Trio's core functionality
=========================

.. module:: trio


Entering Trio
-------------

If you want to use Trio, then the first thing you have to do is call
:func:`trio.run`:

.. autofunction:: run


General principles
------------------

.. _checkpoints:

Checkpoints
~~~~~~~~~~~

When writing code using Trio, it's very important to understand the
concept of a *checkpoint*. Many of Trio's functions act as checkpoints.

A checkpoint is two things:

1. It's a point where Trio checks for cancellation. For example, if
   the code that called your function set a timeout, and that timeout
   has expired, then the next time your function executes a checkpoint
   Trio will raise a :exc:`Cancelled` exception. See
   :ref:`cancellation` below for more details.

2. It's a point where the Trio scheduler checks its scheduling policy
   to see if it's a good time to switch to another task, and
   potentially does so. (Currently, this check is very simple: the
   scheduler always switches at every checkpoint. But `this might
   change in the future
   <https://github.com/python-trio/trio/issues/32>`__.)

When writing Trio code, you need to keep track of where your
checkpoints are. Why? First, because checkpoints require extra
scrutiny: whenever you execute a checkpoint, you need to be prepared
to handle a :exc:`Cancelled` error, or for another task to run and
`rearrange some state out from under you
<https://glyph.twistedmatrix.com/2014/02/unyielding.html>`__. And
second, because you also need to make sure that you have *enough*
checkpoints: if your code doesn't pass through a checkpoint on a
regular basis, then it will be slow to notice and respond to
cancellation and – much worse – since Trio is a cooperative
multi-tasking system where the *only* place the scheduler can switch
tasks is at checkpoints, it'll also prevent the scheduler from fairly
allocating time between different tasks and adversely effect the
response latency of all the other code running in the same
process. (Informally we say that a task that does this is "hogging the
run loop".)

So when you're doing code review on a project that uses Trio, one of
the things you'll want to think about is whether there are enough
checkpoints, and whether each one is handled correctly. Of course this
means you need a way to recognize checkpoints. How do you do that?
The underlying principle is that any operation that blocks has to be a
checkpoint. This makes sense: if an operation blocks, then it might
block for a long time, and you'll want to be able to cancel it if a
timeout expires; and in any case, while this task is blocked we want
another task to be scheduled to run so our code can make full use of
the CPU.

But if we want to write correct code in practice, then this principle
is a little too sloppy and imprecise to be useful. How do we know
which functions might block?  What if a function blocks sometimes, but
not others, depending on the arguments passed / network speed / phase
of the moon? How do we figure out where the checkpoints are when
we're stressed and sleep deprived but still want to get this code
review right, and would prefer to reserve our mental energy for
thinking about the actual logic instead of worrying about checkpoints?

.. _checkpoint-rule:

Don't worry – Trio's got your back. Since checkpoints are important
and ubiquitous, we make it as simple as possible to keep track of
them. Here are the rules:

* Regular (synchronous) functions never contain any checkpoints.

* If you call an async function provided by Trio (``await
  <something in trio>``), and it doesn't raise an exception,
  then it *always* acts as a checkpoint. (If it does raise an
  exception, it might act as a checkpoint or might not.)

  * This includes async iterators: If you write ``async for ... in <a
    trio object>``, then there will be at least one checkpoint in
    each iteration of the loop, and it will still checkpoint if the
    iterable is empty.

  * Partial exception for async context managers:
    Both the entry and exit of an ``async with`` block are
    defined as async functions; but for a
    particular type of async context manager, it's often the
    case that only one of them is able to block, which means
    only that one will act as a checkpoint. This is documented
    on a case-by-case basis.

    * :func:`trio.open_nursery` is a further exception to this rule.

* Third-party async functions / iterators / context managers can act
  as checkpoints; if you see ``await <something>`` or one of its
  friends, then that *might* be a checkpoint. So to be safe, you
  should prepare for scheduling or cancellation happening there.

The reason we distinguish between Trio functions and other functions
is that we can't make any guarantees about third party
code. Checkpoint-ness is a transitive property: if function A acts as
a checkpoint, and you write a function that calls function A, then
your function also acts as a checkpoint. If you don't, then it
isn't. So there's nothing stopping someone from writing a function
like:

.. code-block:: python

   # technically legal, but bad style:
   async def why_is_this_async():
       return 7

that never calls any of Trio's async functions. This is an async
function, but it's not a checkpoint. But why make a function async if
it never calls any async functions? It's possible, but it's a bad
idea. If you have a function that's not calling any async functions,
then you should make it synchronous. The people who use your function
will thank you, because it makes it obvious that your function is not
a checkpoint, and their code reviews will go faster.

(Remember how in the tutorial we emphasized the importance of the
:ref:`"async sandwich" <async-sandwich>`, and the way it means that
``await`` ends up being a marker that shows when you're calling a
function that calls a function that ... eventually calls one of Trio's
built-in async functions? The transitivity of async-ness is a
technical requirement that Python imposes, but since it exactly
matches the transitivity of checkpoint-ness, we're able to exploit it
to help you keep track of checkpoints. Pretty sneaky, eh?)

A slightly trickier case is a function like:

.. code-block:: python

   async def sleep_or_not(should_sleep):
       if should_sleep:
           await trio.sleep(1)
       else:
           pass

Here the function acts as a checkpoint if you call it with
``should_sleep`` set to a true value, but not otherwise. This is why
we emphasize that Trio's own async functions are *unconditional* checkpoints:
they *always* check for cancellation and check for scheduling,
regardless of what arguments they're passed. If you find an async
function in Trio that doesn't follow this rule, then it's a bug and
you should `let us know
<https://github.com/python-trio/trio/issues>`__.

Inside Trio, we're very picky about this, because Trio is the
foundation of the whole system so we think it's worth the extra effort
to make things extra predictable. It's up to you how picky you want to
be in your code. To give you a more realistic example of what this
kind of issue looks like in real life, consider this function:

.. code-block:: python

    async def recv_exactly(sock, nbytes):
        data = bytearray()
        while nbytes > 0:
            # recv() reads up to 'nbytes' bytes each time
            chunk = await sock.recv(nbytes)
            if not chunk:
                raise RuntimeError("socket unexpected closed")
            nbytes -= len(chunk)
            data += chunk
        return data

If called with an ``nbytes`` that's greater than zero, then it will
call ``sock.recv`` at least once, and ``recv`` is an async Trio
function, and thus an unconditional checkpoint. So in this case,
``recv_exactly`` acts as a checkpoint. But if we do ``await
recv_exactly(sock, 0)``, then it will immediately return an empty
buffer without executing a checkpoint. If this were a function in
Trio itself, then this wouldn't be acceptable, but you may decide you
don't want to worry about this kind of minor edge case in your own
code.

If you do want to be careful, or if you have some CPU-bound code that
doesn't have enough checkpoints in it, then it's useful to know that
``await trio.sleep(0)`` is an idiomatic way to execute a checkpoint
without doing anything else, and that
:func:`trio.testing.assert_checkpoints` can be used to test that an
arbitrary block of code contains a checkpoint.


Thread safety
~~~~~~~~~~~~~

The vast majority of Trio's API is *not* thread safe: it can only be
used from inside a call to :func:`trio.run`. This manual doesn't
bother documenting this on individual calls; unless specifically noted
otherwise, you should assume that it isn't safe to call any Trio
functions from anywhere except the Trio thread. (But :ref:`see below
<threads>` if you really do need to work with threads.)


.. _time-and-clocks:

Time and clocks
---------------

Every call to :func:`run` has an associated clock.

By default, Trio uses an unspecified monotonic clock, but this can be
changed by passing a custom clock object to :func:`run` (e.g. for
testing).

You should not assume that Trio's internal clock matches any other
clock you have access to, including the clocks of simultaneous calls
to :func:`trio.run` happening in other processes or threads!

The default clock is currently implemented as :func:`time.perf_counter`
plus a large random offset. The idea here is to catch code that
accidentally uses :func:`time.perf_counter` early, which should help keep
our options open for `changing the clock implementation later
<https://github.com/python-trio/trio/issues/33>`__, and (more importantly)
make sure you can be confident that custom clocks like
:class:`trio.testing.MockClock` will work with third-party libraries
you don't control.

.. autofunction:: current_time

.. autofunction:: sleep
.. autofunction:: sleep_until
.. autofunction:: sleep_forever

If you're a mad scientist or otherwise feel the need to take direct
control over the PASSAGE OF TIME ITSELF, then you can implement a
custom :class:`~trio.abc.Clock` class:

.. autoclass:: trio.abc.Clock
   :members:


.. _cancellation:

Cancellation and timeouts
-------------------------

Trio has a rich, composable system for cancelling work, either
explicitly or when a timeout expires.


A simple timeout example
~~~~~~~~~~~~~~~~~~~~~~~~

In the simplest case, you can apply a timeout to a block of code:

.. code-block:: python

   with trio.move_on_after(30):
       result = await do_http_get("https://...")
       print("result is", result)
   print("with block finished")

We refer to :func:`move_on_after` as creating a "cancel scope", which
contains all the code that runs inside the ``with`` block. If the HTTP
request takes more than 30 seconds to run, then it will be cancelled:
we'll abort the request and we *won't* see ``result is ...`` printed
on the console; instead we'll go straight to printing the ``with block
finished`` message.

.. note::

   Note that this is a single 30 second timeout for the entire body of
   the ``with`` statement. This is different from what you might have
   seen with other Python libraries, where timeouts often refer to
   something `more complicated
   <https://requests.kennethreitz.org/en/master/user/quickstart/#timeouts>`__. We
   think this way is easier to reason about.

How does this work? There's no magic here: Trio is built using
ordinary Python functionality, so we can't just abandon the code
inside the ``with`` block. Instead, we take advantage of Python's
standard way of aborting a large and complex piece of code: we raise
an exception.

Here's the idea: whenever you call a cancellable function like ``await
trio.sleep(...)`` or ``await sock.recv(...)`` – see :ref:`checkpoints`
– then the first thing that function does is to check if there's a
surrounding cancel scope whose timeout has expired, or otherwise been
cancelled. If so, then instead of performing the requested operation,
the function fails immediately with a :exc:`Cancelled` exception. In
this example, this probably happens somewhere deep inside the bowels
of ``do_http_get``. The exception then propagates out like any normal
exception (you could even catch it if you wanted, but that's generally
a bad idea), until it reaches the ``with move_on_after(...):``. And at
this point, the :exc:`Cancelled` exception has done its job – it's
successfully unwound the whole cancelled scope – so
:func:`move_on_after` catches it, and execution continues as normal
after the ``with`` block. And this all works correctly even if you
have nested cancel scopes, because every :exc:`Cancelled` object
carries an invisible marker that makes sure that the cancel scope that
triggered it is the only one that will catch it.


Handling cancellation
~~~~~~~~~~~~~~~~~~~~~

Pretty much any code you write using Trio needs to have some strategy
to handle :exc:`Cancelled` exceptions – even if you didn't set a
timeout, then your caller might (and probably will).

You can catch :exc:`Cancelled`, but you shouldn't! Or more precisely,
if you do catch it, then you should do some cleanup and then re-raise
it or otherwise let it continue propagating (unless you encounter an
error, in which case it's OK to let that propagate instead). To help
remind you of this fact, :exc:`Cancelled` inherits from
:exc:`BaseException`, like :exc:`KeyboardInterrupt` and
:exc:`SystemExit` do, so that it won't be caught by catch-all ``except
Exception:`` blocks.

It's also important in any long-running code to make sure that you
regularly check for cancellation, because otherwise timeouts won't
work! This happens implicitly every time you call a cancellable
operation; see :ref:`below <cancellable-primitives>` for details. If
you have a task that has to do a lot of work without any I/O, then you
can use ``await sleep(0)`` to insert an explicit cancel+schedule
point.

Here's a rule of thumb for designing good Trio-style ("trionic"?)
APIs: if you're writing a reusable function, then you shouldn't take a
``timeout=`` parameter, and instead let your caller worry about
it. This has several advantages. First, it leaves the caller's options
open for deciding how they prefer to handle timeouts – for example,
they might find it easier to work with absolute deadlines instead of
relative timeouts. If they're the ones calling into the cancellation
machinery, then they get to pick, and you don't have to worry about
it. Second, and more importantly, this makes it easier for others to
reuse your code. If you write a ``http_get`` function, and then I come
along later and write a ``log_in_to_twitter`` function that needs to
internally make several ``http_get`` calls, I don't want to have to
figure out how to configure the individual timeouts on each of those
calls – and with Trio's timeout system, it's totally unnecessary.

Of course, this rule doesn't apply to APIs that need to impose
internal timeouts. For example, if you write a ``start_http_server``
function, then you probably should give your caller some way to
configure timeouts on individual requests.


Cancellation semantics
~~~~~~~~~~~~~~~~~~~~~~

You can freely nest cancellation blocks, and each :exc:`Cancelled`
exception "knows" which block it belongs to. So long as you don't stop
it, the exception will keep propagating until it reaches the block
that raised it, at which point it will stop automatically.

Here's an example:

.. code-block:: python

   print("starting...")
   with trio.move_on_after(5):
       with trio.move_on_after(10):
           await trio.sleep(20)
           print("sleep finished without error")
       print("move_on_after(10) finished without error")
   print("move_on_after(5) finished without error")

In this code, the outer scope will expire after 5 seconds, causing the
:func:`sleep` call to return early with a :exc:`Cancelled`
exception. Then this exception will propagate through the ``with
move_on_after(10)`` line until it's caught by the ``with
move_on_after(5)`` context manager. So this code will print:

.. code-block:: none

   starting...
   move_on_after(5) finished without error

The end result is that Trio has successfully cancelled exactly the
work that was happening within the scope that was cancelled.

Looking at this, you might wonder how you can tell whether the inner
block timed out – perhaps you want to do something different, like try
a fallback procedure or report a failure to our caller. To make this
easier, :func:`move_on_after`\´s ``__enter__`` function returns an
object representing this cancel scope, which we can use to check
whether this scope caught a :exc:`Cancelled` exception:

.. code-block:: python

   with trio.move_on_after(5) as cancel_scope:
       await trio.sleep(10)
   print(cancel_scope.cancelled_caught)  # prints "True"

The ``cancel_scope`` object also allows you to check or adjust this
scope's deadline, explicitly trigger a cancellation without waiting
for the deadline, check if the scope has already been cancelled, and
so forth – see :class:`CancelScope` below for the full details.

.. _blocking-cleanup-example:

Cancellations in Trio are "level triggered", meaning that once a block
has been cancelled, *all* cancellable operations in that block will
keep raising :exc:`Cancelled`. This helps avoid some pitfalls around
resource clean-up. For example, imagine that we have a function that
connects to a remote server and sends some messages, and then cleans
up on the way out:

.. code-block:: python

   with trio.move_on_after(TIMEOUT):
       conn = make_connection()
       try:
           await conn.send_hello_msg()
       finally:
           await conn.send_goodbye_msg()

Now suppose that the remote server stops responding, so our call to
``await conn.send_hello_msg()`` hangs forever. Fortunately, we were
clever enough to put a timeout around this code, so eventually the
timeout will expire and ``send_hello_msg`` will raise
:exc:`Cancelled`. But then, in the ``finally`` block, we make another
blocking operation, which will also hang forever! At this point, if we
were using :mod:`asyncio` or another library with "edge-triggered"
cancellation, we'd be in trouble: since our timeout already fired, it
wouldn't fire again, and at this point our application would lock up
forever. But in Trio, this *doesn't* happen: the ``await
conn.send_goodbye_msg()`` call is still inside the cancelled block, so
it will also raise :exc:`Cancelled`.

Of course, if you really want to make another blocking call in your
cleanup handler, Trio will let you; it's trying to prevent you from
accidentally shooting yourself in the foot. Intentional foot-shooting
is no problem (or at least – it's not Trio's problem). To do this,
create a new scope, and set its :attr:`~CancelScope.shield`
attribute to :data:`True`:

.. code-block:: python

   with trio.move_on_after(TIMEOUT):
       conn = make_connection()
       try:
           await conn.send_hello_msg()
       finally:
           with trio.move_on_after(CLEANUP_TIMEOUT, shield=True) as cleanup_scope:
               await conn.send_goodbye_msg()

So long as you're inside a scope with ``shield = True`` set, then
you'll be protected from outside cancellations. Note though that this
*only* applies to *outside* cancellations: if ``CLEANUP_TIMEOUT``
expires then ``await conn.send_goodbye_msg()`` will still be
cancelled, and if ``await conn.send_goodbye_msg()`` call uses any
timeouts internally, then those will continue to work normally as
well. This is a pretty advanced feature that most people probably
won't use, but it's there for the rare cases where you need it.


.. _cancellable-primitives:

Cancellation and primitive operations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We've talked a lot about what happens when an operation is cancelled,
and how you need to be prepared for this whenever calling a
cancellable operation... but we haven't gone into the details about
which operations are cancellable, and how exactly they behave when
they're cancelled.

Here's the rule: if it's in the ``trio`` namespace, and you use ``await``
to call it, then it's cancellable (see :ref:`checkpoints`
above). Cancellable means:

* If you try to call it when inside a cancelled scope, then it will
  raise :exc:`Cancelled`.

* If it blocks, and while it's blocked then one of the scopes around
  it becomes cancelled, it will return early and raise
  :exc:`Cancelled`.

* Raising :exc:`Cancelled` means that the operation *did not
  happen*. If a Trio socket's ``send`` method raises :exc:`Cancelled`,
  then no data was sent. If a Trio socket's ``recv`` method raises
  :exc:`Cancelled` then no data was lost – it's still sitting in the
  socket receive buffer waiting for you to call ``recv`` again. And so
  forth.

There are a few idiosyncratic cases where external constraints make it
impossible to fully implement these semantics. These are always
documented. There is also one systematic exception:

* Async cleanup operations – like ``__aexit__`` methods or async close
  methods – are cancellable just like anything else *except* that if
  they are cancelled, they still perform a minimum level of cleanup
  before raising :exc:`Cancelled`.

For example, closing a TLS-wrapped socket normally involves sending a
notification to the remote peer, so that they can be cryptographically
assured that you really meant to close the socket, and your connection
wasn't just broken by a man-in-the-middle attacker. But handling this
robustly is a bit tricky. Remember our :ref:`example
<blocking-cleanup-example>` above where the blocking
``send_goodbye_msg`` caused problems? That's exactly how closing a TLS
socket works: if the remote peer has disappeared, then our code may
never be able to actually send our shutdown notification, and it would
be nice if it didn't block forever trying. Therefore, the method for
closing a TLS-wrapped socket will *try* to send that notification –
and if it gets cancelled, then it will give up on sending the message,
but *will* still close the underlying socket before raising
:exc:`Cancelled`, so at least you don't leak that resource.


Cancellation API details
~~~~~~~~~~~~~~~~~~~~~~~~

:func:`move_on_after` and all the other cancellation facilities provided
by Trio are ultimately implemented in terms of :class:`CancelScope`
objects.

.. autoclass:: trio.CancelScope

   .. autoattribute:: deadline

   .. autoattribute:: relative_deadline

   .. autoattribute:: shield

   .. automethod:: is_relative()

   .. automethod:: cancel()

   .. attribute:: cancelled_caught

      Readonly :class:`bool`. Records whether this scope caught a
      :exc:`~trio.Cancelled` exception. This requires two things: (1)
      the ``with`` block exited with a :exc:`~trio.Cancelled`
      exception, and (2) this scope is the one that was responsible
      for triggering this :exc:`~trio.Cancelled` exception.

   .. autoattribute:: cancel_called

Often there is no need to create :class:`CancelScope` object. Trio
already includes :attr:`~trio.Nursery.cancel_scope` attribute in a
task-related :class:`Nursery` object. We will cover nurseries later in
the manual.

Trio also provides several convenience functions for the common
situation of just wanting to impose a timeout on some code:

.. autofunction:: move_on_after
   :with: cancel_scope

.. autofunction:: move_on_at
   :with: cancel_scope

.. autofunction:: fail_after
   :with: cancel_scope

.. autofunction:: fail_at
   :with: cancel_scope

Cheat sheet
+++++++++++

* If you want to impose a timeout on a function, but you don't care
  whether it timed out or not:

  .. code-block:: python

     with trio.move_on_after(TIMEOUT):
         await do_whatever()
     # carry on!

* If you want to impose a timeout on a function, and then do some
  recovery if it timed out:

  .. code-block:: python

     with trio.move_on_after(TIMEOUT) as cancel_scope:
         await do_whatever()
     if cancel_scope.cancelled_caught:
         # The operation timed out, try something else
         try_to_recover()

* If you want to impose a timeout on a function, and then if it times
  out then just give up and raise an error for your caller to deal
  with:

  .. code-block:: python

     with trio.fail_after(TIMEOUT):
         await do_whatever()

It's also possible to check what the current effective deadline is,
which is sometimes useful:

.. autofunction:: current_effective_deadline

.. _tasks:

Tasks let you do multiple things at once
----------------------------------------

One of Trio's core design principles is: *no implicit
concurrency*. Every function executes in a straightforward,
top-to-bottom manner, finishing each operation before moving on to the
next – *like Guido intended*.

But, of course, the entire point of an async library is to let you do
multiple things at once. The one and only way to do that in Trio is
through the task spawning interface. So if you want your program to
walk *and* chew gum, this is the section for you.


Nurseries and spawning
~~~~~~~~~~~~~~~~~~~~~~

Most libraries for concurrent programming let you start new child
tasks (or threads, or whatever) willy-nilly, whenever and where-ever
you feel like it. Trio is a bit different: you can't start a child
task unless you're prepared to be a responsible parent. The way you
demonstrate your responsibility is by creating a nursery:

.. code-block:: python

   async with trio.open_nursery() as nursery:
       ...

And once you have a reference to a nursery object, you can start
children in that nursery:

.. code-block:: python

   async def child():
       ...

   async def parent():
       async with trio.open_nursery() as nursery:
           # Make two concurrent calls to child()
           nursery.start_soon(child)
           nursery.start_soon(child)

This means that tasks form a tree: when you call :func:`run`, then
this creates an initial task, and all your other tasks will be
children, grandchildren, etc. of the initial task.

Essentially, the body of the ``async with`` block acts like an initial
task that's running inside the nursery, and then each call to
``nursery.start_soon`` adds another task that runs in parallel. Two
crucial things to keep in mind:

* If any task inside the nursery finishes with an unhandled exception,
  then the nursery immediately cancels all the tasks inside the
  nursery.

* Since all of the tasks are running concurrently inside the ``async
  with`` block, the block does not exit until *all* tasks have
  completed. If you've used other concurrency frameworks, then you can
  think of it as, the de-indentation at the end of the ``async with``
  automatically "joins" (waits for) all of the tasks in the nursery.

* Once all the tasks have finished, then:

  * The nursery is marked as "closed", meaning that no new tasks can
    be started inside it.

  * Any unhandled exceptions are re-raised inside the parent task, grouped into a
    single :exc:`BaseExceptionGroup` or :exc:`ExceptionGroup` exception.

Since all tasks are descendents of the initial task, one consequence
of this is that :func:`run` can't finish until all tasks have
finished.

.. note::

   A return statement will not cancel the nursery if it still has tasks running:

   .. code-block:: python

     async def main():
         async with trio.open_nursery() as nursery:
             nursery.start_soon(trio.sleep, 5)
             return

     trio.run(main)

   This code will wait 5 seconds (for the child task to finish), and then return.

Child tasks and cancellation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In Trio, child tasks inherit the parent nursery's cancel scopes. So in
this example, both the child tasks will be cancelled when the timeout
expires:

.. code-block:: python

   with trio.move_on_after(TIMEOUT):
       async with trio.open_nursery() as nursery:
           nursery.start_soon(child1)
           nursery.start_soon(child2)

Note that what matters here is the scopes that were active when
:func:`open_nursery` was called, *not* the scopes active when
``start_soon`` is called. So for example, the timeout block below does
nothing at all:

.. code-block:: python

   async with trio.open_nursery() as nursery:
       with trio.move_on_after(TIMEOUT):  # don't do this!
           nursery.start_soon(child)

Why is this so? Well, ``start_soon()`` returns as soon as it has scheduled the new task to start running. The flow of execution in the parent then continues on to exit the ``with trio.move_on_after(TIMEOUT):`` block, at which point Trio forgets about the timeout entirely. In order for the timeout to apply to the child task, Trio must be able to tell that its associated cancel scope will stay open for at least as long as the child task is executing. And Trio can only know that for sure if the cancel scope block is outside the nursery block.

You might wonder why Trio can't just remember "this task should be cancelled in ``TIMEOUT`` seconds", even after the ``with trio.move_on_after(TIMEOUT):`` block is gone. The reason has to do with :ref:`how cancellation is implemented <cancellation>`. Recall that cancellation is represented by a `Cancelled` exception, which eventually needs to be caught by the cancel scope that caused it. (Otherwise, the exception would take down your whole program!) In order to be able to cancel the child tasks, the cancel scope has to be able to "see" the `Cancelled` exceptions that they raise -- and those exceptions come out of the ``async with open_nursery()`` block, not out of the call to ``start_soon()``.

If you want a timeout to apply to one task but not another, then you need to put the cancel scope in that individual task's function -- ``child()``, in this example.

.. _exceptiongroups:

Errors in multiple child tasks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Normally, in Python, only one thing happens at a time, which means
that only one thing can go wrong at a time. Trio has no such
limitation. Consider code like:

.. code-block:: python

    async def broken1():
        d = {}
        return d["missing"]

    async def broken2():
        seq = range(10)
        return seq[20]

    async def parent():
        async with trio.open_nursery() as nursery:
            nursery.start_soon(broken1)
            nursery.start_soon(broken2)

``broken1`` raises ``KeyError``. ``broken2`` raises
``IndexError``. Obviously ``parent`` should raise some error, but
what? The answer is that both exceptions are grouped in an :exc:`ExceptionGroup`.
:exc:`ExceptionGroup` and its parent class :exc:`BaseExceptionGroup` are used to
encapsulate multiple exceptions being raised at once.

To catch individual exceptions encapsulated in an exception group, the ``except*``
clause was introduced in Python 3.11 (:pep:`654`). Here's how it works:

.. code-block:: python

    try:
        async with trio.open_nursery() as nursery:
            nursery.start_soon(broken1)
            nursery.start_soon(broken2)
    except* KeyError as excgroup:
        for exc in excgroup.exceptions:
            ...  # handle each KeyError
    except* IndexError as excgroup:
        for exc in excgroup.exceptions:
            ...  # handle each IndexError

If you want to reraise exceptions, or raise new ones, you can do so, but be aware that
exceptions raised in ``except*`` sections will be raised together in a new exception
group.

But what if you can't use Python 3.11, and therefore ``except*``, just yet?
The same exceptiongroup_ library which backports `ExceptionGroup`  also lets
you approximate this behavior with exception handler callbacks:

.. code-block:: python

    from exceptiongroup import catch

    def handle_keyerrors(excgroup):
        for exc in excgroup.exceptions:
            ...  # handle each KeyError

    def handle_indexerrors(excgroup):
        for exc in excgroup.exceptions:
            ...  # handle each IndexError

    with catch({
        KeyError: handle_keyerrors,
        IndexError: handle_indexerrors
    }):
        async with trio.open_nursery() as nursery:
            nursery.start_soon(broken1)
            nursery.start_soon(broken2)

The semantics for the handler functions are equal to ``except*`` blocks, except for
setting local variables. If you need to set local variables, you need to declare them
inside the handler function(s) with the ``nonlocal`` keyword:

.. code-block:: python

    def handle_keyerrors(excgroup):
        nonlocal myflag
        myflag = True

    myflag = False
    with catch({KeyError: handle_keyerrors}):
        async with trio.open_nursery() as nursery:
            nursery.start_soon(broken1)

.. _handling_exception_groups:

Designing for multiple errors
+++++++++++++++++++++++++++++

Structured concurrency is still a young design pattern, but there are a few patterns
we've identified for how you (or your users) might want to handle groups of exceptions.
Note that the final pattern, simply raising an `ExceptionGroup`, is the most common -
and nurseries automatically do that for you.

**First**, you might want to 'defer to' a particular exception type, raising just that if
there is any such instance in the group.  For example: `KeyboardInterrupt` has a clear
meaning for the surrounding code, could reasonably take priority over errors of other
types, and whether you have one or several of them doesn't really matter.

This pattern can often be implemented using a decorator or a context manager, such
as :func:`trio_util.multi_error_defer_to` or :func:`trio_util.defer_to_cancelled`.
Note however that re-raising a 'leaf' exception will discard whatever part of the
traceback is attached to the `ExceptionGroup` itself, so we don't recommend this for
errors that will be presented to humans.

..
    TODO: what about `Cancelled`?  It's relevantly similar to `KeyboardInterrupt`,
    but if you have multiple Cancelleds destined for different scopes, it seems
    like it might be bad to abandon all-but-one of those - we might try to execute
    some more code which then itself gets cancelled again, and incur more cleanup.
    That's only a mild inefficiency though, and the semantics are fine overall.

**Second**, you might want to treat the concurrency inside your code as an implementation
detail which is hidden from your users - for example, abstracting a protocol which
involves sending and receiving data to a simple receive-only interface, or implementing
a context manager which maintains some background tasks for the length of the
``async with`` block.

The simple option here is to ``raise MySpecificError from group``, allowing users to
handle your library-specific error.  This is simple and reliable, but doesn't completely
hide the nursery.  *Do not* unwrap single exceptions if there could ever be multiple
exceptions though; that always ends in latent bugs and then tears.

The more complex option is to ensure that only one exception can in fact happen at a time.
This is *very hard*, for example you'll need to handle `KeyboardInterrupt` somehow, and
we strongly recommend having a ``raise PleaseReportBug from group`` fallback just in case
you get a group containing more than one exception.
This is useful when writing a context manager which starts some background tasks, and then
yields to user code which effectively runs 'inline' in the body of the nursery block.
In this case, the background tasks can be wrapped with e.g. the `outcome
<https://pypi.org/project/outcome/>`__ library to ensure that only one exception
can be raised (from end-user code); and then you can either ``raise SomeInternalError``
if a background task failed, or unwrap the user exception if that was the only error.

..
    For more on this pattern, see https://github.com/python-trio/trio/issues/2929
    and the linked issue on trio-websocket.  We may want to provide a nursery mode
    which handles this automatically; it's annoying but not too complicated and
    seems like it might be a good feature to ship for such cases.

**Third and most often**, the existence of a nursery in your code is not just an
implementation detail, and callers *should* be prepared to handle multiple exceptions
in the form of an `ExceptionGroup`, whether with ``except*`` or manual inspection
or by just letting it propagate to *their* callers.  Because this is so common,
it's nurseries' default behavior and you don't need to do anything.

.. _strict_exception_groups:

Historical Note: "non-strict" ExceptionGroups
+++++++++++++++++++++++++++++++++++++++++++++

In early versions of Trio, the ``except*`` syntax hadn't be dreamt up yet, and we
hadn't worked with structured concurrency for long or in large codebases.
As a concession to convenience, some APIs would therefore raise single exceptions,
and only wrap concurrent exceptions in the old ``trio.MultiError`` type if there
were two or more.

Unfortunately, the results were not good: calling code often didn't realize that
some function *could* raise a ``MultiError``, and therefore handle only the common
case - with the result that things would work well in testing, and then crash under
heavier load (typically in production).  `asyncio.TaskGroup` learned from this
experience and *always* wraps errors into an `ExceptionGroup`, as does ``anyio``,
and as of Trio 0.25 that's our default behavior too.

We currently support a compatibility argument ``strict_exception_groups=False`` to
`trio.run` and `trio.open_nursery`, which restores the old behavior (although
``MultiError`` itself has been fully removed).  We strongly advise against it for
new code, and encourage existing uses to migrate - we consider the option deprecated
and plan to remove it after a period of documented and then runtime warnings.

.. _exceptiongroup: https://pypi.org/project/exceptiongroup/

Spawning tasks without becoming a parent
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Sometimes it doesn't make sense for the task that starts a child to
take on responsibility for watching it. For example, a server task may
want to start a new task for each connection, but it can't listen for
connections and supervise children at the same time.

The solution here is simple once you see it: there's no requirement
that a nursery object stay in the task that created it! We can write
code like this:

.. code-block:: python

   async def new_connection_listener(handler, nursery):
       while True:
           conn = await get_new_connection()
           nursery.start_soon(handler, conn)

   async def server(handler):
       async with trio.open_nursery() as nursery:
           nursery.start_soon(new_connection_listener, handler, nursery)

Notice that ``server`` opens a nursery and passes it to
``new_connection_listener``, and then ``new_connection_listener`` is
able to start new tasks as "siblings" of itself. Of course, in this
case, we could just as well have written:

.. code-block:: python

   async def server(handler):
       async with trio.open_nursery() as nursery:
           while True:
               conn = await get_new_connection()
               nursery.start_soon(handler, conn)

\...but sometimes things aren't so simple, and this trick comes in
handy.

One thing to remember, though: cancel scopes are inherited from the
nursery, **not** from the task that calls ``start_soon``. So in this
example, the timeout does *not* apply to ``child`` (or to anything
else):

.. code-block:: python

   async def do_spawn(nursery):
       with trio.move_on_after(TIMEOUT):  # don't do this, it has no effect
           nursery.start_soon(child)

   async with trio.open_nursery() as nursery:
       nursery.start_soon(do_spawn, nursery)


Custom supervisors
~~~~~~~~~~~~~~~~~~

The default cleanup logic is often sufficient for simple cases, but
what if you want a more sophisticated supervisor? For example, maybe
you have `Erlang envy <http://learnyousomeerlang.com/supervisors>`__
and want features like automatic restart of crashed tasks. Trio itself
doesn't provide these kinds of features, but you can build them on
top; Trio's goal is to enforce basic hygiene and then get out of your
way. (Specifically: Trio won't let you build a supervisor that exits
and leaves orphaned tasks behind, and if you have an unhandled
exception due to bugs or laziness then Trio will make sure they
propagate.) And then you can wrap your fancy supervisor up in a
library and put it on PyPI, because supervisors are tricky and there's
no reason everyone should have to write their own.

For example, here's a function that takes a list of functions, runs
them all concurrently, and returns the result from the one that
finishes first:

.. code-block:: python

   async def race(*async_fns):
       if not async_fns:
           raise ValueError("must pass at least one argument")

       winner = None

       async def jockey(async_fn, cancel_scope):
           nonlocal winner
           winner = await async_fn()
           cancel_scope.cancel()

       async with trio.open_nursery() as nursery:
           for async_fn in async_fns:
               nursery.start_soon(jockey, async_fn, nursery.cancel_scope)

       return winner

This works by starting a set of tasks which each try to run their
function. As soon as the first function completes its execution, the task will set the nonlocal variable ``winner``
from the outer scope to the result of the function, and cancel the other tasks using the passed in cancel scope. Once all tasks
have been cancelled (which exits the nursery block), the variable ``winner`` will be returned.

Here if one or more of the racing functions raises an unhandled
exception then Trio's normal handling kicks in: it cancels the others
and then propagates the exception. If you want different behavior, you
can get that by adding a ``try`` block to the ``jockey`` function to
catch exceptions and handle them however you like.


Task-related API details
~~~~~~~~~~~~~~~~~~~~~~~~

The nursery API
+++++++++++++++

.. autofunction:: open_nursery
   :async-with: nursery


.. autoclass:: Nursery()
   :members: child_tasks, parent_task

   .. automethod:: start(async_fn, *args, name = None)

   .. automethod:: start_soon(async_fn, *args, name = None)

.. attribute:: TASK_STATUS_IGNORED
   :type: TaskStatus

   See :meth:`Nursery.start`.

.. autoclass:: TaskStatus(Protocol[StatusT])
   :members:

.. _task-local-storage:

Task-local storage
------------------

Suppose you're writing a server that responds to network requests, and
you log some information about each request as you process it. If the
server is busy and there are multiple requests being handled at the
same time, then you might end up with logs like this:

.. code-block:: none

   Request handler started
   Request handler started
   Request handler finished
   Request handler finished

In this log, it's hard to know which lines came from which
request. (Did the request that started first also finish first, or
not?) One way to solve this is to assign each request a unique
identifier, and then include this identifier in each log message:

.. code-block:: none

   request 1: Request handler started
   request 2: Request handler started
   request 2: Request handler finished
   request 1: Request handler finished

This way we can see that request 1 was slow: it started before request
2 but finished afterwards. (You can also get `much fancier
<https://opentracing.io/docs/>`__, but this is enough for an
example.)

Now, here's the problem: how does the logging code know what the
request identifier is? One approach would be to explicitly pass it
around to every function that might want to emit logs... but that's
basically every function, because you never know when you might need
to add a ``log.debug(...)`` call to some utility function buried deep
in the call stack, and when you're in the middle of a debugging a
nasty problem that last thing you want is to have to stop first and
refactor everything to pass through the request identifier! Sometimes
this is the right solution, but other times it would be much more
convenient if we could store the identifier in a global variable, so
that the logging function could look it up whenever it needed
it. Except... a global variable can only have one value at a time, so
if we have multiple handlers running at once then this isn't going to
work. What we need is something that's *like* a global variable, but
that can have different values depending on which request handler is
accessing it.

To solve this problem, Python has a module in the standard
library: :mod:`contextvars`.

Here's a toy example demonstrating how to use :mod:`contextvars`:

.. literalinclude:: reference-core/contextvar-example.py

Example output (yours may differ slightly):

.. code-block:: none

   request 1: Request handler started
   request 2: Request handler started
   request 0: Request handler started
   request 2: Helper task a started
   request 2: Helper task b started
   request 1: Helper task a started
   request 1: Helper task b started
   request 0: Helper task b started
   request 0: Helper task a started
   request 2: Helper task b finished
   request 2: Helper task a finished
   request 2: Request received finished
   request 0: Helper task a finished
   request 1: Helper task a finished
   request 1: Helper task b finished
   request 1: Request received finished
   request 0: Helper task b finished
   request 0: Request received finished

For more information, read the
`contextvars docs <https://docs.python.org/3/library/contextvars.html>`__.


.. _synchronization:

Synchronizing and communicating between tasks
---------------------------------------------

Trio provides a standard set of synchronization and inter-task
communication primitives. These objects' APIs are generally modelled
off of the analogous classes in the standard library, but with some
differences.


Blocking and non-blocking methods
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The standard library synchronization primitives have a variety of
mechanisms for specifying timeouts and blocking behavior, and of
signaling whether an operation returned due to success versus a
timeout.

In Trio, we standardize on the following conventions:

* We don't provide timeout arguments. If you want a timeout, then use
  a cancel scope.

* For operations that have a non-blocking variant, the blocking and
  non-blocking variants are different methods with names like ``X``
  and ``X_nowait``, respectively. (This is similar to
  :class:`queue.Queue`, but unlike most of the classes in
  :mod:`threading`.) We like this approach because it allows us to
  make the blocking version async and the non-blocking version sync.

* When a non-blocking method cannot succeed (the channel is empty, the
  lock is already held, etc.), then it raises :exc:`trio.WouldBlock`.
  There's no equivalent to the :exc:`queue.Empty` versus
  :exc:`queue.Full` distinction – we just have the one exception that
  we use consistently.


Fairness
~~~~~~~~

These classes are all guaranteed to be "fair", meaning that when it
comes time to choose who will be next to acquire a lock, get an item
from a queue, etc., then it always goes to the task which has been
waiting longest. It's `not entirely clear
<https://github.com/python-trio/trio/issues/54>`__ whether this is the
best choice, but for now that's how it works.

As an example of what this means, here's a small program in which two
tasks compete for a lock. Notice that the task which releases the lock
always immediately attempts to re-acquire it, before the other task has
a chance to run. (And remember that we're doing cooperative
multi-tasking here, so it's actually *deterministic* that the task
releasing the lock will call :meth:`~Lock.acquire` before the other
task wakes up; in Trio releasing a lock is not a checkpoint.)  With
an unfair lock, this would result in the same task holding the lock
forever and the other task being starved out. But if you run this,
you'll see that the two tasks politely take turns:

.. code-block:: python

   # fairness-demo.py

   import trio

   async def loopy_child(number, lock):
       while True:
           async with lock:
               print(f"Child {number} has the lock!")
               await trio.sleep(0.5)

   async def main():
       async with trio.open_nursery() as nursery:
           lock = trio.Lock()
           nursery.start_soon(loopy_child, 1, lock)
           nursery.start_soon(loopy_child, 2, lock)

   trio.run(main)


Broadcasting an event with :class:`Event`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: Event
   :members:

.. autoclass:: EventStatistics
   :members:

.. _channels:

Using channels to pass values between tasks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

*Channels* allow you to safely and conveniently send objects between
different tasks. They're particularly useful for implementing
producer/consumer patterns.

The core channel API is defined by the abstract base classes
:class:`trio.abc.SendChannel` and :class:`trio.abc.ReceiveChannel`.
You can use these to implement your own custom channels, that do
things like pass objects between processes or over the network. But in
many cases, you just want to pass objects between different tasks
inside a single process, and for that you can use
:func:`trio.open_memory_channel`:

.. autofunction:: open_memory_channel(max_buffer_size)

.. note:: If you've used the :mod:`threading` or :mod:`asyncio`
   modules, you may be familiar with :class:`queue.Queue` or
   :class:`asyncio.Queue`. In Trio, :func:`open_memory_channel` is
   what you use when you're looking for a queue. The main difference
   is that Trio splits the classic queue interface up into two
   objects. The advantage of this is that it makes it possible to put
   the two ends in different processes without rewriting your code,
   and that we can close the two sides separately.

`MemorySendChannel` and `MemoryReceiveChannel` also expose several
more features beyond the core channel interface:

.. autoclass:: MemorySendChannel
   :members:

.. autoclass:: MemoryReceiveChannel
   :members:

.. autoclass:: MemoryChannelStatistics
   :members:

A simple channel example
++++++++++++++++++++++++

Here's a simple example of how to use memory channels:

.. literalinclude:: reference-core/channels-simple.py

If you run this, it prints:

.. code-block:: none

   got value "message 0"
   got value "message 1"
   got value "message 2"

And then it hangs forever. (Use control-C to quit.)


.. _channel-shutdown:

Clean shutdown with channels
++++++++++++++++++++++++++++

Of course we don't generally like it when programs hang. What
happened? The problem is that the producer sent 3 messages and then
exited, but the consumer has no way to tell that the producer is gone:
for all it knows, another message might be coming along any moment. So
it hangs forever waiting for the 4th message.

Here's a new version that fixes this: it produces the same output as
the previous version, and then exits cleanly. The only change is the
addition of ``async with`` blocks inside the producer and consumer:

.. literalinclude:: reference-core/channels-shutdown.py
   :emphasize-lines: 12,18

The really important thing here is the producer's ``async with`` .
When the producer exits, this closes the ``send_channel``, and that
tells the consumer that no more messages are coming, so it can cleanly
exit its ``async for`` loop. Then the program shuts down because both
tasks have exited.

We also added an ``async with`` to the consumer. This isn't as
important, but it can help us catch mistakes or other problems. For
example, suppose that the consumer exited early for some reason –
maybe because of a bug. Then the producer would be sending messages
into the void, and might get stuck indefinitely. But, if the consumer
closes its ``receive_channel``, then the producer will get a
:exc:`BrokenResourceError` to alert it that it should stop sending
messages because no-one is listening.

If you want to see the effect of the consumer exiting early, try
adding a ``break`` statement to the ``async for`` loop – you should
see a :exc:`BrokenResourceError` from the producer.


.. _channel-mpmc:

Managing multiple producers and/or multiple consumers
+++++++++++++++++++++++++++++++++++++++++++++++++++++

You can also have multiple producers, and multiple consumers, all
sharing the same channel. However, this makes shutdown a little more
complicated.

For example, consider this naive extension of our previous example,
now with two producers and two consumers:

.. literalinclude:: reference-core/channels-mpmc-broken.py

The two producers, A and B, send 3 messages apiece. These are then
randomly distributed between the two consumers, X and Y. So we're
hoping to see some output like:

.. code-block:: none

   consumer Y got value '0 from producer B'
   consumer X got value '0 from producer A'
   consumer Y got value '1 from producer A'
   consumer Y got value '1 from producer B'
   consumer X got value '2 from producer B'
   consumer X got value '2 from producer A'

However, on most runs, that's not what happens – the first part of the
output is OK, and then when we get to the end the program crashes with
:exc:`ClosedResourceError`. If you run the program a few times, you'll
see that sometimes the traceback shows ``send`` crashing, and other
times it shows ``receive`` crashing, and you might even find that on
some runs it doesn't crash at all.

Here's what's happening: suppose that producer A finishes first. It
exits, and its ``async with`` block closes the ``send_channel``. But
wait! Producer B was still using that ``send_channel``... so the next
time B calls ``send``, it gets a :exc:`ClosedResourceError`.

Sometimes, though if we're lucky, the two producers might finish at
the same time (or close enough), so they both make their last ``send``
before either of them closes the ``send_channel``.

But, even if that happens, we're not out of the woods yet! After the
producers exit, the two consumers race to be the first to notice that
the ``send_channel`` has closed. Suppose that X wins the race. It
exits its ``async for`` loop, then exits the ``async with`` block...
and closes the ``receive_channel``, while Y is still using it. Again,
this causes a crash.

We could avoid this by using some complicated bookkeeping to make sure
that only the *last* producer and the *last* consumer close their
channel endpoints... but that would be tiresome and fragile.
Fortunately, there's a better way! Here's a fixed version of our
program above:

.. literalinclude:: reference-core/channels-mpmc-fixed.py
   :emphasize-lines: 8, 10, 11, 13, 14

This example demonstrates using the `MemorySendChannel.clone` and
`MemoryReceiveChannel.clone` methods. What these do is create copies
of our endpoints, that act just like the original – except that they
can be closed independently. And the underlying channel is only closed
after *all* the clones have been closed. So this completely solves our
problem with shutdown, and if you run this program, you'll see it
print its six lines of output and then exits cleanly.

Notice a small trick we use: the code in ``main`` creates clone
objects to pass into all the child tasks, and then closes the original
objects using ``async with``. Another option is to pass clones into
all-but-one of the child tasks, and then pass the original object into
the last task, like:

.. code-block:: python

   # Also works, but is more finicky:
   send_channel, receive_channel = trio.open_memory_channel(0)
   nursery.start_soon(producer, "A", send_channel.clone())
   nursery.start_soon(producer, "B", send_channel)
   nursery.start_soon(consumer, "X", receive_channel.clone())
   nursery.start_soon(consumer, "Y", receive_channel)

But this is more error-prone, especially if you use a loop to spawn
the producers/consumers.

Just make sure that you don't write:

.. code-block:: python

   # Broken, will cause program to hang:
   send_channel, receive_channel = trio.open_memory_channel(0)
   nursery.start_soon(producer, "A", send_channel.clone())
   nursery.start_soon(producer, "B", send_channel.clone())
   nursery.start_soon(consumer, "X", receive_channel.clone())
   nursery.start_soon(consumer, "Y", receive_channel.clone())

Here we pass clones into the tasks, but never close the original
objects. That means we have 3 send channel objects (the original + two
clones), but we only close 2 of them, so the consumers will hang
around forever waiting for that last one to be closed.


.. _channel-buffering:

Buffering in channels
+++++++++++++++++++++

When you call :func:`open_memory_channel`, you have to specify how
many values can be buffered internally in the channel. If the buffer
is full, then any task that calls :meth:`~trio.abc.SendChannel.send`
will stop and wait for another task to call
:meth:`~trio.abc.ReceiveChannel.receive`. This is useful because it
produces *backpressure*: if the channel producers are running faster
than the consumers, then it forces the producers to slow down.

You can disable buffering entirely, by doing
``open_memory_channel(0)``. In that case any task that calls
:meth:`~trio.abc.SendChannel.send` will wait until another task calls
:meth:`~trio.abc.ReceiveChannel.receive`, and vice versa. This is similar to
how channels work in the `classic Communicating Sequential Processes
model <https://en.wikipedia.org/wiki/Channel_(programming)>`__, and is
a reasonable default if you aren't sure what size buffer to use.
(That's why we used it in the examples above.)

At the other extreme, you can make the buffer unbounded by using
``open_memory_channel(math.inf)``. In this case,
:meth:`~trio.abc.SendChannel.send` *always* returns immediately.
Normally, this is a bad idea. To see why, consider a program where the
producer runs more quickly than the consumer:

.. literalinclude:: reference-core/channels-backpressure.py

If you run this program, you'll see output like:

.. code-block:: none

   Sent message: 0
   Received message: 0
   Sent message: 1
   Sent message: 2
   Sent message: 3
   Sent message: 4
   Sent message: 5
   Sent message: 6
   Sent message: 7
   Sent message: 8
   Sent message: 9
   Received message: 1
   Sent message: 10
   Sent message: 11
   Sent message: 12
   ...

On average, the producer sends ten messages per second, but the
consumer only calls ``receive`` once per second. That means that each
second, the channel's internal buffer has to grow to hold an extra
nine items. After a minute, the buffer will have ~540 items in it;
after an hour, that grows to ~32,400. Eventually, the program will run
out of memory. And well before we run out of memory, our latency on
handling individual messages will become abysmal. For example, at the
one minute mark, the producer is sending message ~600, but the
consumer is still processing message ~60. Message 600 will have to sit
in the channel for ~9 minutes before the consumer catches up and
processes it.

Now try replacing ``open_memory_channel(math.inf)`` with
``open_memory_channel(0)``, and run it again. We get output like:

.. code-block:: none

   Sent message: 0
   Received message: 0
   Received message: 1
   Sent message: 1
   Received message: 2
   Sent message: 2
   Sent message: 3
   Received message: 3
   ...

Now the ``send`` calls wait for the ``receive`` calls to finish, which
forces the producer to slow down to match the consumer's speed. (It
might look strange that some values are reported as "Received" before
they're reported as "Sent"; this happens because the actual
send/receive happen at the same time, so which line gets printed first
is random.)

Now, let's try setting a small but nonzero buffer size, like
``open_memory_channel(3)``. what do you think will happen?

I get:

.. code-block:: none

   Sent message: 0
   Received message: 0
   Sent message: 1
   Sent message: 2
   Sent message: 3
   Received message: 1
   Sent message: 4
   Received message: 2
   Sent message: 5
   ...

So you can see that the producer runs ahead by 3 messages, and then
stops to wait: when the consumer reads message 1, it sends message 4,
then when the consumer reads message 2, it sends message 5, and so on.
Once it reaches the steady state, this version acts just like our
previous version where we set the buffer size to 0, except that it
uses a bit more memory and each message sits in the buffer for a bit
longer before being processed (i.e., the message latency is higher).

Of course real producers and consumers are usually more complicated
than this, and in some situations, a modest amount of buffering might
improve throughput. But too much buffering wastes memory and increases
latency, so if you want to tune your application you should experiment
to see what value works best for you.

**Why do we even support unbounded buffers then?** Good question!
Despite everything we saw above, there are times when you actually do
need an unbounded buffer. For example, consider a web crawler that
uses a channel to keep track of all the URLs it still wants to crawl.
Each crawler runs a loop where it takes a URL from the channel,
fetches it, checks the HTML for outgoing links, and then adds the new
URLs to the channel. This creates a *circular flow*, where each
consumer is also a producer. In this case, if your channel buffer gets
full, then the crawlers will block when they try to add new URLs to
the channel, and if all the crawlers got blocked, then they aren't
taking any URLs out of the channel, so they're stuck forever in a
deadlock. Using an unbounded channel avoids this, because it means
that :meth:`~trio.abc.SendChannel.send` never blocks.


Lower-level synchronization primitives
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Personally, I find that events and channels are usually enough to
implement most things I care about, and lead to easier to read code
than the lower-level primitives discussed in this section. But if you
need them, they're here. (If you find yourself reaching for these
because you're trying to implement a new higher-level synchronization
primitive, then you might also want to check out the facilities in
:mod:`trio.lowlevel` for a more direct exposure of Trio's underlying
synchronization logic. All of classes discussed in this section are
implemented on top of the public APIs in :mod:`trio.lowlevel`; they
don't have any special access to Trio's internals.)

.. autoclass:: CapacityLimiter
   :members:

.. autoclass:: Semaphore
   :members:

.. We have to use :inherited-members: here because all the actual lock
   methods are stashed in _LockImpl. Weird side-effect of having both
   Lock and StrictFIFOLock, but wanting both to be marked Final so
   neither can inherit from the other.

.. autoclass:: Lock
   :members:
   :inherited-members:

.. autoclass:: StrictFIFOLock
   :members:

.. autoclass:: Condition
   :members:

These primitives return statistics objects that can be inspected.

.. autoclass:: CapacityLimiterStatistics
   :members:

.. autoclass:: LockStatistics
   :members:

.. autoclass:: ConditionStatistics
   :members:

.. _async-generators:

Notes on async generators
-------------------------

Python 3.6 added support for *async generators*, which can use
``await``, ``async for``, and ``async with`` in between their ``yield``
statements. As you might expect, you use ``async for`` to iterate
over them. :pep:`525` has many more details if you want them.

For example, the following is a roundabout way to print
the numbers 0 through 9 with a 1-second delay before each one:

.. code-block:: python

    async def range_slowly(*args):
        """Like range(), but adds a 1-second sleep before each value."""
        for value in range(*args):
            await trio.sleep(1)
            yield value

    async def use_it():
        async for value in range_slowly(10):
            print(value)

    trio.run(use_it)

Trio supports async generators, with some caveats described in this section.

Finalization
~~~~~~~~~~~~

If you iterate over an async generator in its entirety, like the
example above does, then the execution of the async generator will
occur completely in the context of the code that's iterating over it,
and there aren't too many surprises.

If you abandon a partially-completed async generator, though, such as
by ``break``\ing out of the iteration, things aren't so simple.  The
async generator iterator object is still alive, waiting for you to
resume iterating it so it can produce more values. At some point,
Python will realize that you've dropped all references to the
iterator, and will call on Trio to throw in a `GeneratorExit` exception
so that any remaining cleanup code inside the generator has a chance
to run: ``finally`` blocks, ``__aexit__`` handlers, and so on.

So far, so good. Unfortunately, Python provides no guarantees about
*when* this happens. It could be as soon as you break out of the
``async for`` loop, or an arbitrary amount of time later. It could
even be after the entire Trio run has finished! Just about the only
guarantee is that it *won't* happen in the task that was using the
generator. That task will continue on with whatever else it's doing,
and the async generator cleanup will happen "sometime later,
somewhere else": potentially with different context variables,
not subject to timeouts, and/or after any nurseries you're using have
been closed.

If you don't like that ambiguity, and you want to ensure that a
generator's ``finally`` blocks and ``__aexit__`` handlers execute as
soon as you're done using it, then you'll need to wrap your use of the
generator in something like `async_generator.aclosing()
<https://async-generator.readthedocs.io/en/latest/reference.html#context-managers>`__:

.. code-block:: python

    # Instead of this:
    async for value in my_generator():
        if value == 42:
            break

    # Do this:
    async with aclosing(my_generator()) as aiter:
        async for value in aiter:
            if value == 42:
                break

This is cumbersome, but Python unfortunately doesn't provide any other
reliable options. If you use ``aclosing()``, then
your generator's cleanup code executes in the same context as the
rest of its iterations, so timeouts, exceptions, and context
variables work like you'd expect.

If you don't use ``aclosing()``, then Trio will do
its best anyway, but you'll have to contend with the following semantics:

* The cleanup of the generator occurs in a cancelled context, i.e.,
  all blocking calls executed during cleanup will raise `Cancelled`.
  This is to compensate for the fact that any timeouts surrounding
  the original use of the generator have been long since forgotten.

* The cleanup runs without access to any :ref:`context variables
  <task-local-storage>` that may have been present when the generator
  was originally being used.

* If the generator raises an exception during cleanup, then it's
  printed to the ``trio.async_generator_errors`` logger and otherwise
  ignored.

* If an async generator is still alive at the end of the whole
  call to :func:`trio.run`, then it will be cleaned up after all
  tasks have exited and before :func:`trio.run` returns.
  Since the "system nursery" has already been closed at this point,
  Trio isn't able to support any new calls to
  :func:`trio.lowlevel.spawn_system_task`.

If you plan to run your code on PyPy to take advantage of its better
performance, you should be aware that PyPy is *far more likely* than
CPython to perform async generator cleanup at a time well after the
last use of the generator. (This is a consequence of the fact that
PyPy does not use reference counting to manage memory.)  To help catch
issues like this, Trio will issue a `ResourceWarning` (ignored by
default, but enabled when running under ``python -X dev`` for example)
for each async generator that needs to be handled through the fallback
finalization path.

Cancel scopes and nurseries
~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. warning:: You may not write a ``yield`` statement that suspends an async generator
   inside a `CancelScope` or `Nursery` that was entered within the generator.

That is, this is OK:

.. code-block:: python

    async def some_agen():
        with trio.move_on_after(1):
            await long_operation()
        yield "first"
        async with trio.open_nursery() as nursery:
            nursery.start_soon(task1)
            nursery.start_soon(task2)
        yield "second"
        ...

But this is not:

.. code-block:: python

    async def some_agen():
        with trio.move_on_after(1):
            yield "first"
        async with trio.open_nursery() as nursery:
            yield "second"
        ...

Async generators decorated with ``@asynccontextmanager`` to serve as
the template for an async context manager are *not* subject to this
constraint, because ``@asynccontextmanager`` uses them in a limited
way that doesn't create problems.

Violating the rule described in this section will sometimes get you a
useful error message, but Trio is not able to detect all such cases,
so sometimes you'll get an unhelpful `TrioInternalError`. (And
sometimes it will seem to work, which is probably the worst outcome of
all, since then you might not notice the issue until you perform some
minor refactoring of the generator or the code that's iterating it, or
just get unlucky. There is a `proposed Python enhancement
<https://discuss.python.org/t/preventing-yield-inside-certain-context-managers/1091>`__
that would at least make it fail consistently.)

The reason for the restriction on cancel scopes has to do with the
difficulty of noticing when a generator gets suspended and
resumed. The cancel scopes inside the generator shouldn't affect code
running outside the generator, but Trio isn't involved in the process
of exiting and reentering the generator, so it would be hard pressed
to keep its cancellation plumbing in the correct state. Nurseries
use a cancel scope internally, so they have all the problems of cancel
scopes plus a number of problems of their own: for example, when
the generator is suspended, what should the background tasks do?
There's no good way to suspend them, but if they keep running and throw
an exception, where can that exception be reraised?

If you have an async generator that wants to ``yield`` from within a nursery
or cancel scope, your best bet is to refactor it to be a separate task
that communicates over memory channels.  The ``trio_util`` package offers a
`decorator that does this for you transparently
<https://trio-util.readthedocs.io/en/latest/#trio_util.trio_async_generator>`__.

For more discussion, see
Trio issues `264 <https://github.com/python-trio/trio/issues/264>`__
(especially `this comment
<https://github.com/python-trio/trio/issues/264#issuecomment-418989328>`__)
and `638 <https://github.com/python-trio/trio/issues/638>`__.


.. _threads:

Threads (if you must)
---------------------

In a perfect world, all third-party libraries and low-level APIs would
be natively async and integrated into Trio, and all would be happiness
and rainbows.

That world, alas, does not (yet) exist. Until it does, you may find
yourself needing to interact with non-Trio APIs that do rude things
like "blocking".

In acknowledgment of this reality, Trio provides two useful utilities
for working with real, operating-system level,
:mod:`threading`\-module-style threads. First, if you're in Trio but
need to push some blocking I/O into a thread, there's
`trio.to_thread.run_sync`. And if you're in a thread and need
to communicate back with Trio, you can use
:func:`trio.from_thread.run` and :func:`trio.from_thread.run_sync`.


.. _worker-thread-limiting:

Trio's philosophy about managing worker threads
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If you've used other I/O frameworks, you may have encountered the
concept of a "thread pool", which is most commonly implemented as a
fixed size collection of threads that hang around waiting for jobs to
be assigned to them. These solve two different problems: First,
reusing the same threads over and over is more efficient than
starting and stopping a new thread for every job you need done;
basically, the pool acts as a kind of cache for idle threads. And
second, having a fixed size avoids getting into a situation where
100,000 jobs are submitted simultaneously, and then 100,000 threads
are spawned and the system gets overloaded and crashes. Instead, the N
threads start executing the first N jobs, while the other
(100,000 - N) jobs sit in a queue and wait their turn. Which is
generally what you want, and this is how
:func:`trio.to_thread.run_sync` works by default.

The downside of this kind of thread pool is that sometimes, you need
more sophisticated logic for controlling how many threads are run at
once. For example, you might want a policy like "at most 20 threads
total, but no more than 3 of those can be running jobs associated with
the same user account", or you might want a pool whose size is
dynamically adjusted over time in response to system conditions.

It's even possible for a fixed-size policy to cause unexpected
`deadlocks <https://en.wikipedia.org/wiki/Deadlock>`__. Imagine a
situation where we have two different types of blocking jobs that you
want to run in the thread pool, type A and type B. Type A is pretty
simple: it just runs and completes pretty quickly. But type B is more
complicated: it has to stop in the middle and wait for some other work
to finish, and that other work includes running a type A job. Now,
suppose you submit N jobs of type B to the pool. They all start
running, and then eventually end up submitting one or more jobs of
type A. But since every thread in our pool is already busy, the type A
jobs don't actually start running – they just sit in a queue waiting
for the type B jobs to finish. But the type B jobs will never finish,
because they're waiting for the type A jobs. Our system has
deadlocked. The ideal solution to this problem is to avoid having type
B jobs in the first place – generally it's better to keep complex
synchronization logic in the main Trio thread. But if you can't do
that, then you need a custom thread allocation policy that tracks
separate limits for different types of jobs, and make it impossible
for type B jobs to fill up all the slots that type A jobs need to run.

So, we can see that it's important to be able to change the policy
controlling the allocation of threads to jobs. But in many frameworks,
this requires implementing a new thread pool from scratch, which is
highly non-trivial; and if different types of jobs need different
policies, then you may have to create multiple pools, which is
inefficient because now you effectively have two different thread
caches that aren't sharing resources.

Trio's solution to this problem is to split worker thread management
into two layers. The lower layer is responsible for taking blocking
I/O jobs and arranging for them to run immediately on some worker
thread. It takes care of solving the tricky concurrency problems
involved in managing threads and is responsible for optimizations like
reusing threads, but has no admission control policy: if you give it
100,000 jobs, it will spawn 100,000 threads. The upper layer is
responsible for providing the policy to make sure that this doesn't
happen – but since it *only* has to worry about policy, it can be much
simpler. In fact, all there is to it is the ``limiter=`` argument
passed to :func:`trio.to_thread.run_sync`. This defaults to a global
:class:`CapacityLimiter` object, which gives us the classic fixed-size
thread pool behavior. (See
:func:`trio.to_thread.current_default_thread_limiter`.) But if you
want to use "separate pools" for type A jobs and type B jobs, then
it's just a matter of creating two separate :class:`CapacityLimiter`
objects and passing them in when running these jobs. Or here's an
example of defining a custom policy that respects the global thread
limit, while making sure that no individual user can use more than 3
threads at a time:

.. code-block:: python

   class CombinedLimiter:
        def __init__(self, first, second):
            self._first = first
            self._second = second

        async def acquire_on_behalf_of(self, borrower):
            # Acquire both, being careful to clean up properly on error
            await self._first.acquire_on_behalf_of(borrower)
            try:
                await self._second.acquire_on_behalf_of(borrower)
            except:
                self._first.release_on_behalf_of(borrower)
                raise

        def release_on_behalf_of(self, borrower):
            # Release both, being careful to clean up properly on error
            try:
                self._second.release_on_behalf_of(borrower)
            finally:
                self._first.release_on_behalf_of(borrower)


   # Use a weak value dictionary, so that we don't waste memory holding
   # limiter objects for users who don't have any worker threads running.
   USER_LIMITERS = weakref.WeakValueDictionary()
   MAX_THREADS_PER_USER = 3

   def get_user_limiter(user_id):
       try:
           return USER_LIMITERS[user_id]
       except KeyError:
           per_user_limiter = trio.CapacityLimiter(MAX_THREADS_PER_USER)
           global_limiter = trio.current_default_thread_limiter()
           # IMPORTANT: acquire the per_user_limiter before the global_limiter.
           # If we get 100 jobs for a user at the same time, we want
           # to only allow 3 of them at a time to even compete for the
           # global thread slots.
           combined_limiter = CombinedLimiter(per_user_limiter, global_limiter)
           USER_LIMITERS[user_id] = combined_limiter
           return combined_limiter


   async def run_sync_in_thread_for_user(user_id, sync_fn, *args):
       combined_limiter = get_user_limiter(user_id)
       return await trio.to_thread.run_sync(sync_fn, *args, limiter=combined_limiter)


.. module:: trio.to_thread
.. currentmodule:: trio

Putting blocking I/O into worker threads
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autofunction:: trio.to_thread.run_sync

.. autofunction:: trio.to_thread.current_default_thread_limiter


.. module:: trio.from_thread
.. currentmodule:: trio

Getting back into the Trio thread from another thread
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autofunction:: trio.from_thread.run

.. autofunction:: trio.from_thread.run_sync


This will probably be clearer with an example. Here we demonstrate how
to spawn a child thread, and then use a :ref:`memory channel
<channels>` to send messages between the thread and a Trio task:

.. literalinclude:: reference-core/from-thread-example.py

.. note::

   The ``from_thread.run*`` functions reuse the host task that called
   :func:`trio.to_thread.run_sync` to run your provided function, as long as you're
   using the default ``abandon_on_cancel=False`` so Trio can be sure that the task will remain
   around to perform the work. If you pass ``abandon_on_cancel=True`` at the outset, or if
   you provide a :class:`~trio.lowlevel.TrioToken` when calling back in to Trio, your
   functions will be executed in a new system task. Therefore, the
   :func:`~trio.lowlevel.current_task`, :func:`current_effective_deadline`, or other
   task-tree specific values may differ depending on keyword argument values.

You can also use :func:`trio.from_thread.check_cancelled` to check for cancellation from
a thread that was spawned by :func:`trio.to_thread.run_sync`. If the call to
:func:`~trio.to_thread.run_sync` was cancelled, then
:func:`~trio.from_thread.check_cancelled` will raise :func:`trio.Cancelled`.
It's like ``trio.from_thread.run(trio.sleep, 0)``, but much faster.

.. autofunction:: trio.from_thread.check_cancelled

Threads and task-local storage
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

When working with threads, you can use the same `contextvars` we discussed above,
because their values are preserved.

This is done by automatically copying the `contextvars` context when you use any of:

* `trio.to_thread.run_sync`
* `trio.from_thread.run`
* `trio.from_thread.run_sync`

That means that the values of the context variables are accessible even in worker
threads, or when sending a function to be run in the main/parent Trio thread using
`trio.from_thread.run` *from* one of these worker threads.

But it also means that as the context is not the same but a copy, if you `set` the
context variable value *inside* one of these functions that work in threads, the
new value will only be available in that context (that was copied). So, the new value
will be available for that function and other internal/children tasks, but the value
won't be available in the parent thread.

If you need to modify values that would live in the context variables and you need to
make those modifications from the child threads, you can instead set a mutable object
(e.g. a dictionary) in the context variable of the top level/parent Trio thread.
Then in the children, instead of setting the context variable, you can ``get`` the same
object, and modify its values. That way you keep the same object in the context
variable and only mutate it in child threads.

This way, you can modify the object content in child threads and still access the
new content in the parent thread.

Here's an example:

.. literalinclude:: reference-core/thread-contextvars-example.py

Running that script will result in the output:

.. code-block:: none

    Processed user 2 with message Hello 2 in a thread worker
    Processed user 0 with message Hello 0 in a thread worker
    Processed user 1 with message Hello 1 in a thread worker
    New contextvar value from worker thread for user 2: Hello 2
    New contextvar value from worker thread for user 1: Hello 1
    New contextvar value from worker thread for user 0: Hello 0

If you are using ``contextvars`` or you are using a library that uses them, now you
know how they interact when working with threads in Trio.

But have in mind that in many cases it might be a lot simpler to *not* use context
variables in your own code and instead pass values in arguments, as it might be more
explicit and might be easier to reason about.

.. note::

   The context is automatically copied instead of using the same parent context because
   a single context can't be used in more than one thread, it's not supported by
   ``contextvars``.


.. _interactive debugging:


Interactive debugging
---------------------

When you start an interactive Python session to debug any async program
(whether it's based on ``asyncio``, Trio, or something else), every await
expression needs to be inside an async function:

.. code-block:: console

   $ python
   Python 3.10.6
   Type "help", "copyright", "credits" or "license" for more information.
   >>> import trio
   >>> await trio.sleep(1)
   File "<stdin>", line 1
   SyntaxError: 'await' outside function
   >>> async def main():
   ...     print("hello...")
   ...     await trio.sleep(1)
   ...     print("world!")
   ...
   >>> trio.run(main)
   hello...
   world!

This can make it difficult to iterate quickly since you have to redefine the
whole function body whenever you make a tweak.

Trio provides a modified interactive console that lets you ``await`` at the top
level. You can access this console by running ``python -m trio``:

.. code-block:: console

   $ python -m trio
   Trio 0.21.0+dev, Python 3.10.6
   Use "await" directly instead of "trio.run()".
   Type "help", "copyright", "credits" or "license" for more information.
   >>> import trio
   >>> print("hello..."); await trio.sleep(1); print("world!")
   hello...
   world!

If you are an IPython user, you can use IPython's `autoawait
<https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-autoawait>`__
function. This can be enabled within the IPython shell by running the magic command
``%autoawait trio``. To have ``autoawait`` enabled whenever Trio installed, you can
add the following to your IPython startup files.
(e.g. ``~/.ipython/profile_default/startup/10-async.py``)

.. code-block::

   try:
       import trio
       get_ipython().run_line_magic("autoawait", "trio")
   except ImportError:
       pass

Exceptions and warnings
-----------------------

.. autoexception:: Cancelled

.. autoexception:: TooSlowError

.. autoexception:: WouldBlock

.. autoexception:: EndOfChannel

.. autoexception:: BusyResourceError

.. autoexception:: ClosedResourceError

.. autoexception:: BrokenResourceError

.. autoexception:: RunFinishedError

.. autoexception:: TrioInternalError

.. autoexception:: TrioDeprecationWarning
   :show-inheritance: