![ ](./images/uactor-title.96.png)

# uActor: Process Actor Model

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

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

Documentation: [uactor.readthedocs.io](https://uactor.readthedocs.io)

Usage:

```python
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`.
```sh
pip install uactor
```

Or alternatively by including our 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.

```python
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.

```python
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.

```python
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.

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

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.

```python
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.

```python
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.

```python
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.

```python
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 now we can to parallelize workloads across different actor processes.

```python
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](https://uactor.readthedocs.io) or
take a look at our code examples.

* The basics:
  * [Actor inheritance](./examples/inheritance.md).
  * [Actor lifetime](./examples/lifetime.md).
  * [Result proxies](./examples/result_proxies.md).
  * [Method callbacks](./examples/callbacks.md).

* Advanced patterns:
  * [Sticky processes](./examples/stick.md).
  * [Actor pool](./examples/pool.md).
  * [Networking](./examples/networking.md).

## 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][celery]), this is due a few reasons:
* [threading][threading] cannot use multiple cores because
  [Python Global Interpreter Lock][gil] forces the interpreter to run on a
  single core.
* [multiprocessing][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][actor-model], by thinly
wrapping the standard [SyncManager][syncmanager] to circumvent most o
 [multiprocessing][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][syncmanager]
as much as possible, as it is both somewhat actively maintained and
already available as part of the [Python Standard Library][stdlib].

### 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, 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][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.

```python
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, 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.

```python
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][syncmanager] semantics).

```python
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][baseproxy]
semantics), and they will be made available in an actor by including it on
the `Actor._proxies_` mapping (along its typeid).

```python
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][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()`:

```python
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.

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.

## License

Copyright (c) 2020, Felipe A Hernandez.

MIT License (see [LICENSE](./LICENSE)).

[multiprocessing]: https://docs.python.org/3/library/multiprocessing.html
[syncmanager]: https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager
[baseproxy]: https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy
[threading]: https://docs.python.org/3/library/threading.html
[context-managers]: https://docs.python.org/3/reference/datamodel.html#context-managers
[gil]: https://docs.python.org/3/c-api/init.html#thread-state-and-the-global-interpreter-lock
[actor-model]: https://en.wikipedia.org/wiki/Actor_model
[ipc]:https://en.wikipedia.org/wiki/Inter-process_communication
[stdlib]: https://docs.python.org/3/library/index.html
[celery]: https://celeryproject.org