ddbf55768f | ||
---|---|---|
tests | ||
tractor | ||
.gitignore | ||
.travis.yml | ||
LICENSE | ||
README.rst | ||
mypy.ini | ||
requirements-test.txt | ||
setup.py |
README.rst
tractor
An async-native "actor model" built on trio and multiprocessing.
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
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.
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.
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!
Install
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:
import tractor
from functools import partial
= __name__
_this_module = 'Hi my name is {}'
the_line
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!")
= await n.run_in_actor(
donny 'donny',
say_hello,# arguments are always named
='gretchen',
other_actor
)= await n.run_in_actor(
gretchen 'gretchen',
say_hello,='donny',
other_actor
)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:
import tractor
def cellar_door():
return "Dang that's beautiful"
async def main():
"""The main ``tractor`` routine.
"""
async with tractor.open_nursery() as n:
= await n.run_in_actor('some_linguist', cellar_door)
portal
# 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 anActorNusery
and is told to run a single function:cellar_door()
- a
portal
instance (we'll get to what it is shortly) returned fromnursery.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 executescellar_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). Thewith tractor.open_nursery()
exits only once all actors' main function/task complete (just like the nursery intrio
) - actors can be spawned to live forever using the
start_actor()
method and act like an RPC daemon that runs indefinitely (thewith tractor.open_nursery()
won't exit) until cancelled
Here is a similar example using the latter method:
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:
= await n.start_actor(
portal 'frank',
# enable the actor to run funcs from this current module
=[__name__],
rpc_module_paths
)
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).
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
.
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.
async def assert_err():
assert 0
async def main():
async with tractor.open_nursery() as n:
= []
real_actors for i in range(3):
await n.start_actor(
real_actors.append(f'actor_{i}',
=[__name__],
rpc_module_paths
))
# 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.
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 streaming pipeline.
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:
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:
= await n.start_actor(
portal f'donny',
=[__name__],
rpc_module_paths
)
# 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:
@tractor.stream
async def streamer(ctx: tractor.Context, rate: int = 2) -> None:
"""A simple web response streaming server.
"""
while True:
= await web_request('http://data.feed.com')
val
# 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:
async def streamer(
ctx: tractor.Context,int = 2
rate: -> None:
) """A simple web response streaming server.
"""
while True:
= await web_request(url)
val
# 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,str]
sources: List[-> 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.
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:
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
= await nursery.start_actor(
portal =f'streamer_{i}',
name=[__name__],
rpc_module_paths
)
portals.append(portal)
= trio.open_memory_channel(500)
send_chan, recv_chan
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()
= set()
unique_vals 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:
= int(1e3)
seed import time
= time.time()
pre_start
= await nursery.run_in_actor(
portal 'aggregator',
aggregate,=seed,
seed
)
= time.time()
start # 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
= tractor.run(main, arbiter_addr=('127.0.0.1', 1616)) final_stream
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()
.
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:
= {'doggy': 10}
statespace
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:
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}")
'some_actor_name') tractor.run(main,
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:
=('192.168.0.10', 1616)) tractor.run(main, arbiter_addr
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.
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. 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:
# 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.
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:
from tractor.log import get_console_log
= get_console_log('trace') log
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
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!