A Conceptual Overview of "asyncio"
**********************************

This HOWTO article seeks to help you build a sturdy mental model of
how "asyncio" fundamentally works, helping you understand the how and
why behind the recommended patterns.

You might be curious about some key "asyncio" concepts. By the end of
this article, you'll be able to comfortably answer these questions:

* What's happening behind the scenes when an object is awaited?

* How does "asyncio" differentiate between a task which doesn't need
  CPU time (such as a network request or file read) as opposed to a
  task that does (such as computing n-factorial)?

* How to write an asynchronous variant of an operation, such as an
  async sleep or database request.

参考:

  * The guide that inspired this HOWTO article, by Alexander Nordin.

  * This in-depth YouTube tutorial series on "asyncio" created by
    Python core team member, Łukasz Langa.

  * 500 Lines or Less: A Web Crawler With asyncio Coroutines by A.
    Jesse Jiryu Davis and Guido van Rossum.


A conceptual overview part 1: the high-level
============================================

In part 1, we'll cover the main, high-level building blocks of
"asyncio": the event loop, coroutine functions, coroutine objects,
tasks, and "await".


Event Loop
----------

Everything in "asyncio" happens relative to the event loop. It's the
star of the show. It's like an orchestra conductor. It's behind the
scenes managing resources. Some power is explicitly granted to it, but
a lot of its ability to get things done comes from the respect and
cooperation of its worker bees.

In more technical terms, the event loop contains a collection of jobs
to be run. Some jobs are added directly by you, and some indirectly by
"asyncio". The event loop takes a job from its backlog of work and
invokes it (or "gives it control"), similar to calling a function, and
then that job runs. Once it pauses or completes, it returns control to
the event loop. The event loop will then select another job from its
pool and invoke it. You can *roughly* think of the collection of jobs
as a queue: jobs are added and then processed one at a time, generally
(but not always) in order. This process repeats indefinitely, with the
event loop cycling endlessly onwards. If there are no more jobs
pending execution, the event loop is smart enough to rest and avoid
needlessly wasting CPU cycles, and will come back when there's more
work to be done.

Effective execution relies on jobs sharing well and cooperating; a
greedy job could hog control and leave the other jobs to starve,
rendering the overall event loop approach rather useless.

   import asyncio

   # This creates an event loop and indefinitely cycles through
   # its collection of jobs.
   event_loop = asyncio.new_event_loop()
   event_loop.run_forever()


Asynchronous functions and coroutines
-------------------------------------

This is a basic, boring Python function:

   def hello_printer():
       print(
           "Hi, I am a lowly, simple printer, though I have all I "
           "need in life -- \nfresh paper and my dearly beloved octopus "
           "partner in crime."
       )

Calling a regular function invokes its logic or body:

   >>> hello_printer()
   Hi, I am a lowly, simple printer, though I have all I need in life --
   fresh paper and my dearly beloved octopus partner in crime.

The async def, as opposed to just a plain "def", makes this an
asynchronous function (or "coroutine function"). Calling it creates
and returns a coroutine object.

   async def loudmouth_penguin(magic_number: int):
       print(
        "I am a super special talking penguin. Far cooler than that printer. "
        f"By the way, my lucky number is: {magic_number}."
       )

Calling the async function, "loudmouth_penguin", does not execute the
print statement; instead, it creates a coroutine object:

   >>> loudmouth_penguin(magic_number=3)
   <coroutine object loudmouth_penguin at 0x104ed2740>

The terms "coroutine function" and "coroutine object" are often
conflated as coroutine. That can be confusing! In this article,
coroutine specifically refers to a coroutine object, or more
precisely, an instance of "types.CoroutineType" (native coroutine).
Note that coroutines can also exist as instances of
"collections.abc.Coroutine" -- a distinction that matters for type
checking.

A coroutine represents the function's body or logic. A coroutine has
to be explicitly started; again, merely creating the coroutine does
not start it. Notably, the coroutine can be paused and resumed at
various points within the function's body. That pausing and resuming
ability is what allows for asynchronous behavior!

Coroutines and coroutine functions were built by leveraging the
functionality of *generators* and *generator functions*. Recall, a
generator function is a function that "yield"s, like this one:

   def get_random_number():
       # This would be a bad random number generator!
       print("Hi")
       yield 1
       print("Hello")
       yield 7
       print("Howdy")
       yield 4
       ...

Similar to a coroutine function, calling a generator function does not
run it. Instead, it creates a generator object:

   >>> get_random_number()
   <generator object get_random_number at 0x1048671c0>

You can proceed to the next "yield" of a generator by using the built-
in function "next()". In other words, the generator runs, then pauses.
For example:

   >>> generator = get_random_number()
   >>> next(generator)
   Hi
   1
   >>> next(generator)
   Hello
   7


Task
----

Roughly speaking, tasks are coroutines (not coroutine functions) tied
to an event loop. A task also maintains a list of callback functions
whose importance will become clear in a moment when we discuss
"await". The recommended way to create tasks is via
"asyncio.create_task()".

Creating a task automatically schedules it for execution (by adding a
callback to run it in the event loop's to-do list, that is, collection
of jobs).

"asyncio" automatically associates tasks with the event loop for you.
This automatic association was purposely designed into "asyncio" for
the sake of simplicity. Without it, you'd have to keep track of the
event loop object and pass it to any coroutine function that wants to
create tasks, adding redundant clutter to your code.

   coroutine = loudmouth_penguin(magic_number=5)
   # This creates a Task object and schedules its execution via the event loop.
   task = asyncio.create_task(coroutine)

Earlier, we manually created the event loop and set it to run forever.
In practice, it's recommended to use (and common to see)
"asyncio.run()", which takes care of managing the event loop and
ensuring the provided coroutine finishes before advancing. For
example, many async programs follow this setup:

   import asyncio

   async def main():
       # Perform all sorts of wacky, wild asynchronous things...
       ...

   if __name__ == "__main__":
       asyncio.run(main())
       # The program will not reach the following print statement until the
       # coroutine main() finishes.
       print("coroutine main() is done!")

It's important to be aware that the task itself is not added to the
event loop, only a callback to the task is. This matters if the task
object you created is garbage collected before it's called by the
event loop. For example, consider this program:

   async def hello():
       print("hello!")

   async def main():
       asyncio.create_task(hello())
       # Other asynchronous instructions which run for a while
       # and cede control to the event loop...
       ...

   asyncio.run(main())

Because there's no reference to the task object created on line 5, it
*might* be garbage collected before the event loop invokes it. Later
instructions in the coroutine "main()" hand control back to the event
loop so it can invoke other jobs. When the event loop eventually tries
to run the task, it might fail and discover the task object does not
exist! This can also happen even if a coroutine keeps a reference to a
task but completes before that task finishes. When the coroutine
exits, local variables go out of scope and may be subject to garbage
collection. In practice, "asyncio" and Python's garbage collector work
pretty hard to ensure this sort of thing doesn't happen. But that's no
reason to be reckless!


await
-----

"await" is a Python keyword that's commonly used in one of two
different ways:

   await task
   await coroutine

In a crucial way, the behavior of "await" depends on the type of
object being awaited.

Awaiting a task will cede control from the current task or coroutine
to the event loop. In the process of relinquishing control, a few
important things happen. We'll use the following code example to
illustrate:

   async def plant_a_tree():
       dig_the_hole_task = asyncio.create_task(dig_the_hole())
       await dig_the_hole_task

       # Other instructions associated with planting a tree.
       ...

In this example, imagine the event loop has passed control to the
start of the coroutine "plant_a_tree()". As seen above, the coroutine
creates a task and then awaits it. The "await dig_the_hole_task"
instruction adds a callback (which will resume "plant_a_tree()") to
the "dig_the_hole_task" object's list of callbacks. And then, the
instruction cedes control to the event loop. Some time later, the
event loop will pass control to "dig_the_hole_task" and the task will
finish whatever it needs to do. Once the task finishes, it will add
its various callbacks to the event loop, in this case, a call to
resume "plant_a_tree()".

Generally speaking, when the awaited task finishes
("dig_the_hole_task"), the original task or coroutine
("plant_a_tree()") is added back to the event loop's to-do list to be
resumed.

This is a basic, yet reliable mental model. In practice, the control
handoffs are slightly more complex, but not by much. In part 2, we'll
walk through the details that make this possible.

**Unlike tasks, awaiting a coroutine does not hand control back to the
event loop!** Wrapping a coroutine in a task first, then awaiting that
would cede control. The behavior of "await coroutine" is effectively
the same as invoking a regular, synchronous Python function. Consider
this program:

   import asyncio

   async def coro_a():
      print("I am coro_a(). Hi!")

   async def coro_b():
      print("I am coro_b(). I sure hope no one hogs the event loop...")

   async def main():
      task_b = asyncio.create_task(coro_b())
      num_repeats = 3
      for _ in range(num_repeats):
         await coro_a()
      await task_b

   asyncio.run(main())

The first statement in the coroutine "main()" creates "task_b" and
schedules it for execution via the event loop. Then, "coro_a()" is
repeatedly awaited. Control never cedes to the event loop, which is
why we see the output of all three "coro_a()" invocations before
"coro_b()"'s output:

   I am coro_a(). Hi!
   I am coro_a(). Hi!
   I am coro_a(). Hi!
   I am coro_b(). I sure hope no one hogs the event loop...

If we change "await coro_a()" to "await
asyncio.create_task(coro_a())", the behavior changes. The coroutine
"main()" cedes control to the event loop with that statement. The
event loop then proceeds through its backlog of work, calling "task_b"
and then the task which wraps "coro_a()" before resuming the coroutine
"main()".

   I am coro_b(). I sure hope no one hogs the event loop...
   I am coro_a(). Hi!
   I am coro_a(). Hi!
   I am coro_a(). Hi!

This behavior of "await coroutine" can trip a lot of people up! That
example highlights how using only "await coroutine" could
unintentionally hog control from other tasks and effectively stall the
event loop. "asyncio.run()" can help you detect such occurrences via
the "debug=True" flag, which enables debug mode. Among other things,
it will log any coroutines that monopolize execution for 100ms or
longer.

The design intentionally trades off some conceptual clarity around
usage of "await" for improved performance. Each time a task is
awaited, control needs to be passed all the way up the call stack to
the event loop. That might sound minor, but in a large program with
many "await" statements and a deep call stack, that overhead can add
up to a meaningful performance drag.


A conceptual overview part 2: the nuts and bolts
================================================

Part 2 goes into detail on the mechanisms "asyncio" uses to manage
control flow. This is where the magic happens. You'll come away from
this section knowing what "await" does behind the scenes and how to
make your own asynchronous operators.


The inner workings of coroutines
--------------------------------

"asyncio" leverages four components to pass around control.

"coroutine.send(arg)" is the method used to start or resume a
coroutine. If the coroutine was paused and is now being resumed, the
argument "arg" will be sent in as the return value of the "yield"
statement which originally paused it. If the coroutine is being used
for the first time (as opposed to being resumed), "arg" must be
"None".

   class Rock:
       def __await__(self):
           value_sent_in = yield 7
           print(f"Rock.__await__ resuming with value: {value_sent_in}.")
           return value_sent_in

   async def main():
       print("Beginning coroutine main().")
       rock = Rock()
       print("Awaiting rock...")
       value_from_rock = await rock
       print(f"Coroutine received value: {value_from_rock} from rock.")
       return 23

   coroutine = main()
   intermediate_result = coroutine.send(None)
   print(f"Coroutine paused and returned intermediate value: {intermediate_result}.")

   print(f"Resuming coroutine and sending in value: 42.")
   try:
       coroutine.send(42)
   except StopIteration as e:
       returned_value = e.value
   print(f"Coroutine main() finished and provided value: {returned_value}.")

yield, as usual, pauses execution and returns control to the caller.
In the example above, the "yield", on line 3, is called by "... =
await rock" on line 11. More broadly speaking, "await" calls the
"__await__()" method of the given object. "await" also does one more
very special thing: it propagates (or "passes along") any "yield"s it
receives up the call chain. In this case, that's back to "... =
coroutine.send(None)" on line 16.

The coroutine is resumed via the "coroutine.send(42)" call on line 21.
The coroutine picks back up from where it "yield"ed (or paused) on
line 3 and executes the remaining statements in its body. When a
coroutine finishes, it raises a "StopIteration" exception with the
return value attached in the "value" attribute.

That snippet produces this output:

   Beginning coroutine main().
   Awaiting rock...
   Coroutine paused and returned intermediate value: 7.
   Resuming coroutine and sending in value: 42.
   Rock.__await__ resuming with value: 42.
   Coroutine received value: 42 from rock.
   Coroutine main() finished and provided value: 23.

It's worth pausing for a moment here and making sure you followed the
various ways that control flow and values were passed. A lot of
important ideas were covered and it's worth ensuring your
understanding is firm.

The only way to yield (or effectively cede control) from a coroutine
is to "await" an object that "yield"s in its "__await__" method. That
might sound odd to you. You might be thinking:

   1. What about a "yield" directly within the coroutine function? The
   coroutine function becomes an async generator function, a different
   beast entirely.

   2. What about a yield from within the coroutine function to a
   (plain) generator? That causes the error: "SyntaxError: yield from
   not allowed in a coroutine." This was intentionally designed for
   the sake of simplicity -- mandating only one way of using
   coroutines. Initially "yield" was barred as well, but was re-
   accepted to allow for async generators. Despite that, "yield from"
   and "await" effectively do the same thing.


Future
------

A future is an object meant to represent a computation's status and
result. The term is a nod to the idea of something still to come or
not yet happened, and the object is a way to keep an eye on that
something.

A future has a few important attributes. One is its state, which can
be either "pending", "cancelled", or "done". Another is its result,
which is set when the state transitions to done. Unlike a coroutine, a
future does not represent the actual computation to be done; instead,
it represents the status and result of that computation, kind of like
a status light (red, yellow, or green) or indicator.

"asyncio.Task" subclasses "asyncio.Future" in order to gain these
various capabilities. The prior section said tasks store a list of
callbacks, which wasn't entirely accurate. It's actually the "Future"
class that implements this logic, which "Task" inherits.

Futures may also be used directly (not via tasks). Tasks mark
themselves as done when their coroutine is complete. Futures are much
more versatile and will be marked as done when you say so. In this
way, they're the flexible interface for you to make your own
conditions for waiting and resuming.


A homemade asyncio.sleep
------------------------

We'll go through an example of how you could leverage a future to
create your own variant of asynchronous sleep ("async_sleep") which
mimics "asyncio.sleep()".

This snippet registers a few tasks with the event loop and then awaits
the task created by "asyncio.create_task", which wraps the
"async_sleep(3)" coroutine. We want that task to finish only after
three seconds have elapsed, but without preventing other tasks from
running.

   async def other_work():
       print("I like work. Work work.")

   async def main():
       # Add a few other tasks to the event loop, so there's something
       # to do while asynchronously sleeping.
       work_tasks = [
           asyncio.create_task(other_work()),
           asyncio.create_task(other_work()),
           asyncio.create_task(other_work())
       ]
       print(
           "Beginning asynchronous sleep at time: "
           f"{datetime.datetime.now().strftime("%H:%M:%S")}."
       )
       await asyncio.create_task(async_sleep(3))
       print(
           "Done asynchronous sleep at time: "
           f"{datetime.datetime.now().strftime("%H:%M:%S")}."
       )
       # asyncio.gather effectively awaits each task in the collection.
       await asyncio.gather(*work_tasks)

Below, we use a future to enable custom control over when that task
will be marked as done. If "future.set_result()" (the method
responsible for marking that future as done) is never called, then
this task will never finish. We've also enlisted the help of another
task, which we'll see in a moment, that will monitor how much time has
elapsed and, accordingly, call "future.set_result()".

   async def async_sleep(seconds: float):
       future = asyncio.Future()
       time_to_wake = time.time() + seconds
       # Add the watcher-task to the event loop.
       watcher_task = asyncio.create_task(_sleep_watcher(future, time_to_wake))
       # Block until the future is marked as done.
       await future

Below, we use a rather bare "YieldToEventLoop()" object to "yield"
from its "__await__" method, ceding control to the event loop. This is
effectively the same as calling "asyncio.sleep(0)", but this approach
offers more clarity, not to mention it's somewhat cheating to use
"asyncio.sleep" when showcasing how to implement it!

As usual, the event loop cycles through its tasks, giving them control
and receiving control back when they pause or finish. The
"watcher_task", which runs the coroutine "_sleep_watcher(...)", will
be invoked once per full cycle of the event loop. On each resumption,
it'll check the time and if not enough has elapsed, then it'll pause
once again and hand control back to the event loop. Once enough time
has elapsed, "_sleep_watcher(...)" marks the future as done and
completes by exiting its infinite "while" loop. Given this helper task
is only invoked once per cycle of the event loop, you'd be correct to
note that this asynchronous sleep will sleep *at least* three seconds,
rather than exactly three seconds. Note this is also true of
"asyncio.sleep".

   class YieldToEventLoop:
       def __await__(self):
           yield

   async def _sleep_watcher(future, time_to_wake):
       while True:
           if time.time() >= time_to_wake:
               # This marks the future as done.
               future.set_result(None)
               break
           else:
               await YieldToEventLoop()

Here is the full program's output:

   $ python custom-async-sleep.py
   Beginning asynchronous sleep at time: 14:52:22.
   I like work. Work work.
   I like work. Work work.
   I like work. Work work.
   Done asynchronous sleep at time: 14:52:25.

You might feel this implementation of asynchronous sleep was
unnecessarily convoluted. And, well, it was. The example was meant to
showcase the versatility of futures with a simple example that could
be mimicked for more complex needs. For reference, you could implement
it without futures, like so:

   async def simpler_async_sleep(seconds):
       time_to_wake = time.time() + seconds
       while True:
           if time.time() >= time_to_wake:
               return
           else:
               await YieldToEventLoop()

But that's all for now. Hopefully you're ready to more confidently
dive into some async programming or check out advanced topics in the
"rest of the documentation".
