|logo| ``tractor``: distributed structurred concurrency

``tractor`` is a `structured concurrency`_ (SC), multi-processing_ runtime built on trio_.

Fundamentally, ``tractor`` provides parallelism via
``trio``-"*actors*": independent Python **processes** (i.e.
*non-shared-memory threads*) which can schedule ``trio`` tasks whilst
maintaining *end-to-end SC* inside a *distributed supervision tree*.

Cross-process (and thus cross-host) SC is accomplished through the
combined use of our,

- "actor nurseries_" which provide for spawning multiple, and
  possibly nested, Python processes each running a ``trio`` scheduled
  runtime - a call to ``trio.run()``,
- an "SC-transitive supervision protocol" enforced as an
  IPC-message-spec encapsulating all RPC-dialogs.

We believe the system adheres to the `3 axioms`_ of an "`actor model`_"
but likely **does not** look like what **you** probably *think* an "actor
model" looks like, and that's **intentional**.


Where do i start!?
------------------
The first step to grok ``tractor`` is to get an intermediate
knowledge of ``trio`` and **structured concurrency** B)

Some great places to start are,

- the seminal `blog post`_
- obviously the `trio docs`_
- wikipedia's nascent SC_ page
- the fancy diagrams @ libdill-docs_


Features
--------
- **It's just** a ``trio`` API!
- *Infinitely nesteable* process trees running embedded ``trio`` tasks.
- Swappable, OS-specific, process spawning via multiple backends.
- Modular IPC stack, allowing for custom interchange formats (eg.
  as offered from `msgspec`_), varied transport protocols (TCP, RUDP,
  QUIC, wireguard), and OS-env specific higher-perf primitives (UDS,
  shm-ring-buffers).
- Optionally distributed_: all IPC and RPC APIs work over multi-host
  transports the same as local.
- Builtin high-level streaming API that enables your app to easily
  leverage the benefits of a "`cheap or nasty`_" `(un)protocol`_.
- A "native UX" around a multi-process safe debugger REPL using
  `pdbp`_ (a fork & fix of `pdb++`_)
- "Infected ``asyncio``" mode: support for starting an actor's
  runtime as a `guest`_ on the ``asyncio`` loop allowing us to
  provide stringent SC-style ``trio.Task``-supervision around any
  ``asyncio.Task`` spawned via our ``tractor.to_asyncio`` APIs.
- A **very naive** and still very much work-in-progress inter-actor
  `discovery`_ sys with plans to support multiple `modern protocol`_
  approaches.
- Various ``trio`` extension APIs via ``tractor.trionics`` such as,
  - task fan-out `broadcasting`_,
  - multi-task-single-resource-caching and fan-out-to-multi
    ``__aenter__()`` APIs for ``@acm`` functions,
  - (WIP) a ``TaskMngr``: one-cancels-one style nursery supervisor.


Status of `main` / infra
------------------------

- |gh_actions|
- |docs|


Install
-------
``tractor`` is still in a *alpha-near-beta-stage* for many
of its subsystems, however we are very close to having a stable
lowlevel runtime and API.

As such, it's currently recommended that you clone and install the
repo from source::

    pip install git+git://github.com/goodboy/tractor.git


We use the very hip `uv`_ for project mgmt::

    git clone https://github.com/goodboy/tractor.git
    cd tractor
    uv sync --dev
    uv run python examples/rpc_bidir_streaming.py

Consider activating a virtual/project-env before starting to hack on
the code base::

    # you could use plain ol' venvs
    # https://docs.astral.sh/uv/pip/environments/
    uv venv tractor_py313 --python 3.13

    # but @goodboy prefers the more explicit (and shell agnostic)
    # https://docs.astral.sh/uv/configuration/environment/#uv_project_environment
    UV_PROJECT_ENVIRONMENT="tractor_py313

    # hint hint, enter @goodboy's fave shell B)
    uv run --dev xonsh

Alongside all this we ofc offer "releases" on PyPi::

    pip install tractor

Just note that YMMV since the main git branch is often much further
ahead then any latest release.


Example codez
-------------
In ``tractor``'s (very lacking) documention we prefer to point to
example scripts in the repo over duplicating them in docs, but with
that in mind here are some definitive snippets to try and hook you
into digging deeper.


Run a func in a process
***********************
Use ``trio``'s style of focussing on *tasks as functions*:

.. code:: python

    """
    Run with a process monitor from a terminal using::

        $TERM -e watch -n 0.1  "pstree -a $$" \
            & python examples/parallelism/single_func.py \
            && kill $!

    """
    import os

    import tractor
    import trio


    async def burn_cpu():

        pid = os.getpid()

        # burn a core @ ~ 50kHz
        for _ in range(50000):
            await trio.sleep(1/50000/50)

        return os.getpid()


    async def main():

        async with tractor.open_nursery() as n:

            portal = await n.run_in_actor(burn_cpu)

            #  burn rubber in the parent too
            await burn_cpu()

            # wait on result from target function
            pid = await portal.result()

        # end of nursery block
        print(f"Collected subproc {pid}")


    if __name__ == '__main__':
        trio.run(main)


This runs ``burn_cpu()`` in a new process and reaps it on completion
of the nursery block.

If you only need to run a sync function and retreive a single result, you
might want to check out `trio-parallel`_.


Zombie safe: self-destruct a process tree
*****************************************
``tractor`` tries to protect you from zombies, no matter what.

.. code:: python

    """
    Run with a process monitor from a terminal using::

        $TERM -e watch -n 0.1  "pstree -a $$" \
            & python examples/parallelism/we_are_processes.py \
            && kill $!

    """
    from multiprocessing import cpu_count
    import os

    import tractor
    import trio


    async def target():
        print(
            f"Yo, i'm '{tractor.current_actor().name}' "
            f"running in pid {os.getpid()}"
        )

        await trio.sleep_forever()


    async def main():

        async with tractor.open_nursery() as n:

            for i in range(cpu_count()):
                await n.run_in_actor(target, name=f'worker_{i}')

            print('This process tree will self-destruct in 1 sec...')
            await trio.sleep(1)

            # raise an error in root actor/process and trigger
            # reaping of all minions
            raise Exception('Self Destructed')


    if __name__ == '__main__':
        try:
            trio.run(main)
        except Exception:
            print('Zombies Contained')


If you can create zombie child processes (without using a system signal)
it **is a bug**.


"Native" multi-process debugging
********************************
Using the magic of `pdbp`_ and our internal IPC, we've
been able to create a native feeling debugging experience for
any (sub-)process in your ``tractor`` tree.

.. code:: python

    from os import getpid

    import tractor
    import trio


    async def breakpoint_forever():
        "Indefinitely re-enter debugger in child actor."
        while True:
            yield 'yo'
            await tractor.breakpoint()


    async def name_error():
        "Raise a ``NameError``"
        getattr(doggypants)


    async def main():
        """Test breakpoint in a streaming actor.
        """
        async with tractor.open_nursery(
            debug_mode=True,
            loglevel='error',
        ) as n:

            p0 = await n.start_actor('bp_forever', enable_modules=[__name__])
            p1 = await n.start_actor('name_error', enable_modules=[__name__])

            # retreive results
            stream = await p0.run(breakpoint_forever)
            await p1.run(name_error)


    if __name__ == '__main__':
        trio.run(main)


You can run this with::

    >>> python examples/debugging/multi_daemon_subactors.py

And, yes, there's a built-in crash handling mode B)

We're hoping to add a respawn-from-repl system soon!


SC compatible bi-directional streaming
**************************************
Yes, you saw it here first; we provide 2-way streams
with reliable, transitive setup/teardown semantics.

Our nascent api is remniscent of ``trio.Nursery.start()``
style invocation:

.. code:: python

    import trio
    import tractor


    @tractor.context
    async def simple_rpc(

        ctx: tractor.Context,
        data: int,

    ) -> None:
        '''Test a small ping-pong 2-way streaming server.

        '''
        # signal to parent that we're up much like
        # ``trio_typing.TaskStatus.started()``
        await ctx.started(data + 1)

        async with ctx.open_stream() as stream:

            count = 0
            async for msg in stream:

                assert msg == 'ping'
                await stream.send('pong')
                count += 1

            else:
                assert count == 10


    async def main() -> None:

        async with tractor.open_nursery() as n:

            portal = await n.start_actor(
                'rpc_server',
                enable_modules=[__name__],
            )

            # XXX: this syntax requires py3.9
            async with (

                portal.open_context(
                    simple_rpc,
                    data=10,
                ) as (ctx, sent),

                ctx.open_stream() as stream,
            ):

                assert sent == 11

                count = 0
                # receive msgs using async for style
                await stream.send('ping')

                async for msg in stream:
                    assert msg == 'pong'
                    await stream.send('ping')
                    count += 1

                    if count >= 9:
                        break


            # explicitly teardown the daemon-actor
            await portal.cancel_actor()


    if __name__ == '__main__':
        trio.run(main)


See original proposal and discussion in `#53`_ as well
as follow up improvements in `#223`_ that we'd love to
hear your thoughts on!

.. _#53: https://github.com/goodboy/tractor/issues/53
.. _#223: https://github.com/goodboy/tractor/issues/223


Worker poolz are easy peasy
***************************
The initial ask from most new users is *"how do I make a worker
pool thing?"*.

``tractor`` is built to handle any SC (structured concurrent) process
tree you can imagine; a "worker pool" pattern is a trivial special
case.

We have a `full worker pool re-implementation`_ of the std-lib's
``concurrent.futures.ProcessPoolExecutor`` example for reference.

You can run it like so (from this dir) to see the process tree in
real time::

    $TERM -e watch -n 0.1  "pstree -a $$" \
        & python examples/parallelism/concurrent_actors_primes.py \
        && kill $!

This uses no extra threads, fancy semaphores or futures; all we need
is ``tractor``'s IPC!

"Infected ``asyncio``" mode
***************************
Have a bunch of ``asyncio`` code you want to force to be SC at the process level?

Check out our experimental system for `guest`_-mode controlled
``asyncio`` actors:

.. code:: python

    import asyncio
    from statistics import mean
    import time

    import trio
    import tractor


    async def aio_echo_server(
        chan: tractor.to_asyncio.LinkedTaskChannel,
    ) -> None:

        # a first message must be sent **from** this ``asyncio``
        # task or the ``trio`` side will never unblock from
        # ``tractor.to_asyncio.open_channel_from():``
        chan.started_nowait('start')

        while True:
            # echo the msg back
            chan.send_nowait(await chan.get())
            await asyncio.sleep(0)


    @tractor.context
    async def trio_to_aio_echo_server(
        ctx: tractor.Context,
    ):
        # this will block until the ``asyncio`` task sends a "first"
        # message.
        async with tractor.to_asyncio.open_channel_from(
            aio_echo_server,
        ) as (chan, first):

            assert first == 'start'
            await ctx.started(first)

            async with ctx.open_stream() as stream:

                async for msg in stream:
                    await chan.send(msg)

                    out = await chan.receive()
                    # echo back to parent actor-task
                    await stream.send(out)


    async def main():

        async with tractor.open_nursery() as n:
            p = await n.start_actor(
                'aio_server',
                enable_modules=[__name__],
                infect_asyncio=True,
            )
            async with p.open_context(
                trio_to_aio_echo_server,
            ) as (ctx, first):

                assert first == 'start'

                count = 0
                async with ctx.open_stream() as stream:

                    delays = []
                    send = time.time()

                    await stream.send(count)
                    async for msg in stream:
                        recv = time.time()
                        delays.append(recv - send)
                        assert msg == count
                        count += 1
                        send = time.time()
                        await stream.send(count)

                        if count >= 1e3:
                            break

            print(f'mean round trip rate (Hz): {1/mean(delays)}')
            await p.cancel_actor()


    if __name__ == '__main__':
        trio.run(main)


Yes, we spawn a python process, run ``asyncio``, start ``trio`` on the
``asyncio`` loop, then send commands to the ``trio`` scheduled tasks to
tell ``asyncio`` tasks what to do XD

The ``asyncio``-side task receives a single
``chan: LinkedTaskChannel`` handle providing a ``trio``-like
API: ``.started_nowait()``, ``.send_nowait()``, ``.get()``
and more. Feel free to sling your opinion in `#273`_!


.. _#273: https://github.com/goodboy/tractor/issues/273


Higher level "cluster" APIs
***************************
To be extra terse the ``tractor`` devs have started hacking some "higher
level" APIs for managing actor trees/clusters. These interfaces should
generally be condsidered provisional for now but we encourage you to try
them and provide feedback. Here's a new API that let's you quickly
spawn a flat cluster:

.. code:: python

    import trio
    import tractor


    async def sleepy_jane():
        uid = tractor.current_actor().uid
        print(f'Yo i am actor {uid}')
        await trio.sleep_forever()


    async def main():
        '''
        Spawn a flat actor cluster, with one process per
        detected core.

        '''
        portal_map: dict[str, tractor.Portal]
        results: dict[str, str]

        # look at this hip new syntax!
        async with (

            tractor.open_actor_cluster(
                modules=[__name__]
            ) as portal_map,

            trio.open_nursery() as n,
        ):

            for (name, portal) in portal_map.items():
                n.start_soon(portal.run, sleepy_jane)

            await trio.sleep(0.5)

            # kill the cluster with a cancel
            raise KeyboardInterrupt


    if __name__ == '__main__':
        try:
            trio.run(main)
        except KeyboardInterrupt:
            pass


.. _full worker pool re-implementation: https://github.com/goodboy/tractor/blob/master/examples/parallelism/concurrent_actors_primes.py


Under the hood
--------------
``tractor`` is an attempt to pair trionic_ `structured concurrency`_ with
distributed Python. You can think of it as a ``trio``
*-across-processes* or simply as an opinionated replacement for the
stdlib's ``multiprocessing`` but built on async programming primitives
from the ground up.

Don't be scared off by this description. ``tractor`` **is just** ``trio``
but with nurseries for process management and cancel-able streaming IPC.
If you understand how to work with ``trio``, ``tractor`` will give you
the parallelism you may have been needing.


Wait, huh?! I thought "actors" have messages, and mailboxes and stuff?!
***********************************************************************
Let's stop and ask how many canon actor model papers have you actually read ;)

From our experience many "actor systems" aren't really "actor models"
since they **don't adhere** to the `3 axioms`_ and pay even less
attention to the problem of *unbounded non-determinism* (which was the
whole point for creation of the model in the first place).

From the author's mouth, **the only thing required** is `adherance to`_
the `3 axioms`_, *and that's it*.

``tractor`` adheres to said base requirements of an "actor model"::

    In response to a message, an actor may:

    - send a finite number of new messages
    - create a finite number of new actors
    - designate a new behavior to process subsequent messages


**and** requires *no further api changes* to accomplish this.

If you want do debate this further please feel free to chime in on our
chat or discuss on one of the following issues *after you've read
everything in them*:

- https://github.com/goodboy/tractor/issues/210
- https://github.com/goodboy/tractor/issues/18


Let's clarify our parlance
**************************
Whether or not ``tractor`` has "actors" underneath should be mostly
irrelevant to users other then for referring to the interactions of our
primary runtime primitives: each Python process + ``trio.run()``
+ surrounding IPC machinery. These are our high level, base
*runtime-units-of-abstraction* which both *are* (as much as they can
be in Python) and will be referred to as our *"actors"*.

The main goal of ``tractor`` is is to allow for highly distributed
software that, through the adherence to *structured concurrency*,
results in systems which fail in predictable, recoverable and maybe even
understandable ways; being an "actor model" is just one way to describe
properties of the system.


What's on the TODO:
-------------------
Help us push toward the future of distributed `Python`.

- Erlang-style supervisors via composed context managers (see `#22
  <https://github.com/goodboy/tractor/issues/22>`_)
- Typed messaging protocols (ex. via ``msgspec.Struct``, see `#36
  <https://github.com/goodboy/tractor/issues/36>`_)
- Typed capability-based (dialog) protocols ( see `#196
  <https://github.com/goodboy/tractor/issues/196>`_ with draft work
  started in `#311 <https://github.com/goodboy/tractor/pull/311>`_)
- **macOS is now officially supported** and tested in CI
  alongside Linux!
- We **recently disabled CI-testing on windows** and need
  help getting it running again! (see `#327
  <https://github.com/goodboy/tractor/pull/327>`_). **We do
  have windows support** (and have for quite a while) but
  since no active hacker exists in the user-base to help
  test on that OS, for now we're not actively maintaining
  testing due to the added hassle and general latency..


Feel like saying hi?
--------------------
This project is very much coupled to the ongoing development of
``trio`` (i.e. ``tractor`` gets most of its ideas from that brilliant
community). If you want to help, have suggestions or just want to
say hi, please feel free to reach us in our `matrix channel`_.  If
matrix seems too hip, we're also mostly all in the the `trio gitter
channel`_!

.. _structured concurrent: https://trio.discourse.group/t/concise-definition-of-structured-concurrency/228
.. _distributed: https://en.wikipedia.org/wiki/Distributed_computing
.. _multi-processing: https://en.wikipedia.org/wiki/Multiprocessing
.. _trio: https://github.com/python-trio/trio
.. _nurseries: https://vorpus.org/blog/notes-on-structured-concurrency-or-go-statement-considered-harmful/#nurseries-a-structured-replacement-for-go-statements
.. _actor model: https://en.wikipedia.org/wiki/Actor_model
.. _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
.. _3 axioms: https://www.youtube.com/watch?v=7erJ1DV_Tlo&t=162s
.. .. _3 axioms: https://en.wikipedia.org/wiki/Actor_model#Fundamental_concepts
.. _adherance to: https://www.youtube.com/watch?v=7erJ1DV_Tlo&t=1821s
.. _trio gitter channel: https://gitter.im/python-trio/general
.. _matrix channel: https://matrix.to/#/!tractor:matrix.org
.. _broadcasting: https://github.com/goodboy/tractor/pull/229
.. _modern procotol: https://en.wikipedia.org/wiki/Rendezvous_protocol
.. _pdbp: https://github.com/mdmintz/pdbp
.. _pdb++: https://github.com/pdbpp/pdbpp
.. _cheap or nasty: https://zguide.zeromq.org/docs/chapter7/#The-Cheap-or-Nasty-Pattern
.. _(un)protocol: https://zguide.zeromq.org/docs/chapter7/#Unprotocols
.. _discovery: https://zguide.zeromq.org/docs/chapter8/#Discovery
.. _modern protocol: https://en.wikipedia.org/wiki/Rendezvous_protocol
.. _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://en.wikipedia.org/wiki/Structured_concurrency
.. _SC: https://en.wikipedia.org/wiki/Structured_concurrency
.. _libdill-docs: https://sustrik.github.io/libdill/structured-concurrency.html
.. _unrequirements: https://en.wikipedia.org/wiki/Actor_model#Direct_communication_and_asynchrony
.. _async generators: https://www.python.org/dev/peps/pep-0525/
.. _trio-parallel: https://github.com/richardsheridan/trio-parallel
.. _uv: https://docs.astral.sh/uv/
.. _msgspec: https://jcristharif.com/msgspec/
.. _guest: https://trio.readthedocs.io/en/stable/reference-lowlevel.html?highlight=guest%20mode#using-guest-mode-to-run-trio-on-top-of-other-event-loops

..
   NOTE, on generating badge links from the UI
   https://docs.github.com/en/actions/how-tos/monitoring-and-troubleshooting-workflows/monitoring-workflows/adding-a-workflow-status-badge?ref=gitguardian-blog-automated-secrets-detection#using-the-ui
.. |gh_actions| image:: https://github.com/goodboy/tractor/actions/workflows/ci.yml/badge.svg?branch=main
    :target: https://github.com/goodboy/tractor/actions/workflows/ci.yml

.. |docs| image:: https://readthedocs.org/projects/tractor/badge/?version=latest
    :target: https://tractor.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

.. |logo| image:: _static/tractor_logo_side.svg
    :width: 250
    :align: middle
