Apply the fix from @chrizzFTD where we invoke the entry point using
module exec mode on a ``__main__.py`` and import the
``test_example::`main()` from within that entry point script.
A per #98 we need tests for examples from the docs as they would be run
by a user copy and pasting the code. This adds a small system for loading
examples from an "examples/" directory and executing them in
a subprocess while checking the output. We can use this to also verify
end-to-end expected logging output on std streams (ex. logging on
stderr).
To expand this further we can parameterize the test list using the
contents of the examples directory instead of hardcoding the script
names as I've done here initially.
Also, fix up the current readme examples to have the required/proper `if
__name__ == '__main__'` script guard.
This was originally bundled in #102 but the windows CI there has blocked
that from landing quickly. These examples need to be fixed stat since
I've had at least a couple people notice it now when first trying out
the project.
The logic in the `ActorNursery` block is critical to cancellation
semantics and in particular, understanding how supervisor strategies are
invoked. Stick in a bunch of explanatory comments to clear up these
details and also prepare to introduce more supervisor strats besides
the current one-cancels-all approach.
Instead of hackery trying to map modules manually from the filesystem
let Python do all the work by simply copying what ``multiprocessing``
does to "fixup the __main__ module" in spawned subprocesses. The new
private module ``_mp_fixup_main.py`` is simply cherry picked code from
``multiprocessing.spawn`` which does just that. We only need these
"fixups" when using a backend other then ``multiprocessing``; for
now just when using ``trio_run_in_process``.
Thanks to @salotz for pointing out that the first example in the docs
was broken. Though it's somewhat embarrassing this might also explain
the problem in #79 and certain issues in #59...
The solution here is to import the target RPC module using the its
unique basename and absolute filepath in the sub-actor that requires it.
Special handling for `__main__` and `__mp_main__` is needed since the
spawned subprocess will have no knowledge about these parent-
-state-specific module variables. Solution: map the modules name to the
respective module file basename in the child process since the module
variables will of course have different values in children.
Add a `--spawn-backend` option which can be set to one of {'mp',
'trio_run_in_process'} which will either run the test suite using the
`multiprocessing` or `trio-run-in-process` backend respectively.
Currently trying to run both in the same session can result in hangs
seemingly due to a lack of cleanup of forkservers / resource trackers
from `multiprocessing` which cause broken pipe errors on occasion (no
idea on the details).
For `test_cancellation.py::test_nested_multierrors`, use less nesting
when mp is used since it breaks if we push it too hard with the
whole recursive subprocess spawning thing...
Set `trio-run-in-process` as the default on *nix systems and
`multiprocessing`'s spawn method on Windows. Enable overriding the
default choice using `tractor._spawn.try_set_start_method()`. Allows
for easy runs of the test suite using a user chosen backend.
It seems that mixing the two backends in the test suite results in hangs
due to lingering forkservers and resource managers from
`multiprocessing`? Likely we'll need either 2 separate CI runs to work
or someway to be sure that these lingering servers are killed in between
tests.
This took a ton of tinkering and a rework of the actor nursery tear down
logic. The main changes include:
- each subprocess is now spawned from inside a trio task
from one of two containing nurseries created in the body of
`tractor.open_nursery()`: one for `run_in_actor()` processes and one for
`start_actor()` "daemons". This is to address the need for
`trio-run-in_process.open_in_process()` opening a nursery which must
be closed from the same task that opened it. Using this same approach
for `multiprocessing` seems to work well. The nurseries are waited in
order (rip actors then daemon actors) during tear down which allows
for avoiding the recursive re-entry of `ActorNursery.wait()` handled
prior.
- pull out all the nested functions / closures that were in
`ActorNursery.wait()` and move into the `_spawn` module such that
that process shutdown logic takes place in each containing task's
code path. This allows for vastly simplifying `.wait()` to just contain an
event trigger which initiates process waiting / result collection.
Likely `.wait()` should just be removed since it can no longer be used
to synchronously wait on the actor nursery.
- drop `ActorNursery.__aenter__()` / `.__atexit__()` and move this
"supervisor" tear down logic into the closing block of `open_nursery()`.
This not only cleans makes the code more comprehensible it also
makes our nursery implementation look more like the one in `trio`.
Resolves#93
Get a few more things working:
- fail reliably when remote module loading goes awry
- do a real hacky job of module loading using `sys.path` stuffsies
- we're still totally borked when trying to spin up and quickly cancel
a bunch of subactors...
It's a small move forward I guess.
Well...after enough `# type: ignore`s `mypy` is happy, and after enough clicking of *rerun build* the windows CI passed so I think this is prolly gtg peeps!
Prepend the actor and task names in each log emission. This makes
debugging much more sane since you can see from which process and
running task the log message originates from!
Resolves#13
Another step toward having a complete test for #89.
Subactor breadth still seems to cause the most havoc and is why I've
kept that value to just 2 for now.
Tyler Goodlet
goodboy