uActor multiprocessing microframework

uActor is a multiprocessing actor library for Python. It designed to power concurrent applications via implementing the actor model with a simple yet powerful API.

It is distributed as a single file module and has no dependencies other than the Python Standard Library.

import os
import uactor

class Actor(uactor.Actor):
    def hello(self):
        return f'Hello from subprocess {os.getpid()}!'

print(f'Hello from process {os.getpid()}!')
# Hello from process 22682!

print(Actor().hello())
# Hello from subprocess 22683!

Quickstart

Installation

You can install it using pip.

pip install uactor

Or alternatively by including that single uactor.py file into your project.

Your first actor

With uActor, actors are defined as classes inheriting from uactor.Actor, with some special attributes we'll cover later.

import uactor

class MyActor(uactor.Actor):
    def my_method(self):
        return True

Actors are initialized inside dedicated processes returning proxies.

my_actor_proxy = MyActor()
my_actor_proxy.my_method()

Once you're done with your actor, it is always a good idea to finalize its process with uactor.Actor.shutdown method.

my_actor_proxy.shutdown()

Alternatively, uactor.Actor instances can be used as context managers, so the actor process will be finalized once we're done with it.

with MyActor() as my_actor_proxy:
    my_actor_proxy.my_method()

Returning result proxies

We can return proxies to objects instead of the objects themselves, while keeping them sound and safe on our actor process, this can be achieved pretty easily.

To specify a proxy will be used to serialize a method result, we can add both method name and proxy typid to uactor.Actor._method_to_typeid_ special class attribute.

import uactor

class MyActor(uactor.Actor):

    _method_to_typeid_ = {'my_method': 'dict'}

    def __init__(self):
        self.my_data = {}

    def my_method(self):
        return self.my_data

Or, alternatively, we can explicitly create a proxy for our object, using uactor.proxy utility function.

import uactor

class MyActor(uactor.Actor):
    def __init__(self):
        self.my_data = {}

    def my_method(self):
        return uactor.proxy(self.my_data, 'dict')

Becoming asynchronous (and concurrent)

Actors are fully synchronous by default, but that's usually not very useful, the following example will show you how to return asynchronous results from the actor.

import time
import multiprocessing.pool
import uactor

class MyActor(uactor.Actor):

    _method_to_typeid_ = {'my_method': 'AsyncResult'}

    def __init__(self):
        self.threadpool = multiprocessing.pool.ThreadPool()

    def my_method(self):
        return self.threadpool.apply_async(time.sleep, [10])  # wait 10s

with MyActor() as my_actor:

    # will return immediately
    result = my_actor.my_method()

    # will take 10 seconds
    result.wait()

Based on this, we can now run code concurrently running on the same actor.

with MyActor() as my_actor:

    # these will return immediately
    result_a = my_actor.my_method()
    result_b = my_actor.my_method()

    # these all will take 10 seconds in total
    result_a.wait()
    result_b.wait()

And even from different actor processes.

actor_a = MyActor()
actor_b = MyActor()
with actor_a, actor_b:

    # these will return immediately
    result_a = actor_a.my_method()
    result_b = actor_b.my_method()

    # these all will take 10 seconds in total
    result_a.wait()
    result_b.wait()

Next steps

You can take a look at our code examples.

• The basics:

  • Actor inheritance.
  • Actor lifetime.
  • Method callbacls.
  • Result proxies.

• Advanced patterns:

  • Actor pool.

uActor design

With the constant rise in CPU core number, highly threaded python applications are still very rare (except for distributed processing frameworks like celery), this is due a few reasons:

• threading cannot use multiple cores because Python Global Interpreter Lock forces the interpreter to run on a single core.
• multiprocessing is meant to overcome threading limitations by using processes, but exposes a pretty convoluted API as processes are way more complex, while exposing many quirks and limitations.

uActor allows implementing multi-core software as easy as just declaring and instancing classes, following the actor model, by thinly wrapping (<150 LLOC) standard SyncManager to circumvent most of multiprocessing complexity and some of its flaws.

uActor API is designed to be both minimalistic and intuitive, but still few compromises had to be taken to leverage on SyncManager as much as possible, as it is both somewhat actively maintained and already available as part of the Python Standard Library.

Actors

Just like the actor programming model revolves around the actor entity, uActor features the uactor.Actor base class.

When an actor class is declared, just because either it inherits from uactor.Actor or uses the uactor.ActorMeta metaclass, its Actor.proxy_class gets also inherited and updated with uactor.ProxyMethod callable descriptors, following the exposed actor interface, either explicitly defined via actor Actor._exposed_ property or implicitly by actor public methods.

Actor.manager_class is also inherited allowing registering specific proxies for the current actor, including those defined in Actor._proxies_ mapping (key used as a typeid) along with 'actor' and 'auto' special proxies.

Keep in mind the default Actor.manager_class, uactor.ActorManager, already includes all proxies from SyncManager (including the internal AsyncResult and Iterator) which are all available to the actor and ready use.

As a reference, these are all the available uactor.Actor configuration class attributes:

• manager_class: manager base class (defaults to parent's one, up to uactor.ActorManager).
• proxy_class: actor proxy class (defaults to parent's one, up to uactor.ActorProxy).
• _options_: option mapping will be passed to manager_class.
• _exposed_: list of explicitly exposed methods will be made available by proxy_class, if None or undefined then all public methods will be exposed.
• _proxies_: mapping (typeid, proxy class) of additional proxies will be registered in the manager_class and, thus, will be available to be returned by the actor.
• _method_to_typeid_: mapping (method name, typeid) defining which method return values will be wrapped into proxies when invoked from proxy_class.

When an uactor.Actor class is instantiated, a new process is spawned and a uactor.Actor.proxy_class instance is returned (as the real actor will be kept safe in said process), transparently exposing a message-based interface.

import os
import uactor

class Actor(uactor.Actor):
    def getpid(self):
        return os.getpid()

actor = Actor()
print('My process id is', os.getpid())
# My process id is 153333
print('Actor process id is ', actor.getpid())
# Actor process id is 153344

Proxies

Proxies are objects communicating with the actor process, while exposing a similar interface, in the most transparent way possible.

It is implied most calls made to a proxy will result on inter-process communication and serialization overhead.

To alleviate the serialization cost, actor methods can also return proxies, so the real data is kept well inside the actor process boundaries, which can be efficiently shared between processes with very little serialization cost.

Actors can define which proxy will be used to expose the result of certain methods by defining that in their Actor._method_to_typeid_ property.

import uactor

class Actor(uactor.Actor):
    _method_to_typeid_ = {'get_mapping': 'dict'}
    ...
    def get_data(self):
        return self.my_data_dict

Or, alternatively, using the uactor.proxy function, receiving both value and a proxy typeid (as in SyncManager semantics).

import uactor

class Actor(uactor.Actor):
    ...
    def get_data(self):
        return uactor.proxy(self.my_data_dict, 'dict')

Keep in mind uactor.proxy can only be called from inside the actor process (it will raise uactor.ProxyError otherwise), as proxies can only be created from there.

You can define your own proxy classes (following BaseProxy semantics), and they will be made available in an actor by including it on the Actor._proxies_ mapping (along its typeid).

import uactor

class MyDataProxy(uactor.BaseProxy):
    def my_method(self):
        return self._callmethod('my_method')

    my_other_method = uactor.ProxyMethod('my_other_method')

class Actor(uactor.Actor):
    _proxies_ = {'MyDataProxy': MyDataProxy}
    _method_to_typeid_ = {'get_data': 'MyDataProxy'}
    ...

In addition to all proxies imported from both SyncManager (including internal ones as Iterator and AsyncResult) and Actor._proxies_, we always register these ones:

• actor: proxy to the current process actor.
• auto: dynamic proxy based based on the wrapped object.

You can list all available proxies (which can vary between python versions) by calling ActorManager.typeids():

import uactor

print(uactor.Actor.manager_class.typeids())
# ('Queue', 'JoinableQueue', 'Event', ..., 'auto', 'actor')

print(uactor.ActorManager.typeids())
# ('Queue', 'JoinableQueue', 'Event', ..., 'auto')

Contributing

uActor is deliberately very small in scope, while still aiming to be easily extended, so extra functionality might be implemented via external means.

If you find any bug or a possible improvement to existing functionality it will likely be accepted so feel free to contribute.

In the other hand, if you feel a feature is missing, you can create another library and use this as dependency of yours, or even forking this project.

License

MIT License.

See LICENSE file.