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uActor: Process Actor Model

Project description

uActor: Process Actor Model

uActor is a process actor library for Python with a simple yet powerful API, implementing the actor model atop multiprocessing, with no dependencies other than the Python Standard Library.

  • Simple: Minimalistic API, no boilerplate required.
  • Flexible: Trivial to integrate, meant to be extended.
  • Concurrent: Share workload over CPU cores, and across the network.



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!

# Hello from subprocess 22683!



You can install it using pip.

pip install uactor

Or alternatively by including our single 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

During instantiation, every actor is initialized on its own dedicated process, returning a proxy.

my_actor_proxy = MyActor()

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


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:

Actor processes will be also finished when every proxy gets garbage-collected on its parent process.

Returning result proxies

Actor methods can return proxies instead of actual objects, keeping them sound and safe on our actor process.

To specify which proxy will be returned from an specific method, we can add both method name and proxy typeid 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')

There is a limitation with proxies, applying two different proxies to the same object will raise an exception, this is likely to change in the future.

Becoming asynchronous (and concurrent)

Actor methods are fully synchronous by default, which is usually not very useful on distributed software, 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

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

And now we can to parallelize workloads across different actor processes.

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

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

    result_a.wait() # this will take ~10s to complete
    result_b.wait() # this will be immediate (we already waited 10s)

Next steps

You can dive into our documentation or take a look at our code examples.

uActor design

With the constant rise in CPU core count, highly threaded python applications are still pretty 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, meant to overcome threading limitations by using processes, exposes a pretty convoluted API as processes are way more complex, exposing many quirks and limitations.

uActor allows implementing distributed software as easy as just declaring and instancing classes, following the actor model, by thinly wrapping the standard SyncManager to circumvent most o 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.


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

When an actor class is declared, by inheriting from uactor.Actor, its Actor.proxy_class gets also inherited and updated to mirror the actor interface, either following the explicit list of properties defined at Actor._exposed_ or implicitly by actor public methods.

Actor.manager_class is also inherited registering actor specific proxies 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 every proxy from SyncManager (including the internal AsyncResult and Iterator) which are all available to the actor and ready use (you can call Actor.manager_class.typeids() to list them all).

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 are objects communicating with the actor process, 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

# ('Queue', 'JoinableQueue', 'Event', ..., 'auto', 'actor')

# ('Queue', 'JoinableQueue', 'Event', ..., 'auto')


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.

If, in the other hand, you feel a feature is missing, you can either create another library using uActor as dependency or fork this project.


Copyright (c) 2020, Felipe A Hernandez.

MIT License (see LICENSE).

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