Introspecting and extending Trio with 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 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

trio.lowlevel.current_statistics()

Returns an object containing run-loop-level debugging information.

Currently the following fields are defined:

  • tasks_living (int): The number of tasks that have been spawned and not yet exited.

  • tasks_runnable (int): The number of tasks that are currently queued on the run queue (as opposed to blocked waiting for something to happen).

  • seconds_to_next_deadline (float): The time until the next pending cancel scope deadline. May be negative if the deadline has expired but we haven’t yet processed cancellations. May be inf if there are no pending deadlines.

  • run_sync_soon_queue_size (int): The number of unprocessed callbacks queued via trio.lowlevel.TrioToken.run_sync_soon().

  • io_statistics (object): Some statistics from Trio’s I/O backend. This always has an attribute backend which is a string naming which operating-system-specific I/O backend is in use; the other attributes vary between backends.

The current clock

trio.lowlevel.current_clock()

Returns the 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.

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.

trio.lowlevel.add_instrument(instrument)

Start instrumenting the current run loop with the given instrument.

Parameters

instrument (trio.abc.Instrument) – The instrument to activate.

If instrument is already active, does nothing.

trio.lowlevel.remove_instrument(instrument)

Stop instrumenting the current run loop with the given instrument.

Parameters

instrument (trio.abc.Instrument) – The instrument to de-activate.

Raises

KeyError – if the instrument is not currently active. This could occur either because you never added it, or because you added it and then it raised an unhandled exception and was automatically deactivated.

And here’s the interface to implement if you want to build your own Instrument:

class trio.abc.Instrument

The interface for run loop instrumentation.

Instruments don’t have to inherit from this abstract base class, and all of these methods are optional. This class serves mostly as documentation.

after_io_wait(timeout)

Called after handling pending I/O.

Parameters

timeout (float) – The number of seconds we were willing to wait. This much time may or may not have elapsed, depending on whether any I/O was ready.

after_run()

Called just before trio.run() returns.

after_task_step(task)

Called when we return to the main run loop after a task has yielded.

Parameters

task (trio.lowlevel.Task) – The task that just ran.

before_io_wait(timeout)

Called before blocking to wait for I/O readiness.

Parameters

timeout (float) – The number of seconds we are willing to wait.

before_run()

Called at the beginning of trio.run().

before_task_step(task)

Called immediately before we resume running the given task.

Parameters

task (trio.lowlevel.Task) – The task that is about to run.

task_exited(task)

Called when the given task exits.

Parameters

task (trio.lowlevel.Task) – The finished task.

task_scheduled(task)

Called when the given task becomes runnable.

It may still be some time before it actually runs, if there are other runnable tasks ahead of it.

Parameters

task (trio.lowlevel.Task) – The task that became runnable.

task_spawned(task)

Called when the given task is created.

Parameters

task (trio.lowlevel.Task) – The new task.

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

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:

await trio.lowlevel.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
await trio.lowlevel.wait_writable(obj)

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

See wait_readable for the definition of obj.

Raises
trio.lowlevel.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 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 Stream, then use trio.StapledStream:

bidirectional_stream = trio.StapledStream(
    trio.lowlevel.FdStream(write_fd),
    trio.lowlevel.FdStream(read_fd)
)
class trio.lowlevel.FdStream(fd: int)

Bases: trio.abc.Stream

Represents a stream given the file descriptor to a pipe, TTY, etc.

fd must refer to a file that is open for reading and/or writing and supports non-blocking I/O (pipes and TTYs will work, on-disk files probably not). The returned stream takes ownership of the fd, so closing the stream will close the fd too. As with os.fdopen, you should not directly use an fd after you have wrapped it in a stream using this function.

To be used as a Trio stream, an open file must be placed in non-blocking mode. Unfortunately, this impacts all I/O that goes through the underlying open file, including I/O that uses a different file descriptor than the one that was passed to Trio. If other threads or processes are using file descriptors that are related through os.dup or inheritance across os.fork to the one that Trio is using, they are unlikely to be prepared to have non-blocking I/O semantics suddenly thrust upon them. For example, you can use FdStream(os.dup(0)) to obtain a stream for reading from standard input, but it is only safe to do so with heavy caveats: your stdin must not be shared by any other processes and you must not make any calls to synchronous methods of sys.stdin until the stream returned by FdStream is closed. See issue #174 for a discussion of the challenges involved in relaxing this restriction.

Parameters

fd (int) – The fd to be wrapped.

Returns

A new FdStream object.

Kqueue-specific API

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

trio.lowlevel.current_kqueue()
await trio.lowlevel.wait_kevent(ident, filter, abort_func)
with trio.lowlevel.monitor_kevent(ident, filter) as queue

Windows-specific API

await trio.lowlevel.WaitForSingleObject(handle)

Async and cancellable variant of WaitForSingleObject. Windows only.

Parameters

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.

trio.lowlevel.register_with_iocp(handle)
await trio.lowlevel.wait_overlapped(handle, lpOverlapped)
trio.lowlevel.current_iocp()
with trio.lowlevel.monitor_completion_key() as queue

Global state: system tasks and run-local variables

class trio.lowlevel.RunVar(name, default=<object object>)

The run-local variant of a context variable.

RunVar objects are similar to context variable objects, except that they are shared across a single call to trio.run() rather than a single task.

trio.lowlevel.spawn_system_task(async_fn, *args, name=None)

Spawn a “system” task.

System tasks have a few differences from regular tasks:

  • They don’t need an explicit nursery; instead they go into the internal “system nursery”.

  • If a system task raises an exception, then it’s converted into a TrioInternalError and all tasks are cancelled. If you write a system task, you should be careful to make sure it doesn’t crash.

  • System tasks are automatically cancelled when the main task exits.

  • By default, system tasks have KeyboardInterrupt protection enabled. If you want your task to be interruptible by control-C, then you need to use disable_ki_protection() explicitly (and come up with some plan for what to do with a KeyboardInterrupt, given that system tasks aren’t allowed to raise exceptions).

  • System tasks do not inherit context variables from their creator.

Parameters

  • async_fn – An async callable.

  • args – Positional arguments for async_fn. If you want to pass keyword arguments, use functools.partial().

  • name – The name for this task. Only used for debugging/introspection (e.g. repr(task_obj)). If this isn’t a string, spawn_system_task() will try to make it one. A common use case is if you’re wrapping a function before spawning a new task, you might pass the original function as the name= to make debugging easier.

Returns

the newly spawned task

Return type

Task

Trio tokens

class trio.lowlevel.TrioToken

An opaque object representing a single call to trio.run().

It has no public constructor; instead, see current_trio_token().

This object has two uses:

  1. It lets you re-enter the Trio run loop from external threads or signal handlers. This is the low-level primitive that trio.to_thread() and trio.from_thread use to communicate with worker threads, that trio.open_signal_receiver uses to receive notifications about signals, and so forth.

  2. Each call to trio.run() has exactly one associated TrioToken object, so you can use it to identify a particular call.

run_sync_soon(sync_fn, *args, idempotent=False)

Schedule a call to sync_fn(*args) to occur in the context of a Trio task.

This is safe to call from the main thread, from other threads, and from signal handlers. This is the fundamental primitive used to re-enter the Trio run loop from outside of it.

The call will happen “soon”, but there’s no guarantee about exactly when, and no mechanism provided for finding out when it’s happened. If you need this, you’ll have to build your own.

The call is effectively run as part of a system task (see spawn_system_task()). In particular this means that:

  • KeyboardInterrupt protection is enabled by default; if you want sync_fn to be interruptible by control-C, then you need to use disable_ki_protection() explicitly.

  • If sync_fn raises an exception, then it’s converted into a TrioInternalError and all tasks are cancelled. You should be careful that sync_fn doesn’t crash.

All calls with idempotent=False are processed in strict first-in first-out order.

If idempotent=True, then sync_fn and args must be hashable, and Trio will make a best-effort attempt to discard any call submission which is equal to an already-pending call. Trio will process these in first-in first-out order.

Any ordering guarantees apply separately to idempotent=False and idempotent=True calls; there’s no rule for how calls in the different categories are ordered with respect to each other.

Raises

trio.RunFinishedError – if the associated call to trio.run() has already exited. (Any call that doesn’t raise this error is guaranteed to be fully processed before trio.run() exits.)

trio.lowlevel.current_trio_token()

Retrieve the TrioToken for the current call to trio.run().

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 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, a function is protected if its caller is, and not if its caller isn’t; this is helpful because it 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:

@trio.lowlevel.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. 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.

@trio.lowlevel.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, 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 Lock, where a poorly-timed KeyboardInterrupt could leave the lock in an inconsistent state and cause a deadlock.

trio.lowlevel.currently_ki_protected()

Check whether the calling code has KeyboardInterrupt protection enabled.

It’s surprisingly easy to think that one’s KeyboardInterrupt protection is enabled when it isn’t, or vice-versa. This function tells you what Trio thinks of the matter, which makes it useful for asserts and unit tests.

Returns

True if protection is enabled, and False otherwise.

Return type

bool

Sleeping and waking

Wait queue abstraction

class trio.lowlevel.ParkingLot

A fair wait queue with cancellation and requeueing.

This class encapsulates the tricky parts of implementing a wait queue. It’s useful for implementing higher-level synchronization primitives like queues and locks.

In addition to the methods below, you can use len(parking_lot) to get the number of parked tasks, and if parking_lot: ... to check whether there are any parked tasks.

await park()

Park the current task until woken by a call to unpark() or unpark_all().

repark(new_lot, *, count=1)

Move parked tasks from one ParkingLot object to another.

This dequeues count tasks from one lot, and requeues them on another, preserving order. For example:

async def parker(lot):
    print("sleeping")
    await lot.park()
    print("woken")

async def main():
    lot1 = trio.lowlevel.ParkingLot()
    lot2 = trio.lowlevel.ParkingLot()
    async with trio.open_nursery() as nursery:
        nursery.start_soon(parker, lot1)
        await trio.testing.wait_all_tasks_blocked()
        assert len(lot1) == 1
        assert len(lot2) == 0
        lot1.repark(lot2)
        assert len(lot1) == 0
        assert len(lot2) == 1
        # This wakes up the task that was originally parked in lot1
        lot2.unpark()

If there are fewer than count tasks parked, then reparks as many tasks as are available and then returns successfully.

Parameters

  • new_lot (ParkingLot) – the parking lot to move tasks to.

  • count (int) – the number of tasks to move.

repark_all(new_lot)

Move all parked tasks from one ParkingLot object to another.

See repark() for details.

statistics()

Return an object containing debugging information.

Currently the following fields are defined:

  • tasks_waiting: The number of tasks blocked on this lot’s park() method.

unpark(*, count=1)

Unpark one or more tasks.

This wakes up count tasks that are blocked in park(). If there are fewer than count tasks parked, then wakes as many tasks are available and then returns successfully.

Parameters

count (int) – the number of tasks to unpark.

unpark_all()

Unpark all parked tasks.

Low-level checkpoint functions

await trio.lowlevel.checkpoint()

A pure checkpoint.

This checks for cancellation and allows other tasks to be scheduled, without otherwise blocking.

Note that the scheduler has the option of ignoring this and continuing to run the current task if it decides this is appropriate (e.g. for increased efficiency).

Equivalent to await trio.sleep(0) (which is implemented by calling checkpoint().)

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

await trio.lowlevel.checkpoint_if_cancelled()

Issue a checkpoint if the calling context has been cancelled.

Equivalent to (but potentially more efficient than):

if trio.current_deadline() == -inf:
    await trio.lowlevel.checkpoint()

This is either a no-op, or else it allow other tasks to be scheduled and then raises trio.Cancelled.

Typically used together with cancel_shielded_checkpoint().

await trio.lowlevel.cancel_shielded_checkpoint()

Introduce a schedule point, but not a cancel point.

This is not a checkpoint, but it is half of a checkpoint, and when combined with checkpoint_if_cancelled() it can make a full checkpoint.

Equivalent to (but potentially more efficient than):

with trio.CancelScope(shield=True):
    await trio.lowlevel.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 say that 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

await trio.lowlevel.wait_task_rescheduled(abort_func)

Put the current task to sleep, with cancellation support.

This is the lowest-level API for blocking in Trio. Every time a Task blocks, it does so by calling this function (usually indirectly via some higher-level API).

This is a tricky interface with no guard rails. If you can use ParkingLot or the built-in I/O wait functions instead, then you should.

Generally the way it works is that before calling this function, you make arrangements for “someone” to call reschedule() on the current task at some later point.

Then you call wait_task_rescheduled(), passing in abort_func, an “abort callback”.

(Terminology: in Trio, “aborting” is the process of attempting to interrupt a blocked task to deliver a cancellation.)

There are two possibilities for what happens next:

  1. “Someone” calls reschedule() on the current task, and wait_task_rescheduled() returns or raises whatever value or error was passed to reschedule().

  2. The call’s context transitions to a cancelled state (e.g. due to a timeout expiring). When this happens, the abort_func is called. Its interface looks like:

    def abort_func(raise_cancel):
    ...
    return trio.lowlevel.Abort.SUCCEEDED  # or FAILED

    It should attempt to clean up any state associated with this call, and in particular, arrange that reschedule() will not be called later. If (and only if!) it is successful, then it should return Abort.SUCCEEDED, in which case the task will automatically be rescheduled with an appropriate Cancelled error.

    Otherwise, it should return Abort.FAILED. This means that the task can’t be cancelled at this time, and still has to make sure that “someone” eventually calls reschedule().

    At that point there are again two possibilities. You can simply ignore the cancellation altogether: wait for the operation to complete and then reschedule and continue as normal. (For example, this is what trio.to_thread.run_sync() does if cancellation is disabled.) The other possibility is that the abort_func does succeed in cancelling the operation, but for some reason isn’t able to report that right away. (Example: on Windows, it’s possible to request that an async (“overlapped”) I/O operation be cancelled, but this request is also asynchronous – you don’t find out until later whether the operation was actually cancelled or not.) To report a delayed cancellation, then you should reschedule the task yourself, and call the raise_cancel callback passed to abort_func to raise a Cancelled (or possibly KeyboardInterrupt) exception into this task. Either of the approaches sketched below can work:

    # Option 1:
# Catch the exception from raise_cancel and inject it into the task.
# (This is what Trio does automatically for you if you return
# Abort.SUCCEEDED.)
trio.lowlevel.reschedule(task, outcome.capture(raise_cancel))

# Option 2:
# wait to be woken by "someone", and then decide whether to raise
# the error from inside the task.
outer_raise_cancel = None
def abort(inner_raise_cancel):
    nonlocal outer_raise_cancel
    outer_raise_cancel = inner_raise_cancel
    TRY_TO_CANCEL_OPERATION()
    return trio.lowlevel.Abort.FAILED
await wait_task_rescheduled(abort)
if OPERATION_WAS_SUCCESSFULLY_CANCELLED:
    # raises the error
    outer_raise_cancel()

    In any case it’s guaranteed that we only call the abort_func at most once per call to wait_task_rescheduled().

Sometimes, it’s useful to be able to share some mutable sleep-related data between the sleeping task, the abort function, and the waking task. You can use the sleeping task’s custom_sleep_data attribute to store this data, and Trio won’t touch it, except to make sure that it gets cleared when the task is rescheduled.

Warning

If your abort_func raises an error, or returns any value other than Abort.SUCCEEDED or Abort.FAILED, then Trio will crash violently. Be careful! Similarly, it is entirely possible to deadlock a Trio program by failing to reschedule a blocked task, or cause havoc by calling reschedule() too many times. Remember what we said up above about how you should use a higher-level API if at all possible?

class trio.lowlevel.Abort

enum.Enum used as the return value from abort functions.

See wait_task_rescheduled() for details.

SUCCEEDED
FAILED
trio.lowlevel.reschedule(task, next_send=<object object>)

Reschedule the given task with the given outcome.Outcome.

See wait_task_rescheduled() for the gory details.

There must be exactly one call to reschedule() for every call to wait_task_rescheduled(). (And when counting, keep in mind that returning Abort.SUCCEEDED from an abort callback is equivalent to calling reschedule() once.)

Parameters

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):
        while self._held:
            task = trio.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)
        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

trio.lowlevel.current_root_task()

Returns the current root Task.

This is the task that is the ultimate parent of all other tasks.

trio.lowlevel.current_task()

Return the Task object representing the current task.

Returns

the Task that called current_task().

Return type

Task

class trio.lowlevel.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

The nursery this task is inside (or None if this is the “init” task).

Example use case: drawing a visualization of the task tree in a debugger.

child_nurseries

The nurseries this task contains.

This is a list, with outer nurseries before inner 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.)

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.

await trio.lowlevel.permanently_detach_coroutine_object(final_outcome)

Permanently detach the current task from the Trio scheduler.

Normally, a Trio task doesn’t exit until its coroutine object exits. When you call this function, Trio acts like the coroutine object just exited and the task terminates with the given outcome. This is useful if you want to permanently switch the coroutine object over to a different coroutine runner.

When the calling coroutine enters this function it’s running under Trio, and when the function returns it’s running under the foreign coroutine runner.

You should make sure that the coroutine object has released any Trio-specific resources it has acquired (e.g. nurseries).

Parameters

final_outcome (outcome.Outcome) – Trio acts as if the current task exited with the given return value or exception.

Returns or raises whatever value or exception the new coroutine runner uses to resume the coroutine.

await trio.lowlevel.temporarily_detach_coroutine_object(abort_func)

Temporarily detach the current coroutine object from the Trio scheduler.

When the calling coroutine enters this function it’s running under Trio, and when the function returns it’s running under the foreign coroutine runner.

The Trio Task will continue to exist, but will be suspended until you use reattach_detached_coroutine_object() to resume it. In the mean time, you can use another coroutine runner to schedule the coroutine object. In fact, you have to – the function doesn’t return until the coroutine is advanced from outside.

Note that you’ll need to save the current Task object to later resume; you can retrieve it with current_task(). You can also use this Task object to retrieve the coroutine object – see Task.coro.

Parameters

abort_func – Same as for wait_task_rescheduled(), except that it must return Abort.FAILED. (If it returned Abort.SUCCEEDED, then Trio would attempt to reschedule the detached task directly without going through reattach_detached_coroutine_object(), which would be bad.) Your abort_func should still arrange for whatever the coroutine object is doing to be cancelled, and then reattach to Trio and call the raise_cancel callback, if possible.

Returns or raises whatever value or exception the new coroutine runner uses to resume the coroutine.

await trio.lowlevel.reattach_detached_coroutine_object(task, yield_value)

Reattach a coroutine object that was detached using temporarily_detach_coroutine_object().

When the calling coroutine enters this function it’s running under the foreign coroutine runner, and when the function returns it’s running under Trio.

This must be called from inside the coroutine being resumed, and yields whatever value you pass in. (Presumably you’ll pass a value that will cause the current coroutine runner to stop scheduling this task.) Then the coroutine is resumed by the Trio scheduler at the next opportunity.

Parameters

  • task (Task) – The Trio task object that the current coroutine was detached from.

  • yield_value (object) – The object to yield to the current coroutine runner.