Executor_.
 Since you might also want to spawn long running *worker* or *daemon*
 actors, each actor's *lifetime* can be determined based on the spawn
 method:
 - if the actor is spawned using ``run_in_actor()`` it terminates when
  its *main* task completes (i.e. when the (async) function submitted
  to it *returns*). The ``with tractor.open_nursery()`` exits only once
  all actors' main function/task complete (just like the nursery_ in ``trio``)
 - actors can be spawned to *live forever* using the ``start_actor()``
  method and act like an RPC daemon that runs indefinitely (the
  ``with tractor.open_nursery()`` won't exit) until cancelled_
 Here is a similar example using the latter method:
 .. literalinclude:: ../examples/actor_spawning_and_causality_with_daemon.py
 The ``rpc_module_paths`` `kwarg` above is a list of module path
 strings that will be loaded and made accessible for execution in the
 remote actor through a call to ``Portal.run()``. For now this is
 a simple mechanism to restrict the functionality of the remote
 (and possibly daemonized) actor and uses Python's module system to
 limit the allowed remote function namespace(s).
 ``tractor`` is opinionated about the underlying threading model used for
 each *actor*. Since Python has a GIL and an actor model by definition
 shares no state between actors, it fits naturally to use a multiprocessing_
 ``Process``. This allows ``tractor`` programs to leverage not only multi-core
 hardware but also distribute over many hardware hosts (each *actor* can talk
 to all others with ease over standard network protocols).
 .. _task: https://trio.readthedocs.io/en/latest/reference-core.html#tasks-let-you-do-multiple-things-at-once
 .. _nursery: https://trio.readthedocs.io/en/latest/reference-core.html#nurseries-and-spawning
 .. _causal: https://vorpus.org/blog/some-thoughts-on-asynchronous-api-design-in-a-post-asyncawait-world/#causality
 .. _cancelled: https://trio.readthedocs.io/en/latest/reference-core.html#child-tasks-and-cancellation
 .. _run_in_executor(): https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor
 .. _Executor: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Executor
 Cancellation
 ************
 ``tractor`` supports ``trio``'s cancellation_ system verbatim.
 Cancelling a nursery block cancels all actors spawned by it.
 Eventually ``tractor`` plans to support different `supervision strategies`_ like ``erlang``.
 .. _supervision strategies: http://erlang.org/doc/man/supervisor.html#sup_flags
 Remote error propagation
 ************************
 Any task invoked in a remote actor should ship any error(s) back to the calling
 actor where it is raised and expected to be dealt with. This way remote actors
 are never cancelled unless explicitly asked or there's a bug in ``tractor`` itself.
 .. literalinclude:: ../examples/remote_error_propagation.py
 You'll notice the nursery cancellation conducts a *one-cancels-all*
 supervisory strategy `exactly like trio`_. The plan is to add more
 `erlang strategies`_ in the near future by allowing nurseries to accept
 a ``Supervisor`` type.
 .. _exactly like trio: https://trio.readthedocs.io/en/latest/reference-core.html#cancellation-semantics
 .. _erlang strategies: http://learnyousomeerlang.com/supervisors
 IPC using *portals*
 *******************
 ``tractor`` introduces the concept of a *portal* which is an API
 borrowed_ from ``trio``. A portal may seem similar to the idea of
 a RPC future_ except a *portal* allows invoking remote *async* functions and
 generators and intermittently blocking to receive responses. This allows
 for fully async-native IPC between actors.
 When you invoke another actor's routines using a *portal* it looks as though
 it was called locally in the current actor. So when you see a call to
 ``await portal.run()`` what you get back is what you'd expect
 to if you'd called the function directly in-process. This approach avoids
 the need to add any special RPC *proxy* objects to the library by instead just
 relying on the built-in (async) function calling semantics and protocols of Python.
 Depending on the function type ``Portal.run()`` tries to
 correctly interface exactly like a local version of the remote
 built-in Python *function type*. Currently async functions, generators,
 and regular functions are supported. Inspiration for this API comes
 `remote function execution`_ but without the client code being
 concerned about the underlying channels_ system or shipping code
 over the network.
 This *portal* approach turns out to be paricularly exciting with the
 introduction of `asynchronous generators`_ in Python 3.6! It means that
 actors can compose nicely in a data streaming pipeline.
 .. _exactly like trio: https://trio.readthedocs.io/en/latest/reference-core.html#cancellation-semantics
 Streaming
 *********
 By now you've figured out that ``tractor`` lets you spawn process based
 *actors* that can invoke cross-process (async) functions and all with
 structured concurrency built in. But the **real cool stuff** is the
 native support for cross-process *streaming*.
 Asynchronous generators
 +++++++++++++++++++++++
 The default streaming function is simply an async generator definition.
 Every value *yielded* from the generator is delivered to the calling
 portal exactly like if you had invoked the function in-process meaning
 you can ``async for`` to receive each value on the calling side.
 As an example here's a parent actor that streams for 1 second from a
 spawned subactor:
 .. literalinclude:: ../examples/asynchronous_generators.py
 By default async generator functions are treated as inter-actor
 *streams* when invoked via a portal (how else could you really interface
 with them anyway) so no special syntax to denote the streaming *service*
 is necessary.
 Channels and Contexts
 +++++++++++++++++++++
 If you aren't fond of having to write an async generator to stream data
 between actors (or need something more flexible) you can instead use
 a ``Context``. A context wraps an actor-local spawned task and
 a ``Channel`` so that tasks executing across multiple processes can
 stream data to one another using a low level, request oriented API.
 A ``Channel`` wraps an underlying *transport* and *interchange* format
 to enable *inter-actor-communication*. In its present state ``tractor``
 uses TCP and msgpack_.
 As an example if you wanted to create a streaming server without writing
 an async generator that *yields* values you instead define a decorated
 async function:
 .. code:: python
   @tractor.stream
   async def streamer(ctx: tractor.Context, rate: int = 2) -> None:
      """A simple web response streaming server.
      """
      while True:
         val = await web_request('http://data.feed.com')
         # this is the same as ``yield`` in the async gen case
         await ctx.send_yield(val)
         await trio.sleep(1 / rate)
 You must decorate the function with ``@tractor.stream`` and declare
 a ``ctx`` argument as the first in your function signature and then
 ``tractor`` will treat the async function like an async generator - as
 a stream from the calling/client side.
 This turns out to be handy particularly if you have multiple tasks
 pushing responses concurrently:
 .. code:: python
   async def streamer(
      ctx: tractor.Context,
      rate: int = 2
   ) -> None:
      """A simple web response streaming server.
      """
      while True:
         val = await web_request(url)
         # this is the same as ``yield`` in the async gen case
         await ctx.send_yield(val)
         await trio.sleep(1 / rate)
   @tractor.stream
   async def stream_multiple_sources(
      ctx: tractor.Context,
      sources: List[str]
   ) -> None:
      async with trio.open_nursery() as n:
         for url in sources:
            n.start_soon(streamer, ctx, url)
 The context notion comes from the context_ in nanomsg_.
 .. _context: https://nanomsg.github.io/nng/man/tip/nng_ctx.5
 .. _msgpack: https://en.wikipedia.org/wiki/MessagePack
 A full fledged streaming service
 ++++++++++++++++++++++++++++++++
 Alright, let's get fancy.
 Say you wanted to spawn two actors which each pull data feeds from
 two different sources (and wanted this work spread across 2 cpus).
 You also want to aggregate these feeds, do some processing on them and then
 deliver the final result stream to a client (or in this case parent) actor
 and print the results to your screen:
 .. literalinclude:: ../examples/full_fledged_streaming_service.py
 Here there's four actors running in separate processes (using all the
 cores on you machine). Two are streaming by *yielding* values from the
 ``stream_data()`` async generator, one is aggregating values from
 those two in ``aggregate()`` (also an async generator) and shipping the
 single stream of unique values up the parent actor (the ``'MainProcess'``
 as ``multiprocessing`` calls it) which is running ``main()``. 
 .. _future: https://en.wikipedia.org/wiki/Futures_and_promises
 .. _borrowed:
    https://trio.readthedocs.io/en/latest/reference-core.html#getting-back-into-the-trio-thread-from-another-thread
 .. _asynchronous generators: https://www.python.org/dev/peps/pep-0525/
 .. _remote function execution: https://codespeak.net/execnet/example/test_info.html#remote-exec-a-function-avoiding-inlined-source-part-i
 Actor local variables
 *********************
 Although ``tractor`` uses a *shared-nothing* architecture between processes
 you can of course share state between tasks running *within* an actor.
 ``trio`` tasks spawned via multiple RPC calls to an actor can access global
 state using the per actor ``statespace`` dictionary:
 .. code:: python
        statespace = {'doggy': 10}
        def check_statespace():
            # Remember this runs in a new process so no changes
            # will propagate back to the parent actor
            assert tractor.current_actor().statespace == statespace
        async def main():
            async with tractor.open_nursery() as n:
                await n.run_in_actor(
                    'checker',
                    check_statespace,
                    statespace=statespace
                )
 Of course you don't have to use the ``statespace`` variable (it's mostly
 a convenience for passing simple data to newly spawned actors); building
 out a state sharing system per-actor is totally up to you.
 Service Discovery
 *****************
 Though it will be built out much more in the near future, ``tractor``
 currently keeps track of actors by ``(name: str, id: str)`` using a
 special actor called the *arbiter*. Currently the *arbiter* must exist
 on a host (or it will be created if one can't be found) and keeps a
 simple ``dict`` of actor names to sockets for discovery by other actors.
 Obviously this can be made more sophisticated (help me with it!) but for
 now it does the trick.
 To find the arbiter from the current actor use the ``get_arbiter()`` function and to
 find an actor's socket address by name use the ``find_actor()`` function:
 .. literalinclude:: ../examples/service_discovery.py
 The ``name`` value you should pass to ``find_actor()`` is the one you passed as the
 *first* argument to either ``tractor.run()`` or ``ActorNursery.start_actor()``.
 Running actors standalone
 *************************
 You don't have to spawn any actors using ``open_nursery()`` if you just
 want to run a single actor that connects to an existing cluster.
 All the comms and arbiter registration stuff still works. This can
 somtimes turn out being handy when debugging mult-process apps when you
 need to hop into a debugger. You just need to pass the existing
 *arbiter*'s socket address you'd like to connect to:
 .. code:: python
    tractor.run(main, arbiter_addr=('192.168.0.10', 1616))
 Choosing a process spawning backend
 ***********************************
 ``tractor`` is architected to support multiple actor (sub-process)
 spawning backends. Specific defaults are chosen based on your system
 but you can also explicitly select a backend of choice at startup
 via a ``start_method`` kwarg to ``tractor.run()``.
 Currently the options available are:
 - ``trio_run_in_process``: a ``trio``-native spawner from the `Ethereum community`_
 - ``spawn``: one of the stdlib's ``multiprocessing`` `start methods`_
 - ``forkserver``: a faster ``multiprocessing`` variant that is Unix only
 .. _start methods: https://docs.python.org/3.8/library/multiprocessing.html#contexts-and-start-methods
 .. _Ethereum community : https://github.com/ethereum/trio-run-in-process
 ``trio-run-in-process``
 +++++++++++++++++++++++
 `trio-run-in-process`_ is a young "pure ``trio``" process spawner
 which utilizes the native `trio subprocess APIs`_. It has shown great
 reliability under testing for predictable teardown when launching
 recursive pools of actors (multiple nurseries deep) and as such has been
 chosen as the default backend on \*nix systems.
 .. _trio-run-in-process: https://github.com/ethereum/trio-run-in-process
 .. _trio subprocess APIs : https://trio.readthedocs.io/en/stable/reference-io.html#spawning-subprocesses
 ``multiprocessing``
 +++++++++++++++++++
 There is support for the stdlib's ``multiprocessing`` `start methods`_.
 Note that on Windows *spawn* it the only supported method and on \*nix
 systems *forkserver* is the best method for speed but has the caveat
 that it will break easily (hangs due to broken pipes) if spawning actors
 using nested nurseries.
 In general, the ``multiprocessing`` backend **has not proven reliable**
 for handling errors from actors more then 2 nurseries *deep* (see `#89`_).
 If you for some reason need this consider sticking with alternative
 backends.
 .. _#89: https://github.com/goodboy/tractor/issues/89
 .. _windowsgotchas:
 Windows "gotchas"
 ^^^^^^^^^^^^^^^^^
 On Windows (which requires the use of the stdlib's `multiprocessing`
 package) there are some gotchas. Namely, the need for calling
 `freeze_support()`_ inside the ``__main__`` context.  Additionally you
 may need place you `tractor` program entry point in a seperate
 `__main__.py` module in your package in order to avoid an error like the
 following ::
    Traceback (most recent call last):
      File "C:\ProgramData\Miniconda3\envs\tractor19030601\lib\site-packages\tractor\_actor.py", line 234, in _get_rpc_func
        return getattr(self._mods[ns], funcname)
    KeyError: '__mp_main__'
 To avoid this, the following is the **only code** that should be in your
 main python module of the program:
 .. code:: python
    # application/__main__.py
    import tractor
    import multiprocessing
    from . import tractor_app
    if __name__ == '__main__':
        multiprocessing.freeze_support()
        tractor.run(tractor_app.main)
 And execute as::
    python -m application
 As an example we use the following code to test all documented examples
 in the test suite on windows:
 .. literalinclude:: ../examples/__main__.py
 See `#61`_ and `#79`_ for further details.
 .. _freeze_support(): https://docs.python.org/3/library/multiprocessing.html#multiprocessing.freeze_support
 .. _#61: https://github.com/goodboy/tractor/pull/61#issuecomment-470053512
 .. _#79: https://github.com/goodboy/tractor/pull/79
 Enabling logging
 ****************
 Considering how complicated distributed software can become it helps to know
 what exactly it's doing (even at the lowest levels). Luckily ``tractor`` has
 tons of logging throughout the core. ``tractor`` isn't opinionated on
 how you use this information and users are expected to consume log messages in
 whichever way is appropriate for the system at hand. That being said, when hacking
 on ``tractor`` there is a prettified console formatter which you can enable to
 see what the heck is going on. Just put the following somewhere in your code:
 .. code:: python
    from tractor.log import get_console_log
    log = get_console_log('trace')
 What the future holds
 ---------------------
 Stuff I'd like to see ``tractor`` do real soon:
 - TLS_, duh.
 - erlang-like supervisors_
 - native support for `nanomsg`_ as a channel transport
 - native `gossip protocol`_ support for service discovery and arbiter election
 - a distributed log ledger for tracking cluster behaviour
 - a slick multi-process aware debugger much like in celery_
  but with better `pdb++`_ support
 - an extensive `chaos engineering`_ test suite
 - support for reactive programming primitives and native support for asyncitertools_ like libs
 - introduction of a `capability-based security`_ model
 .. _TLS: https://trio.readthedocs.io/en/latest/reference-io.html#ssl-tls-support
 .. _supervisors: https://github.com/goodboy/tractor/issues/22
 .. _nanomsg: https://nanomsg.github.io/nng/index.html
 .. _gossip protocol: https://en.wikipedia.org/wiki/Gossip_protocol
 .. _celery: http://docs.celeryproject.org/en/latest/userguide/debugging.html
 .. _asyncitertools: https://github.com/vodik/asyncitertools
 .. _pdb++: https://github.com/antocuni/pdb
 .. _capability-based security: https://en.wikipedia.org/wiki/Capability-based_security
--- a/examples/a_trynamic_first_scene.py
+++ b/examples/a_trynamic_first_scene.py
@ -1,4 +1,3 @@
 import platform
 import tractor
 _this_module = __name__
--- a/examples/actor_spawning_and_causality_with_daemon.py
+++ b/examples/actor_spawning_and_causality_with_daemon.py
@ -0,0 +1,33 @@
 import tractor
 def movie_theatre_question():
    """A question asked in a dark theatre, in a tangent
    (errr, I mean different) process.
    """
    return 'have you ever seen a portal?'
 async def main():
    """The main ``tractor`` routine.
    """
    async with tractor.open_nursery() as n:
        portal = await n.start_actor(
            'frank',
            # enable the actor to run funcs from this current module
            rpc_module_paths=[__name__],
        )
        print(await portal.run(__name__, 'movie_theatre_question'))
        # call the subactor a 2nd time
        print(await portal.run(__name__, 'movie_theatre_question'))
        # the async with will block here indefinitely waiting
        # for our actor "frank" to complete, but since it's an
        # "outlive_main" actor it will never end until cancelled
        await portal.cancel_actor()
 if __name__ == '__main__':
    tractor.run(main)
--- a/examples/remote_error_propagation.py
+++ b/examples/remote_error_propagation.py
@ -0,0 +1,29 @@
 import tractor
 async def assert_err():
    assert 0
 async def main():
    async with tractor.open_nursery() as n:
        real_actors = []
        for i in range(3):
            real_actors.append(await n.start_actor(
                f'actor_{i}',
                rpc_module_paths=[__name__],
            ))
        # start one actor that will fail immediately
        await n.run_in_actor('extra', assert_err)
    # should error here with a ``RemoteActorError`` containing
    # an ``AssertionError`` and all the other actors have been cancelled
 if __name__ == '__main__':
    try:
        # also raises
        tractor.run(main)
    except tractor.RemoteActorError:
        print("Look Maa that actor failed hard, hehhh!")
--- a/tests/test_docs_examples.py
+++ b/tests/test_docs_examples.py
@ -2,6 +2,7 @@
 Let's make sure them docs work yah?
 """
 from contextlib import contextmanager
 import itertools
 import os
 import sys
 import subprocess
@ -42,9 +43,13 @@ def run_example_in_subproc(loglevel, testdir, arb_addr):
                os.path.join(str(testdir), '__main__.py')
            )
-            # drop the ``if __name__ == '__main__'`` guard from the *NIX
+            # drop the ``if __name__ == '__main__'`` guard onwards from
-            # version of each script
+            # the *NIX version of each script
-            script_code = '\n'.join(script_code.splitlines()[:-4])
+            windows_script_lines = itertools.takewhile(
                lambda line: "if __name__ ==" not in line,
                script_code.splitlines()
            )
            script_code = '\n'.join(windows_script_lines)
            script_file = testdir.makefile('.py', script_code)
            # without this, tests hang on windows forever
@ -99,7 +104,9 @@ def test_example(run_example_in_subproc, example_script):
            err, _ = proc.stderr.read(), proc.stdout.read()
            # if we get some gnarly output let's aggregate and raise
-            if err and b'Error' in err:
+            errmsg = err.decode()
-                raise Exception(err.decode())
+            errlines = errmsg.splitlines()
            if err and 'Error' in errlines[-1]:
                raise Exception(errmsg)
            assert proc.returncode == 0