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Introspecting and extending Trio with trio.lowlevel

trio.lowlevel

trio.lowlevel contains low-level APIs for introspecting and extending Trio. If you're writing ordinary, everyday code, then you can ignore this module completely. But sometimes you need something a bit lower level. Here are some examples of situations where you should reach for trio.lowlevel:

  • You want to implement a new synchronization primitive <synchronization> that Trio doesn't (yet) provide, like a reader-writer lock.
  • You want to extract low-level metrics to monitor the health of your application.
  • You want to use a low-level operating system interface that Trio doesn't (yet) provide its own wrappers for, like watching a filesystem directory for changes.
  • You want to implement an interface for calling between Trio and another event loop within the same process.
  • You're writing a debugger and want to visualize Trio's task tree.
  • You need to interoperate with a C library whose API exposes raw file descriptors.

You don't need to be scared of trio.lowlevel, as long as you take proper precautions. These are real public APIs, with strictly defined and carefully documented semantics. They're the same tools we use to implement all the nice high-level APIs in the trio namespace. But, be careful. Some of those strict semantics have nasty big pointy teeth. If you make a mistake, Trio may not be able to handle it gracefully; conventions and guarantees that are followed strictly in the rest of Trio do not always apply. When you use this module, it's your job to think about how you're going to handle the tricky cases so you can expose a friendly Trio-style API to your users.

Debugging and instrumentation

Trio tries hard to provide useful hooks for debugging and instrumentation. Some are documented above (the nursery introspection attributes, trio.Lock.statistics, etc.). Here are some more.

Global statistics

current_statistics

The current clock

current_clock

Instrument API

The instrument API provides a standard way to add custom instrumentation to the run loop. Want to make a histogram of scheduling latencies, log a stack trace of any task that blocks the run loop for >50 ms, or measure what percentage of your process's running time is spent waiting for I/O? This is the place.

The general idea is that at any given moment, trio.run maintains a set of "instruments", which are objects that implement the trio.abc.Instrument interface. When an interesting event happens, it loops over these instruments and notifies them by calling an appropriate method. The tutorial has a simple example of using this for tracing <tutorial-instrument-example>.

Since this hooks into Trio at a rather low level, you do have to be careful. The callbacks are run synchronously, and in many cases if they error out then there isn't any plausible way to propagate this exception (for instance, we might be deep in the guts of the exception propagation machinery...). Therefore our current strategy for handling exceptions raised by instruments is to (a) log an exception to the "trio.abc.Instrument" logger, which by default prints a stack trace to standard error and (b) disable the offending instrument.

You can register an initial list of instruments by passing them to trio.run. add_instrument and remove_instrument let you add and remove instruments at runtime.

add_instrument

remove_instrument

And here's the interface to implement if you want to build your own ~trio.abc.Instrument:

trio.abc.Instrument

The tutorial has a fully-worked example <tutorial-instrument-example> of defining a custom instrument to log Trio's internal scheduling decisions.

Low-level process spawning

trio.lowlevel.open_process

Low-level I/O primitives

Different environments expose different low-level APIs for performing async I/O. trio.lowlevel exposes these APIs in a relatively direct way, so as to allow maximum power and flexibility for higher level code. However, this means that the exact API provided may vary depending on what system Trio is running on.

Universally available API

All environments provide the following functions:

wait_readable(obj)

Block until the kernel reports that the given object is readable.

On Unix systems, obj must either be an integer file descriptor, or else an object with a .fileno() method which returns an integer file descriptor. Any kind of file descriptor can be passed, though the exact semantics will depend on your kernel. For example, this probably won't do anything useful for on-disk files.

On Windows systems, obj must either be an integer SOCKET handle, or else an object with a .fileno() method which returns an integer SOCKET handle. File descriptors aren't supported, and neither are handles that refer to anything besides a SOCKET.

raises trio.BusyResourceError

if another task is already waiting for the given socket to become readable.

raises trio.ClosedResourceError

if another task calls notify_closing while this function is still working.

wait_writable(obj)

Block until the kernel reports that the given object is writable.

See wait_readable for the definition of obj.

raises trio.BusyResourceError

if another task is already waiting for the given socket to become writable.

raises trio.ClosedResourceError

if another task calls notify_closing while this function is still working.

notify_closing(obj)

Call this before closing a file descriptor (on Unix) or socket (on Windows). This will cause any wait_readable or wait_writable calls on the given object to immediately wake up and raise ~trio.ClosedResourceError.

This doesn't actually close the object – you still have to do that yourself afterwards. Also, you want to be careful to make sure no new tasks start waiting on the object in between when you call this and when it's actually closed. So to close something properly, you usually want to do these steps in order:

  1. Explicitly mark the object as closed, so that any new attempts to use it will abort before they start.
  2. Call notify_closing to wake up any already-existing users.
  3. Actually close the object.

It's also possible to do them in a different order if that's more convenient, but only if you make sure not to have any checkpoints in between the steps. This way they all happen in a single atomic step, so other tasks won't be able to tell what order they happened in anyway.

Unix-specific API

FdStream supports wrapping Unix files (such as a pipe or TTY) as a stream.

If you have two different file descriptors for sending and receiving, and want to bundle them together into a single bidirectional ~trio.abc.Stream, then use `trio.StapledStream`:

bidirectional_stream = trio.StapledStream(
    trio.lowlevel.FdStream(write_fd),
    trio.lowlevel.FdStream(read_fd)
)

FdStream

Kqueue-specific API

TODO: these are implemented, but are currently more of a sketch than anything real. See #26.

current_kqueue()

wait_kevent(ident, filter, abort_func)

monitor_kevent(ident, filter)

Windows-specific API

WaitForSingleObject(handle)

Async and cancellable variant of WaitForSingleObject. Windows only.

arg handle

A Win32 object handle, as a Python integer.

raises OSError

If the handle is invalid, e.g. when it is already closed.

TODO: these are implemented, but are currently more of a sketch than anything real. See #26 and #52.

register_with_iocp(handle)

wait_overlapped(handle, lpOverlapped)

current_iocp()

monitor_completion_key()

Global state: system tasks and run-local variables

RunVar

spawn_system_task

Trio tokens

TrioToken()

current_trio_token

Spawning threads

start_thread_soon

Safer KeyboardInterrupt handling

Trio's handling of control-C is designed to balance usability and safety. On the one hand, there are sensitive regions (like the core scheduling loop) where it's simply impossible to handle arbitrary KeyboardInterrupt exceptions while maintaining our core correctness invariants. On the other, if the user accidentally writes an infinite loop, we do want to be able to break out of that. Our solution is to install a default signal handler which checks whether it's safe to raise KeyboardInterrupt at the place where the signal is received. If so, then we do; otherwise, we schedule a KeyboardInterrupt to be delivered to the main task at the next available opportunity (similar to how ~trio.Cancelled is delivered).

So that's great, but – how do we know whether we're in one of the sensitive parts of the program or not?

This is determined on a function-by-function basis. By default:

  • The top-level function in regular user tasks is unprotected.
  • The top-level function in system tasks is protected.
  • If a function doesn't specify otherwise, then it inherits the protection state of its caller.

This means you only need to override the defaults at places where you transition from protected code to unprotected code or vice-versa.

These transitions are accomplished using two function decorators:

disable_ki_protection()

Decorator that marks the given regular function, generator function, async function, or async generator function as unprotected against KeyboardInterrupt, i.e., the code inside this function can be rudely interrupted by KeyboardInterrupt at any moment.

If you have multiple decorators on the same function, then this should be at the bottom of the stack (closest to the actual function).

An example of where you'd use this is in implementing something like trio.from_thread.run, which uses TrioToken.run_sync_soon to get into the Trio thread. ~TrioToken.run_sync_soon callbacks are run with KeyboardInterrupt protection enabled, and trio.from_thread.run takes advantage of this to safely set up the machinery for sending a response back to the original thread, but then uses disable_ki_protection when entering the user-provided function.

enable_ki_protection()

Decorator that marks the given regular function, generator function, async function, or async generator function as protected against KeyboardInterrupt, i.e., the code inside this function won't be rudely interrupted by KeyboardInterrupt. (Though if it contains any checkpoints <checkpoints>, then it can still receive KeyboardInterrupt at those. This is considered a polite interruption.)

Warning

Be very careful to only use this decorator on functions that you know will either exit in bounded time, or else pass through a checkpoint regularly. (Of course all of your functions should have this property, but if you mess it up here then you won't even be able to use control-C to escape!)

If you have multiple decorators on the same function, then this should be at the bottom of the stack (closest to the actual function).

An example of where you'd use this is on the __exit__ implementation for something like a ~trio.Lock, where a poorly-timed KeyboardInterrupt could leave the lock in an inconsistent state and cause a deadlock.

currently_ki_protected

Sleeping and waking

Wait queue abstraction

ParkingLot

Low-level checkpoint functions

checkpoint

The next two functions are used together to make up a checkpoint:

checkpoint_if_cancelled

cancel_shielded_checkpoint

These are commonly used in cases where you have an operation that might-or-might-not block, and you want to implement Trio's standard checkpoint semantics. Example:

async def operation_that_maybe_blocks():
    await checkpoint_if_cancelled()
    try:
        ret = attempt_operation()
    except BlockingIOError:
        # need to block and then retry, which we do below
        pass
    else:
        # operation succeeded, finish the checkpoint then return
        await cancel_shielded_checkpoint()
        return ret
    while True:
        await wait_for_operation_to_be_ready()
        try:
            return attempt_operation()
        except BlockingIOError:
            pass

This logic is a bit convoluted, but accomplishes all of the following:

  • Every successful execution path passes through a checkpoint (assuming that wait_for_operation_to_be_ready is an unconditional checkpoint)
  • Our cancellation semantics <cancellable-primitives> say that ~trio.Cancelled should only be raised if the operation didn't happen. Using cancel_shielded_checkpoint on the early-exit branch accomplishes this.
  • On the path where we do end up blocking, we don't pass through any schedule points before that, which avoids some unnecessary work.
  • Avoids implicitly chaining the BlockingIOError with any errors raised by attempt_operation or wait_for_operation_to_be_ready, by keeping the while True: loop outside of the except BlockingIOError: block.

These functions can also be useful in other situations. For example, when trio.to_thread.run_sync schedules some work to run in a worker thread, it blocks until the work is finished (so it's a schedule point), but by default it doesn't allow cancellation. So to make sure that the call always acts as a checkpoint, it calls checkpoint_if_cancelled before starting the thread.

Low-level blocking

wait_task_rescheduled

Abort

reschedule

Here's an example lock class implemented using wait_task_rescheduled directly. This implementation has a number of flaws, including lack of fairness, O(n) cancellation, missing error checking, failure to insert a checkpoint on the non-blocking path, etc. If you really want to implement your own lock, then you should study the implementation of trio.Lock and use ParkingLot, which handles some of these issues for you. But this does serve to illustrate the basic structure of the wait_task_rescheduled API:

class NotVeryGoodLock:
    def __init__(self):
        self._blocked_tasks = collections.deque()
        self._held = False

    async def acquire(self):
        # We might have to try several times to acquire the lock.
        while self._held:
            # Someone else has the lock, so we have to wait.
            task = trio.lowlevel.current_task()
            self._blocked_tasks.append(task)
            def abort_fn(_):
                self._blocked_tasks.remove(task)
                return trio.lowlevel.Abort.SUCCEEDED
            await trio.lowlevel.wait_task_rescheduled(abort_fn)
            # At this point the lock was released -- but someone else
            # might have swooped in and taken it again before we
            # woke up. So we loop around to check the 'while' condition
            # again.
        # if we reach this point, it means that the 'while' condition
        # has just failed, so we know no-one is holding the lock, and
        # we can take it.
        self._held = True

    def release(self):
        self._held = False
        if self._blocked_tasks:
            woken_task = self._blocked_tasks.popleft()
            trio.lowlevel.reschedule(woken_task)

Task API

current_root_task()

current_task()

A Task object represents a concurrent "thread" of execution. It has no public constructor; Trio internally creates a Task object for each call to nursery.start(...) or nursery.start_soon(...).

Its public members are mostly useful for introspection and debugging:

name

String containing this Task's name. Usually the name of the function this Task is running, but can be overridden by passing name= to start or start_soon.

coro

This task's coroutine object. Example usage: extracting a stack trace:

import traceback

def walk_coro_stack(coro):
    while coro is not None:
        if hasattr(coro, "cr_frame"):
            # A real coroutine
            yield coro.cr_frame, coro.cr_frame.f_lineno
            coro = coro.cr_await
        else:
            # A generator decorated with @types.coroutine
            yield coro.gi_frame, coro.gi_frame.f_lineno
            coro = coro.gi_yieldfrom

def print_stack_for_task(task):
    ss = traceback.StackSummary.extract(walk_coro_stack(task.coro))
    print("".join(ss.format()))

context

This task's contextvars.Context object.

parent_nursery

eventual_parent_nursery

child_nurseries

custom_sleep_data

Trio doesn't assign this variable any meaning, except that it sets it to None whenever a task is rescheduled. It can be used to share data between the different tasks involved in putting a task to sleep and then waking it up again. (See wait_task_rescheduled for details.)

Using "guest mode" to run Trio on top of other event loops

What is "guest mode"?

An event loop acts as a central coordinator to manage all the IO happening in your program. Normally, that means that your application has to pick one event loop, and use it for everything. But what if you like Trio, but also need to use a framework like Qt or PyGame that has its own event loop? Then you need some way to run both event loops at once.

It is possible to combine event loops, but the standard approaches all have significant downsides:

  • Polling: this is where you use a busy-loop to manually check for IO on both event loops many times per second. This adds latency, and wastes CPU time and electricity.
  • Pluggable IO backends: this is where you reimplement one of the event loop APIs on top of the other, so you effectively end up with just one event loop. This requires a significant amount of work for each pair of event loops you want to integrate, and different backends inevitably end up with inconsistent behavior, forcing users to program against the least-common-denominator. And if the two event loops expose different feature sets, it may not even be possible to implement one in terms of the other.
  • Running the two event loops in separate threads: This works, but most event loop APIs aren't thread-safe, so in this approach you need to keep careful track of which code runs on which event loop, and remember to use explicit inter-thread messaging whenever you interact with the other loop – or else risk obscure race conditions and data corruption.

That's why Trio offers a fourth option: guest mode. Guest mode lets you execute trio.run on top of some other "host" event loop, like Qt. Its advantages are:

  • Efficiency: guest mode is event-driven instead of using a busy-loop, so it has low latency and doesn't waste electricity.

  • No need to think about threads: your Trio code runs in the same thread as the host event loop, so you can freely call sync Trio APIs from the host, and call sync host APIs from Trio. For example, if you're making a GUI app with Qt as the host loop, then making a cancel button and connecting it to a trio.CancelScope is as easy as writing:

    # Trio code can create Qt objects without any special ceremony...
my_cancel_button = QPushButton("Cancel")
# ...and Qt can call back to Trio just as easily
my_cancel_button.clicked.connect(my_cancel_scope.cancel)

    (For async APIs, it's not that simple, but you can use sync APIs to build explicit bridges between the two worlds, e.g. by passing async functions and their results back and forth through queues.)

  • Consistent behavior: guest mode uses the same code as regular Trio: the same scheduler, same IO code, same everything. So you get the full feature set and everything acts the way you expect.
  • Simple integration and broad compatibility: pretty much every event loop offers some threadsafe "schedule a callback" operation, and that's all you need to use it as a host loop.

Really? How is that possible?

Note

You can use guest mode without reading this section. It's included for those who enjoy understanding how things work.

All event loops have the same basic structure. They loop through two operations, over and over:

  1. Wait for the operating system to notify them that something interesting has happened, like data arriving on a socket or a timeout passing. They do this by invoking a platform-specific sleep_until_something_happens() system call – select, epoll, kqueue, GetQueuedCompletionEvents, etc.
  2. Run all the user tasks that care about whatever happened, then go back to step 1.

The problem here is step 1. Two different event loops on the same thread can take turns running user tasks in step 2, but when they're idle and nothing is happening, they can't both invoke their own sleep_until_something_happens() function at the same time.

The "polling" and "pluggable backend" strategies solve this by hacking the loops so both step 1s can run at the same time in the same thread. Keeping everything in one thread is great for step 2, but the step 1 hacks create problems.

The "separate threads" strategy solves this by moving both steps into separate threads. This makes step 1 work, but the downside is that now the user tasks in step 2 are running separate threads as well, so users are forced to deal with inter-thread coordination.

The idea behind guest mode is to combine the best parts of each approach: we move Trio's step 1 into a separate worker thread, while keeping Trio's step 2 in the main host thread. This way, when the application is idle, both event loops do their sleep_until_something_happens() at the same time in their own threads. But when the app wakes up and your code is actually running, it all happens in a single thread. The threading trickiness is all handled transparently inside Trio.

Concretely, we unroll Trio's internal event loop into a chain of callbacks, and as each callback finishes, it schedules the next callback onto the host loop or a worker thread as appropriate. So the only thing the host loop has to provide is a way to schedule a callback onto the main thread from a worker thread.

Coordinating between Trio and the host loop does add some overhead. The main cost is switching in and out of the background thread, since this requires cross-thread messaging. This is cheap (on the order of a few microseconds, assuming your host loop is implemented efficiently), but it's not free.

But, there's a nice optimization we can make: we only need the thread when our sleep_until_something_happens() call actually sleeps, that is, when the Trio part of your program is idle and has nothing to do. So before we switch into the worker thread, we double-check whether we're idle, and if not, then we skip the worker thread and jump directly to step 2. This means that your app only pays the extra thread-switching penalty at moments when it would otherwise be sleeping, so it should have minimal effect on your app's overall performance.

The total overhead will depend on your host loop, your platform, your application, etc. But we expect that in most cases, apps running in guest mode should only be 5-10% slower than the same code using trio.run. If you find that's not true for your app, then please let us know and we'll see if we can fix it!

Implementing guest mode for your favorite event loop

Let's walk through what you need to do to integrate Trio's guest mode with your favorite event loop. Treat this section like a checklist.

Getting started: The first step is to get something basic working. Here's a minimal example of running Trio on top of asyncio, that you can use as a model:

import asyncio, trio

# A tiny Trio program
async def trio_main():
    for _ in range(5):
        print("Hello from Trio!")
        # This is inside Trio, so we have to use Trio APIs
        await trio.sleep(1)
    return "trio done!"

# The code to run it as a guest inside asyncio
async def asyncio_main():
    asyncio_loop = asyncio.get_running_loop()

    def run_sync_soon_threadsafe(fn):
        asyncio_loop.call_soon_threadsafe(fn)

    def done_callback(trio_main_outcome):
        print(f"Trio program ended with: {trio_main_outcome}")

    # This is where the magic happens:
    trio.lowlevel.start_guest_run(
        trio_main,
        run_sync_soon_threadsafe=run_sync_soon_threadsafe,
        done_callback=done_callback,
    )

    # Let the host loop run for a while to give trio_main time to
    # finish. (WARNING: This is a hack. See below for better
    # approaches.)
    #
    # This function is in asyncio, so we have to use asyncio APIs.
    await asyncio.sleep(10)

asyncio.run(asyncio_main())

You can see we're using asyncio-specific APIs to start up a loop, and then we call trio.lowlevel.start_guest_run. This function is very similar to trio.run, and takes all the same arguments. But it has two differences:

First, instead of blocking until trio_main has finished, it schedules trio_main to start running on top of the host loop, and then returns immediately. So trio_main is running in the background – that's why we have to sleep and give it time to finish.

And second, it requires two extra keyword arguments: run_sync_soon_threadsafe, and done_callback.

For run_sync_soon_threadsafe, we need a function that takes a synchronous callback, and schedules it to run on your host loop. And this function needs to be "threadsafe" in the sense that you can safely call it from any thread. So you need to figure out how to write a function that does that using your host loop's API. For asyncio, this is easy because ~asyncio.loop.call_soon_threadsafe does exactly what we need; for your loop, it might be more or less complicated.

For done_callback, you pass in a function that Trio will automatically invoke when the Trio run finishes, so you know it's done and what happened. For this basic starting version, we just print the result; in the next section we'll discuss better alternatives.

At this stage you should be able to run a simple Trio program inside your host loop. Now we'll turn that prototype into something solid.

Loop lifetimes: One of the trickiest things in most event loops is shutting down correctly. And having two event loops makes this even harder!

If you can, we recommend following this pattern:

  • Start up your host loop
  • Immediately call start_guest_run to start Trio
  • When Trio finishes and your done_callback is invoked, shut down the host loop
  • Make sure that nothing else shuts down your host loop

This way, your two event loops have the same lifetime, and your program automatically exits when your Trio function finishes.

Here's how we'd extend our asyncio example to implement this pattern:

# Improved version, that shuts down properly after Trio finishes
async def asyncio_main():
    asyncio_loop = asyncio.get_running_loop()

    def run_sync_soon_threadsafe(fn):
        asyncio_loop.call_soon_threadsafe(fn)

    # Revised 'done' callback: set a Future
    done_fut = asyncio_loop.create_future()
    def done_callback(trio_main_outcome):
        done_fut.set_result(trio_main_outcome)

    trio.lowlevel.start_guest_run(
        trio_main,
        run_sync_soon_threadsafe=run_sync_soon_threadsafe,
        done_callback=done_callback,
    )

    # Wait for the guest run to finish
    trio_main_outcome = await done_fut
    # Pass through the return value or exception from the guest run
    return trio_main_outcome.unwrap()

And then you can encapsulate all this machinery in a utility function that exposes a trio.run-like API, but runs both loops together:

def trio_run_with_asyncio(trio_main, *args, **trio_run_kwargs):
    async def asyncio_main():
        # same as above
        ...

    return asyncio.run(asyncio_main())

Technically, it is possible to use other patterns. But there are some important limitations you have to respect:

  • You must let the Trio program run to completion. Many event loops let you stop the event loop at any point, and any pending callbacks/tasks/etc. just... don't run. Trio follows a more structured system, where you can cancel things, but the code always runs to completion, so finally blocks run, resources are cleaned up, etc. If you stop your host loop early, before the done_callback is invoked, then that cuts off the Trio run in the middle without a chance to clean up. This can leave your code in an inconsistent state, and will definitely leave Trio's internals in an inconsistent state, which will cause errors if you try to use Trio again in that thread.

    Some programs need to be able to quit at any time, for example in response to a GUI window being closed or a user selecting a "Quit" from a menu. In these cases, we recommend wrapping your whole program in a trio.CancelScope, and cancelling it when you want to quit.

  • Each host loop can only have one start_guest_run at a time. If you try to start a second one, you'll get an error. If you need to run multiple Trio functions at the same time, then start up a single Trio run, open a nursery, and then start your functions as child tasks in that nursery.
  • Unless you or your host loop register a handler for signal.SIGINT before starting Trio (this is not common), then Trio will take over delivery of KeyboardInterrupts. And since Trio can't tell which host code is safe to interrupt, it will only deliver KeyboardInterrupt into the Trio part of your code. This is fine if your program is set up to exit when the Trio part exits, because the KeyboardInterrupt will propagate out of Trio and then trigger the shutdown of your host loop, which is just what you want.

Given these constraints, we think the simplest approach is to always start and stop the two loops together.

Signal management: "Signals" are a low-level inter-process communication primitive. When you hit control-C to kill a program, that uses a signal. Signal handling in Python has a lot of moving parts. One of those parts is signal.set_wakeup_fd, which event loops use to make sure that they wake up when a signal arrives so they can respond to it. (If you've ever had an event loop ignore you when you hit control-C, it was probably because they weren't using signal.set_wakeup_fd correctly.)

But, only one event loop can use signal.set_wakeup_fd at a time. And in guest mode that can cause problems: Trio and the host loop might start fighting over who's using signal.set_wakeup_fd.

Some event loops, like asyncio, won't work correctly unless they win this fight. Fortunately, Trio is a little less picky: as long as someone makes sure that the program wakes up when a signal arrives, it should work correctly. So if your host loop wants signal.set_wakeup_fd, then you should disable Trio's signal.set_wakeup_fd support, and then both loops will work correctly.

On the other hand, if your host loop doesn't use signal.set_wakeup_fd, then the only way to make everything work correctly is to enable Trio's signal.set_wakeup_fd support.

By default, Trio assumes that your host loop doesn't use signal.set_wakeup_fd. It does try to detect when this creates a conflict with the host loop, and print a warning – but unfortunately, by the time it detects it, the damage has already been done. So if you're getting this warning, then you should disable Trio's signal.set_wakeup_fd support by passing host_uses_signal_set_wakeup_fd=True to start_guest_run.

If you aren't seeing any warnings with your initial prototype, you're probably fine. But the only way to be certain is to check your host loop's source. For example, asyncio may or may not use signal.set_wakeup_fd depending on the Python version and operating system.

A small optimization: Finally, consider a small optimization. Some event loops offer two versions of their "call this function soon" API: one that can be used from any thread, and one that can only be used from the event loop thread, with the latter being cheaper. For example, asyncio has both ~asyncio.loop.call_soon_threadsafe and ~asyncio.loop.call_soon.

If you have a loop like this, then you can also pass a run_sync_soon_not_threadsafe=... kwarg to start_guest_run, and Trio will automatically use it when appropriate.

If your loop doesn't have a split like this, then don't worry about it; run_sync_soon_not_threadsafe= is optional. (If it's not passed, then Trio will just use your threadsafe version in all cases.)

That's it! If you've followed all these steps, you should now have a cleanly-integrated hybrid event loop. Go make some cool GUIs/games/whatever!

Limitations

In general, almost all Trio features should work in guest mode. The exception is features which rely on Trio having a complete picture of everything that your program is doing, since obviously, it can't control the host loop or see what it's doing.

Custom clocks can be used in guest mode, but they only affect Trio timeouts, not host loop timeouts. And the autojump clock <testing-time> and related trio.testing.wait_all_tasks_blocked can technically be used in guest mode, but they'll only take Trio tasks into account when decided whether to jump the clock or whether all tasks are blocked.

Reference

start_guest_run

Handing off live coroutine objects between coroutine runners

Internally, Python's async/await syntax is built around the idea of "coroutine objects" and "coroutine runners". A coroutine object represents the state of an async callstack. But by itself, this is just a static object that sits there. If you want it to do anything, you need a coroutine runner to push it forward. Every Trio task has an associated coroutine object (see Task.coro), and the Trio scheduler acts as their coroutine runner.

But of course, Trio isn't the only coroutine runner in Python – asyncio has one, other event loops have them, you can even define your own.

And in some very, very unusual circumstances, it even makes sense to transfer a single coroutine object back and forth between different coroutine runners. That's what this section is about. This is an extremely exotic use case, and assumes a lot of expertise in how Python async/await works internally. For motivating examples, see trio-asyncio issue #42, and trio issue #649. For more details on how coroutines work, we recommend André Caron's A tale of event loops, or going straight to PEP 492 for the full details.

permanently_detach_coroutine_object

temporarily_detach_coroutine_object

reattach_detached_coroutine_object