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tractor
=======
An async-native "`actor model`_" built on trio_ and multiprocessing_.
|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://docs.python.org/3/library/multiprocessing.html
.. _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
.. _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.
``tractor`` lets you spawn ``trio`` *"actors"*: processes which each run a ``trio`` scheduler and task
tree (also known as an `async sandwich`_). *Actors* communicate by exchanging asynchronous messages_ over
channels_ 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.
``tractor`` is an actor-model-*like* system in the sense that it adheres to the `3 axioms`_ but not does
not (yet) fufill all "unrequirements_" in practice. The API and design takes inspiration from pulsar_ and
execnet_ but attempts to be more focussed on sophistication of the lower level distributed architecture as
well as have first class support for streaming using `async generators`_.
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/
.. _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/
.. 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 / caller
- verbatim support for ``trio``'s cancellation_ system
- `shared nothing architecture`_
- no use of *proxy* objects to wrap RPC calls
- 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
.. _pulsar: http://quantmind.github.io/pulsar/design.html
.. _execnet: https://codespeak.net/execnet/
Install
-------
No PyPi release yet!
::
pip install git+git://github.com/tgoodlet/tractor.git
Windows "gotchas"
*****************
`tractor` internally uses the stdlib's `multiprocessing` package which
*can* have some gotchas on Windows. Namely, the need for calling
`freeze_support()`_ inside the ``__main__`` context. See `#61`_ for the
deats.
.. _freeze_support(): https://docs.python.org/3/library/multiprocessing.html#multiprocessing.freeze_support
.. _#61: https://github.com/tgoodlet/tractor/pull/61#issuecomment-470053512
Examples
--------
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
from functools import partial
_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...")
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())
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
Async 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
from the way execnet_ does `remote function execution`_ but without
the client code (necessarily) having to worry 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 processing pipeline.
As an example here's an actor that streams for 1 second from a remote async
generator function running in a separate actor:
.. 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)
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
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
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.
How do actors find each other (a poor man's *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()``.
Streaming using channels and contexts
*************************************
``Channel`` is the API which wraps an underlying *transport* and *interchange*
format to enable *inter-actor-communication*. In its present state ``tractor``
uses TCP and msgpack_.
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.
As an example if you wanted to create a streaming server without writing
an async generator that *yields* values you instead define an async
function:
.. code:: python
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)
All that's required is declaring a ``ctx`` argument name somewhere in
your function signature and ``tractor`` will treat the async function
like an async generator - as a streaming function from the client side.
This turns out to be handy particularly if you have
multiple tasks streaming 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)
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
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 ``multiprocessing`` *start method*
*********************************************
``tractor`` supports selection of the `multiprocessing start method`_ via
a ``start_method`` kwarg to ``tractor.run()``. Note that on Windows
*spawn* it the only supported method and on nix systems *forkserver* is
selected by default for speed.
.. _multiprocessing start method: https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
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:
- 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
.. _supervisors: https://github.com/tgoodlet/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
``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
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