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Slim the README to one `Context` showcase 

The README inlined six example scripts; per our own "point at
`examples/`, don't duplicate" philosophy that's a lot of rot
surface. Drop all but the showcase and rework it onto the modern
`Context` API to mirror the docs landing + `we_are_processes.py`:
spawn a subactor per core, `open_context()` each, crash the root,
reap the tree.

Replace the rest with a short "want more?" pointer to the docs site
+ the `examples/` dir (where the debugger, streaming, cancellation,
`asyncio`, msging + cluster demos all live, CI-run).

Also tidy a few nits while here,
- fix the title typo "structurred" -> "structured",
- close the unterminated quote in the `UV_PROJECT_ENVIRONMENT`
  install snippet,
- add the blank line a nested `trionics` bullet list needs so the
  PyPI long-desc (this file) renders as valid RST.

(this patch was generated in some part by [`claude-code`][claude-code-gh])
[claude-code-gh]: https://github.com/anthropics/claude-code
Gud Boi 2026-06-27 22:13:30 -04:00
parent b16173482b
commit 055f8540e8
1 changed files with 78 additions and 421 deletions
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499
docs/README.rst
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@ -1,4 +1,4 @@
|logo| ``tractor``: distributed structurred concurrency |logo| ``tractor``: distributed structured concurrency
``tractor`` is a `structured concurrency`_ (SC), multi-processing_ runtime built on trio_. ``tractor`` is a `structured concurrency`_ (SC), multi-processing_ runtime built on trio_.
@ -57,6 +57,7 @@ Features
`discovery`_ sys with plans to support multiple `modern protocol`_ `discovery`_ sys with plans to support multiple `modern protocol`_
approaches. approaches.
- Various ``trio`` extension APIs via ``tractor.trionics`` such as, - Various ``trio`` extension APIs via ``tractor.trionics`` such as,
- task fan-out `broadcasting`_, - task fan-out `broadcasting`_,
- multi-task-single-resource-caching and fan-out-to-multi - multi-task-single-resource-caching and fan-out-to-multi
``__aenter__()`` APIs for ``@acm`` functions, ``__aenter__()`` APIs for ``@acm`` functions,
@ -98,7 +99,7 @@ the code base::
# but @goodboy prefers the more explicit (and shell agnostic) # but @goodboy prefers the more explicit (and shell agnostic)
# https://docs.astral.sh/uv/configuration/environment/#uv_project_environment # https://docs.astral.sh/uv/configuration/environment/#uv_project_environment
UV_PROJECT_ENVIRONMENT="tractor_py313 UV_PROJECT_ENVIRONMENT="tractor_py313"
# hint hint, enter @goodboy's fave shell B) # hint hint, enter @goodboy's fave shell B)
uv run --dev xonsh uv run --dev xonsh
@ -118,84 +119,24 @@ and rendered as the "Building these docs" section of our dev-tips guide.
Example codez Example codez
------------- -------------
In ``tractor``'s (very lacking) documention we prefer to point to We prefer to point you at the runnable scripts under ``examples/``
example scripts in the repo over duplicating them in docs, but with - each is CI-run and ``literalinclude``-d straight into the docs, so
that in mind here are some definitive snippets to try and hook you what you read there is what actually runs - rather than duplicate a
into digging deeper. pile of them here. But here's the one-minute pitch: spawn a subactor
per core, open a ``Context`` into each, then crash the root *on
purpose* and watch the runtime reap the whole tree - zero zombies,
Run a func in a process guaranteed.
***********************
Use ``trio``'s style of focussing on *tasks as functions*:
.. code:: python .. code:: python
""" '''
Run with a process monitor from a terminal using::
$TERM -e watch -n 0.1 "pstree -a $$" \
& python examples/parallelism/single_func.py \
&& kill $!
"""
import os
import tractor
import trio
async def burn_cpu():
pid = os.getpid()
# burn a core @ ~ 50kHz
for _ in range(50000):
await trio.sleep(1/50000/50)
return os.getpid()
async def main():
async with tractor.open_nursery() as n:
portal = await n.run_in_actor(burn_cpu)
# burn rubber in the parent too
await burn_cpu()
# wait on result from target function
pid = await portal.result()
# end of nursery block
print(f"Collected subproc {pid}")
if __name__ == '__main__':
trio.run(main)
This runs ``burn_cpu()`` in a new process and reaps it on completion
of the nursery block.
If you only need to run a sync function and retreive a single result, you
might want to check out `trio-parallel`_.
Zombie safe: self-destruct a process tree
*****************************************
``tractor`` tries to protect you from zombies, no matter what.
.. code:: python
"""
Run with a process monitor from a terminal using:: Run with a process monitor from a terminal using::
$TERM -e watch -n 0.1 "pstree -a $$" \ $TERM -e watch -n 0.1 "pstree -a $$" \
& python examples/parallelism/we_are_processes.py \ & python examples/parallelism/we_are_processes.py \
&& kill $! && kill $!
""" '''
from multiprocessing import cpu_count from multiprocessing import cpu_count
import os import os
@ -203,27 +144,70 @@ Zombie safe: self-destruct a process tree
import trio import trio
async def target(): @tractor.context
print( async def endpoint(
f"Yo, i'm '{tractor.current_actor().name}' " ctx: tractor.Context,
f"running in pid {os.getpid()}" ):
) actor_name: str = tractor.current_actor().name
pid: int = os.getpid()
await ctx.started((actor_name, pid))
await trio.sleep_forever() await trio.sleep_forever()
async def spawn_and_open_ep(
an: tractor.ActorNursery,
i: int,
) -> None:
'''
Spawn a subactor, start a remote `endpoint()`-task in it.
'''
ptl: tractor.Portal = await an.start_actor(
name=f'worker_{i}',
enable_modules=[__name__],
)
ctx: tractor.Context
async with ptl.open_context(endpoint) as (
ctx,
(sub_name, sub_pid),
):
print(
f'Started ep-task in subactor,\n'
f'{i}::{sub_name!r}@{sub_pid}\n'
)
await ctx.wait_for_result()
async def main(): async def main():
'''
Spawn a subactor-per-CPU then self-destruct the cluster.
async with tractor.open_nursery() as n: '''
tn: trio.Nursery
an: tractor.ActorNursery
async with (
tractor.open_nursery(
# XXX coming soon!
# https://github.com/goodboy/tractor/pull/463
# start_method='main_thread_forkserver',
) as an,
# spawn subs concurrently (in bg `trio.Task`s) so each
# actor's cold `import tractor` (~0.4s, see #470) overlaps
# instead of stacking; once forkserver (#463) lands, spawn
# is cheap enough to just loop sequentially.
trio.open_nursery() as tn,
):
for i in range(cpu_count()): for i in range(cpu_count()):
await n.run_in_actor(target, name=f'worker_{i}') tn.start_soon(
spawn_and_open_ep,
print('This process tree will self-destruct in 1 sec...') an,
await trio.sleep(1) i,
)
# raise an error in root actor/process and trigger destruct_in: int = 2
# reaping of all minions print(
f'This tree will self-destruct in {destruct_in}s..\n'
)
await trio.sleep(destruct_in)
raise Exception('Self Destructed') raise Exception('Self Destructed')
@ -234,343 +218,16 @@ Zombie safe: self-destruct a process tree
print('Zombies Contained') print('Zombies Contained')
If you can create zombie child processes (without using a system signal) If you can create zombie child processes (without using a system
it **is a bug**. signal) it **is a bug** - please report it!
Want more? Our docs walk the flagship multi-process debugger,
bidirectional streaming over a ``Context``, cancellation, discovery,
"infected ``asyncio``", typed messaging and worker-pool / cluster
patterns - each backed by a runnable script:
"Native" multi-process debugging - docs: https://goodboy.github.io/tractor/
******************************** - examples: https://github.com/goodboy/tractor/tree/main/examples
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 Under the hood