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Remove duplicate docs from readme

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Tyler Goodlet 2020-02-09 23:52:23 -05:00
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@ -8,17 +8,6 @@ An async-native "`actor model`_" built on trio_ and multiprocessing_.
.. |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://en.wikipedia.org/wiki/Multiprocessing
.. _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
@ -31,35 +20,24 @@ 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`_.
.. _actor model: https://en.wikipedia.org/wiki/Actor_model
.. _trio: https://github.com/python-trio/trio
.. _multiprocessing: https://en.wikipedia.org/wiki/Multiprocessing
.. _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/
.. _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
@ -94,742 +72,6 @@ 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:
.. code:: python
import tractor
_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...")
if __name__ == '__main__':
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())
if __name__ == '__main__':
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
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
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
`remote function execution`_ but without the client code being
concerned 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.
.. _exactly like trio: https://trio.readthedocs.io/en/latest/reference-core.html#cancellation-semantics
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:
.. 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)
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:
.. code:: python
@tractor.stream
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)
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:
.. 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)
@tractor.stream
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
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
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.
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()``.
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 process spawning backend
***********************************
``tractor`` is architected to support multiple actor (sub-process)
spawning backends. Specific defaults are chosen based on your system
but you can also explicitly select a backend of choice at startup
via a ``start_method`` kwarg to ``tractor.run()``.
Currently the options available are:
- ``trio_run_in_process``: a ``trio``-native spawner from the `Ethereum community`_
- ``spawn``: one of the stdlib's ``multiprocessing`` `start methods`_
- ``forkserver``: a faster ``multiprocessing`` variant that is Unix only
.. _start methods: https://docs.python.org/3.8/library/multiprocessing.html#contexts-and-start-methods
.. _Ethereum community : https://github.com/ethereum/trio-run-in-process
``trio-run-in-process``
+++++++++++++++++++++++
`trio-run-in-process`_ is a young "pure ``trio``" process spawner
which utilizes the native `trio subprocess APIs`_. It has shown great
reliability under testing for predictable teardown when launching
recursive pools of actors (multiple nurseries deep) and as such has been
chosen as the default backend on \*nix systems.
.. _trio-run-in-process: https://github.com/ethereum/trio-run-in-process
.. _trio subprocess APIs : https://trio.readthedocs.io/en/stable/reference-io.html#spawning-subprocesses
``multiprocessing``
+++++++++++++++++++
There is support for the stdlib's ``multiprocessing`` `start methods`_.
Note that on Windows *spawn* it the only supported method and on \*nix
systems *forkserver* is the best method for speed but has the caveat
that it will break easily (hangs due to broken pipes) if spawning actors
using nested nurseries.
In general, the ``multiprocessing`` backend **has not proven reliable**
for handling errors from actors more then 2 nurseries *deep* (see `#89`_).
If you for some reason need this consider sticking with alternative
backends.
.. _#89: https://github.com/goodboy/tractor/issues/89
Windows "gotchas"
^^^^^^^^^^^^^^^^^
On Windows (which requires the use of the stdlib's `multiprocessing`
package) there are some gotchas. 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:
.. code:: python
# 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.
.. _freeze_support(): https://docs.python.org/3/library/multiprocessing.html#multiprocessing.freeze_support
.. _#61: https://github.com/goodboy/tractor/pull/61#issuecomment-470053512
.. _#79: https://github.com/goodboy/tractor/pull/79
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:
- 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
.. _TLS: https://trio.readthedocs.io/en/latest/reference-io.html#ssl-tls-support
.. _supervisors: https://github.com/goodboy/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