[Python-Dev] async/await in Python; v2 (original) (raw)

Yury Selivanov yselivanov.ml at gmail.com
Tue Apr 21 19:26:48 CEST 2015


Hi python-dev,

I'm moving the discussion from python-ideas to here.

The updated version of the PEP should be available shortly at https://www.python.org/dev/peps/pep-0492 and is also pasted in this email.

Updates:

  1. CO_ASYNC flag was renamed to CO_COROUTINE;

  2. sys.set_async_wrapper() was renamed to sys.set_coroutine_wrapper();

  3. New function: sys.get_coroutine_wrapper();

  4. types.async_def() renamed to types.coroutine();

  5. New section highlighting differences from PEP 3152.

  6. New AST node - AsyncFunctionDef; the proposal now is 100% backwards compatible;

  7. A new section clarifying that coroutine-generators are not part of the current proposal;

  8. Various small edits/typos fixes.

There's is a bug tracker issue to track code review of the reference implementation (Victor Stinner is doing the review): http://bugs.python.org/issue24017 While the PEP isn't accepted, we want to make sure that the reference implementation is ready when such a decision will be made.

Let's discuss some open questions:

  1. Victor raised a question if we should locate coroutine() function from 'types' module to 'functools'.

    My opinion is that 'types' module is a better place for 'corotine()', since it adjusts the type of the passed generator. 'functools' is about 'partials', 'lru_cache' and 'wraps' kind of things.

  2. I propose to disallow using of 'for..in' loops, and builtins like 'list()', 'iter()', 'next()', 'tuple()' etc on coroutines.

    It's possible by modifying PyObject_GetIter to raise an exception if it receives a coroutine-object.

    'yield from' can also be modified to only accept coroutine objects if it is called from a generator with CO_COROUTINE flag.

    This will further separate coroutines from generators, making it harder to screw something up by an accident.

    I have a branch of reference implementation https://github.com/1st1/cpython/tree/await_noiter where this is implemented. I did not observe any performance drop.

    There is just one possible backwards compatibility issue here: there will be an exception if some user of asyncio actually used to iterate over generators decorated with @coroutine. But I can't imagine why would someone do that, and even if they did -- it's probably a bug or wrong usage of asyncio.

That's it! I'd be happy to hear some feedback!

Thanks, Yury

PEP: 492 Title: Coroutines with async and await syntax Version: RevisionRevisionRevision Last-Modified: DateDateDate Author: Yury Selivanov <yselivanov at sprymix.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 09-Apr-2015 Python-Version: 3.5 Post-History: 17-Apr-2015, 21-Apr-2015

Abstract

This PEP introduces new syntax for coroutines, asynchronous with statements and for loops. The main motivation behind this proposal is to streamline writing and maintaining asynchronous code, as well as to simplify previously hard to implement code patterns.

Rationale and Goals

Current Python supports implementing coroutines via generators (PEP 342), further enhanced by the yield from syntax introduced in PEP 380. This approach has a number of shortcomings:

This proposal makes coroutines a native Python language feature, and clearly separates them from generators. This removes generator/coroutine ambiguity, and makes it possible to reliably define coroutines without reliance on a specific library. This also enables linters and IDEs to improve static code analysis and refactoring.

Native coroutines and the associated new syntax features make it possible to define context manager and iteration protocols in asynchronous terms. As shown later in this proposal, the new async with statement lets Python programs perform asynchronous calls when entering and exiting a runtime context, and the new async for statement makes it possible to perform asynchronous calls in iterators.

Specification

This proposal introduces new syntax and semantics to enhance coroutine support in Python, it does not change the internal implementation of coroutines, which are still based on generators.

It is strongly suggested that the reader understands how coroutines are implemented in Python (PEP 342 and PEP 380). It is also recommended to read PEP 3156 (asyncio framework) and PEP 3152 (Cofunctions).

From this point in this document we use the word coroutine to refer to functions declared using the new syntax. generator-based coroutine is used where necessary to refer to coroutines that are based on generator syntax.

New Coroutine Declaration Syntax

The following new syntax is used to declare a coroutine::

 async def read_data(db):
     pass

Key properties of coroutines:

types.coroutine()

A new function coroutine(gen) is added to the types module. It applies CO_COROUTINE flag to the passed generator-function's code object, making it to return a coroutine object when called.

This feature enables an easy upgrade path for existing libraries.

Await Expression

The following new await expression is used to obtain a result of coroutine execution::

 async def read_data(db):
     data = await db.fetch('SELECT ...')
     ...

await, similarly to yield from, suspends execution of read_data coroutine until db.fetch awaitable completes and returns the result data.

It uses the yield from implementation with an extra step of validating its argument. await only accepts an awaitable, which can be one of:

It is a SyntaxError to use await outside of a coroutine.

Asynchronous Context Managers and "async with"

An asynchronous context manager is a context manager that is able to suspend execution in its enter and exit methods.

To make this possible, a new protocol for asynchronous context managers is proposed. Two new magic methods are added: __aenter__ and __aexit__. Both must return an awaitable.

An example of an asynchronous context manager::

 class AsyncContextManager:
     async def __aenter__(self):
         await log('entering context')

     async def __aexit__(self, exc_type, exc, tb):
         await log('exiting context')

New Syntax ''''''''''

A new statement for asynchronous context managers is proposed::

 async with EXPR as VAR:
     BLOCK

which is semantically equivalent to::

 mgr = (EXPR)
 aexit = type(mgr).__aexit__
 aenter = type(mgr).__aenter__(mgr)
 exc = True

 try:
     try:
         VAR = await aenter
         BLOCK
     except:
         exc = False
         exit_res = await aexit(mgr, *sys.exc_info())
         if not exit_res:
             raise

 finally:
     if exc:
         await aexit(mgr, None, None, None)

As with regular with statements, it is possible to specify multiple context managers in a single async with statement.

It is an error to pass a regular context manager without __aenter__ and __aexit__ methods to async with. It is a SyntaxError to use async with outside of a coroutine.

Example '''''''

With asynchronous context managers it is easy to implement proper database transaction managers for coroutines::

 async def commit(session, data):
     ...

     async with session.transaction():
         ...
         await session.update(data)
         ...

Code that needs locking also looks lighter::

 async with lock:
     ...

instead of::

 with (yield from lock):
     ...

Asynchronous Iterators and "async for"

An asynchronous iterable is able to call asynchronous code in its iter implementation, and asynchronous iterator can call asynchronous code in its next method. To support asynchronous iteration:

  1. An object must implement an __aiter__ method returning an awaitable resulting in an asynchronous iterator object.

  2. An asynchronous iterator object must implement an __anext__ method returning an awaitable.

  3. To stop iteration __anext__ must raise a StopAsyncIteration exception.

An example of asynchronous iterable::

 class AsyncIterable:
     async def __aiter__(self):
         return self

     async def __anext__(self):
         data = await self.fetch_data()
         if data:
             return data
         else:
             raise StopAsyncIteration

     async def fetch_data(self):
         ...

New Syntax ''''''''''

A new statement for iterating through asynchronous iterators is proposed::

 async for TARGET in ITER:
     BLOCK
 else:
     BLOCK2

which is semantically equivalent to::

 iter = (ITER)
 iter = await type(iter).__aiter__(iter)
 running = True
 while running:
     try:
         TARGET = await type(iter).__anext__(iter)
     except StopAsyncIteration:
         running = False
     else:
         BLOCK
 else:
     BLOCK2

It is an error to pass a regular iterable without __aiter__ method to async for. It is a SyntaxError to use async for outside of a coroutine.

As for with regular for statement, async for has an optional else clause.

Example 1 '''''''''

With asynchronous iteration protocol it is possible to asynchronously buffer data during iteration::

 async for data in cursor:
     ...

Where cursor is an asynchronous iterator that prefetches N rows of data from a database after every N iterations.

The following code illustrates new asynchronous iteration protocol::

 class Cursor:
     def __init__(self):
         self.buffer = collections.deque()

     def _prefetch(self):
         ...

     async def __aiter__(self):
         return self

     async def __anext__(self):
         if not self.buffer:
             self.buffer = await self._prefetch()
             if not self.buffer:
                 raise StopAsyncIteration
         return self.buffer.popleft()

then the Cursor class can be used as follows::

 async for row in Cursor():
     print(row)

which would be equivalent to the following code::

 i = await Cursor().__aiter__()
 while True:
     try:
         row = await i.__anext__()
     except StopAsyncIteration:
         break
     else:
         print(row)

Example 2 '''''''''

The following is a utility class that transforms a regular iterable to an asynchronous one. While this is not a very useful thing to do, the code illustrates the relationship between regular and asynchronous iterators.

:: class AsyncIteratorWrapper: def init(self, obj): self._it = iter(obj)

     async def __aiter__(self):
         return self

     async def __anext__(self):
         try:
             value = next(self._it)
         except StopIteration:
             raise StopAsyncIteration
         return value

 async for item in AsyncIteratorWrapper("abc"):
     print(item)

Why StopAsyncIteration? '''''''''''''''''''''''

Coroutines are still based on generators internally. So, before PEP 479, there was no fundamental difference between

:: def g1(): yield from fut return 'spam'

and

:: def g2(): yield from fut raise StopIteration('spam')

And since PEP 479 is accepted and enabled by default for coroutines, the following example will have its StopIteration wrapped into a RuntimeError

:: async def a1(): await fut raise StopIteration('spam')

The only way to tell the outside code that the iteration has ended is to raise something other than StopIteration. Therefore, a new built-in exception class StopAsyncIteration was added.

Moreover, with semantics from PEP 479, all StopIteration exceptions raised in coroutines are wrapped in RuntimeError.

Debugging Features

One of the most frequent mistakes that people make when using generators as coroutines is forgetting to use yield from::

 @asyncio.coroutine
 def useful():
     asyncio.sleep(1) # this will do noting without 'yield from'

For debugging this kind of mistakes there is a special debug mode in asyncio, in which @coroutine decorator wraps all functions with a special object with a destructor logging a warning. Whenever a wrapped generator gets garbage collected, a detailed logging message is generated with information about where exactly the decorator function was defined, stack trace of where it was collected, etc. Wrapper object also provides a convenient __repr__ function with detailed information about the generator.

The only problem is how to enable these debug capabilities. Since debug facilities should be a no-op in production mode, @coroutine decorator makes the decision of whether to wrap or not to wrap based on an OS environment variable PYTHONASYNCIODEBUG. This way it is possible to run asyncio programs with asyncio's own functions instrumented. EventLoop.set_debug, a different debug facility, has no impact on @coroutine decorator's behavior.

With this proposal, coroutines is a native, distinct from generators, concept. New methods set_coroutine_wrapper and get_coroutine_wrapper are added to the sys module, with which frameworks can provide advanced debugging facilities.

It is also important to make coroutines as fast and efficient as possible, therefore there are no debug features enabled by default.

Example::

 async def debug_me():
     await asyncio.sleep(1)

 def async_debug_wrap(generator):
     return asyncio.AsyncDebugWrapper(generator)

 sys.set_coroutine_wrapper(async_debug_wrap)

 debug_me()  # <- this line will likely GC the coroutine object and
             # trigger AsyncDebugWrapper's code.

 assert isinstance(debug_me(), AsyncDebugWrapper)

 sys.set_coroutine_wrapper(None) # <- this unsets any
                                 #    previously set wrapper
 assert not isinstance(debug_me(), AsyncDebugWrapper)

If sys.set_coroutine_wrapper() is called twice, the new wrapper replaces the previous wrapper. sys.set_coroutine_wrapper(None) unsets the wrapper.

Glossary

:Coroutine: A coroutine function, or just "coroutine", is declared with async def. It uses await and return value; see New Coroutine Declaration Syntax_ for details.

:Coroutine object: Returned from a coroutine function. See Await Expression_ for details.

:Future-like object: An object with an __await__ method. Can be consumed by an await expression in a coroutine. A coroutine waiting for a Future-like object is suspended until the Future-like object's __await__ completes, and returns the result. See Await Expression_ for details.

:Awaitable: A Future-like object or a coroutine object. See Await Expression_ for details.

:Generator-based coroutine: Coroutines based in generator syntax. Most common example is @asyncio.coroutine.

:Asynchronous context manager: An asynchronous context manager has __aenter__ and __aexit__ methods and can be used with async with. See Asynchronous Context Managers and "async with"_ for details.

:Asynchronous iterable: An object with an __aiter__ method, which must return an asynchronous iterator object. Can be used with async for. See Asynchronous Iterators and "async for"_ for details.

:Asynchronous iterator: An asynchronous iterator has an __anext__ method. See Asynchronous Iterators and "async for"_ for details.

List of functions and methods

================= =================================== ================= Method Can contain Can't contain ================= =================================== ================= async def func await, return value yield, yield from async def a* await, return value yield, yield from def a* return awaitable await def await yield, yield from, return iterable await generator yield, yield from, return value await ================= =================================== =================

Where:

Transition Plan

To avoid backwards compatibility issues with async and await keywords, it was decided to modify tokenizer.c in such a way, that it:

This approach allows for seamless combination of new syntax features (all of them available only in async functions) with any existing code.

An example of having "async def" and "async" attribute in one piece of code::

 class Spam:
     async = 42

 async def ham():
     print(getattr(Spam, 'async'))

 # The coroutine can be executed and will print '42'

Backwards Compatibility

This proposal preserves 100% backwards compatibility.

Grammar Updates

Grammar changes are also fairly minimal::

 await_expr: AWAIT test
 await_stmt: await_expr

 decorated: decorators (classdef | funcdef | async_funcdef)
 async_funcdef: ASYNC funcdef

 async_stmt: ASYNC (funcdef | with_stmt | for_stmt)

 compound_stmt: (if_stmt | while_stmt | for_stmt | try_stmt |
                 with_stmt | funcdef | classdef | decorated |
                 async_stmt)

 atom: ('(' [yield_expr|await_expr|testlist_comp] ')' |
       '[' [testlist_comp] ']' |
       '{' [dictorsetmaker] '}' |
       NAME | NUMBER | STRING+ | '...' | 'None' | 'True' | 'False’)

 expr_stmt: testlist_star_expr
                 (augassign (yield_expr|await_expr|testlist) |
                 ('=' (yield_expr|await_expr|testlist_star_expr))*)

Transition Period Shortcomings

There is just one.

Until async and await are not proper keywords, it is not possible (or at least very hard) to fix tokenizer.c to recognize them on the same line with def keyword::

 # async and await will always be parsed as variables

 async def outer():                             # 1
     def nested(a=(await fut)):
         pass

 async def foo(): return (await fut)            # 2

Since await and async in such cases are parsed as NAME tokens, a SyntaxError will be raised.

To workaround these issues, the above examples can be easily rewritten to a more readable form::

 async def outer():                             # 1
     a_default = await fut
     def nested(a=a_default):
         pass

 async def foo():                               # 2
     return (await fut)

This limitation will go away as soon as async and await ate proper keywords. Or if it's decided to use a future import for this PEP.

Deprecation Plans

async and await names will be softly deprecated in CPython 3.5 and 3.6. In 3.7 we will transform them to proper keywords. Making async and await proper keywords before 3.7 might make it harder for people to port their code to Python 3.

asyncio

asyncio module was adapted and tested to work with coroutines and new statements. Backwards compatibility is 100% preserved.

The required changes are mainly:

  1. Modify @asyncio.coroutine decorator to use new types.coroutine() function.

  2. Add __await__ = __iter__ line to asyncio.Future class.

  3. Add ensure_task() as an alias for async() function. Deprecate async() function.

Design Considerations

PEP 3152

PEP 3152 by Gregory Ewing proposes a different mechanism for coroutines (called "cofunctions"). Some key points:

  1. A new keyword codef to declare a cofunction. Cofunction is always a generator, even if there is no cocall expressions inside it. Maps to async def in this proposal.

  2. A new keyword cocall to call a cofunction. Can only be used inside a cofunction. Maps to await in this proposal (with some differences, see below.)

  3. It is not possible to call a cofunction without a cocall keyword.

  4. cocall grammatically requires parentheses after it::

    atom: cocall | cocall: 'cocall' atom cotrailer* '(' [arglist] ')' cotrailer: '[' subscriptlist ']' | '.' NAME

  5. cocall f(*args, **kwds) is semantically equivalent to yield from f.__cocall__(*args, **kwds).

Differences from this proposal:

  1. There is no equivalent of __cocall__ in this PEP, which is called and its result is passed to yield from in the cocall expression. await keyword expects an awaitable object, validates the type, and executes yield from on it. Although, __await__ method is similar to __cocall__, but is only used to define Future-like objects.

  2. await is defined in almost the same way as yield from in the grammar (it is later enforced that await can only be inside async def). It is possible to simply write await future, whereas cocall always requires parentheses.

  3. To make asyncio work with PEP 3152 it would be required to modify @asyncio.coroutine decorator to wrap all functions in an object with a __cocall__ method. To call cofunctions from existing generator-based coroutines it would be required to use costart built-in. In this proposal @asyncio.coroutine simply sets CO_COROUTINE on the wrapped function's code object and everything works automatically.

  4. Since it is impossible to call a cofunction without a cocall keyword, it automatically prevents the common mistake of forgetting to use yield from on generator-based coroutines. This proposal addresses this problem with a different approach, see Debugging Features_.

  5. A shortcoming of requiring a cocall keyword to call a coroutine is that if is decided to implement coroutine-generators -- coroutines with yield or async yield expressions -- we wouldn't need a cocall keyword to call them. So we'll end up having __cocall__ and no __call__ for regular coroutines, and having __call__ and no __cocall__ for coroutine- generators.

  6. There are no equivalents of async for and async with in PEP

Coroutine-generators

With async for keyword it is desirable to have a concept of a coroutine-generator -- a coroutine with yield and yield from expressions. To avoid any ambiguity with regular generators, we would likely require to have an async keyword before yield, and async yield from would raise a StopAsyncIteration exception.

While it is possible to implement coroutine-generators, we believe that they are out of scope of this proposal. It is an advanced concept that should be carefully considered and balanced, with a non-trivial changes in the implementation of current generator objects. This is a matter for a separate PEP.

No implicit wrapping in Futures

There is a proposal to add similar mechanism to ECMAScript 7 [2]_. A key difference is that JavaScript "async functions" always return a Promise. While this approach has some advantages, it also implies that a new Promise object is created on each "async function" invocation.

We could implement a similar functionality in Python, by wrapping all coroutines in a Future object, but this has the following disadvantages:

  1. Performance. A new Future object would be instantiated on each coroutine call. Moreover, this makes implementation of await expressions slower (disabling optimizations of yield from).

  2. A new built-in Future object would need to be added.

  3. Coming up with a generic Future interface that is usable for any use case in any framework is a very hard to solve problem.

  4. It is not a feature that is used frequently, when most of the code is coroutines.

Why "async" and "await" keywords

async/await is not a new concept in programming languages:

This is a huge benefit, as some users already have experience with async/await, and because it makes working with many languages in one project easier (Python with ECMAScript 7 for instance).

Why "aiter" is a coroutine

In principle, __aiter__ could be a regular function. There are several good reasons to make it a coroutine:

Importance of "async" keyword

While it is possible to just implement await expression and treat all functions with at least one await as coroutines, this approach makes APIs design, code refactoring and its long time support harder.

Let's pretend that Python only has await keyword::

 def useful():
     ...
     await log(...)
     ...

 def important():
     await useful()

If useful() function is refactored and someone removes all await expressions from it, it would become a regular python function, and all code that depends on it, including important() would be broken. To mitigate this issue a decorator similar to @asyncio.coroutine has to be introduced.

Why "async def"

For some people bare async name(): pass syntax might look more appealing than async def name(): pass. It is certainly easier to type. But on the other hand, it breaks the symmetry between async def, async with and async for, where async is a modifier, stating that the statement is asynchronous. It is also more consistent with the existing grammar.

Why not a future import

__future__ imports are inconvenient and easy to forget to add. Also, they are enabled for the whole source file. Consider that there is a big project with a popular module named "async.py". With future imports it is required to either import it using __import__() or importlib.import_module() calls, or to rename the module. The proposed approach makes it possible to continue using old code and modules without a hassle, while coming up with a migration plan for future python versions.

Why magic methods start with "a"

New asynchronous magic methods __aiter__, __anext__, __aenter__, and __aexit__ all start with the same prefix "a". An alternative proposal is to use "async" prefix, so that __aiter__ becomes __async_iter__. However, to align new magic methods with the existing ones, such as __radd__ and __iadd__ it was decided to use a shorter version.

Why not reuse existing magic names

An alternative idea about new asynchronous iterators and context managers was to reuse existing magic methods, by adding an async keyword to their declarations::

 class CM:
     async def __enter__(self): # instead of __aenter__
         ...

This approach has the following downsides:

Comprehensions

For the sake of restricting the broadness of this PEP there is no new syntax for asynchronous comprehensions. This should be considered in a separate PEP, if there is a strong demand for this feature.

Async lambdas

Lambda coroutines are not part of this proposal. In this proposal they would look like async lambda(parameters): expression. Unless there is a strong demand to have them as part of this proposal, it is recommended to consider them later in a separate PEP.

Performance

Overall Impact

This proposal introduces no observable performance impact. Here is an output of python's official set of benchmarks [4]_:

:: python perf.py -r -b default ../cpython/python.exe ../cpython-aw/python.exe

 [skipped]

 Report on Darwin ysmac 14.3.0 Darwin Kernel Version 14.3.0:
 Mon Mar 23 11:59:05 PDT 2015; root:xnu-2782.20.48~5/RELEASE_X86_64
 x86_64 i386

 Total CPU cores: 8

 ### etree_iterparse ###
 Min: 0.365359 -> 0.349168: 1.05x faster
 Avg: 0.396924 -> 0.379735: 1.05x faster
 Significant (t=9.71)
 Stddev: 0.01225 -> 0.01277: 1.0423x larger

 The following not significant results are hidden, use -v to show them:
 django_v2, 2to3, etree_generate, etree_parse, etree_process, 

fastpickle, fastunpickle, json_dump_v2, json_load, nbody, regex_v8, tornado_http.

Tokenizer modifications

There is no observable slowdown of parsing python files with the modified tokenizer: parsing of one 12Mb file (Lib/test/test_binop.py repeated 1000 times) takes the same amount of time.

async/await

The following micro-benchmark was used to determine performance difference between "async" functions and generators::

 import sys
 import time

 def binary(n):
     if n <= 0:
         return 1
     l = yield from binary(n - 1)
     r = yield from binary(n - 1)
     return l + 1 + r

 async def abinary(n):
     if n <= 0:
         return 1
     l = await abinary(n - 1)
     r = await abinary(n - 1)
     return l + 1 + r

 def timeit(gen, depth, repeat):
     t0 = time.time()
     for _ in range(repeat):
         list(gen(depth))
     t1 = time.time()
     print('{}({}) * {}: total {:.3f}s'.format(
         gen.__name__, depth, repeat, t1-t0))

The result is that there is no observable performance difference. Minimum timing of 3 runs

:: abinary(19) * 30: total 12.985s binary(19) * 30: total 12.953s

Note that depth of 19 means 1,048,575 calls.

Reference Implementation

The reference implementation can be found here: [3]_.

List of high-level changes and new protocols

  1. New syntax for defining coroutines: async def and new await keyword.

  2. New __await__ method for Future-like objects.

  3. New syntax for asynchronous context managers: async with. And associated protocol with __aenter__ and __aexit__ methods.

  4. New syntax for asynchronous iteration: async for. And associated protocol with __aiter__, __aexit__ and new built- in exception StopAsyncIteration.

  5. New AST nodes: AsyncFunctionDef, AsyncFor, AsyncWith, Await.

  6. New functions: sys.set_coroutine_wrapper(callback), sys.get_coroutine_wrapper(), and types.coroutine(gen).

  7. New CO_COROUTINE bit flag for code objects.

While the list of changes and new things is not short, it is important to understand, that most users will not use these features directly. It is intended to be used in frameworks and libraries to provide users with convenient to use and unambiguous APIs with async def, await, async for and async with syntax.

Working example

All concepts proposed in this PEP are implemented [3]_ and can be tested.

:: import asyncio

 async def echo_server():
     print('Serving on localhost:8000')
     await asyncio.start_server(handle_connection,
                                'localhost', 8000)

 async def handle_connection(reader, writer):
     print('New connection...')

     while True:
         data = await reader.read(8192)

         if not data:
             break

         print('Sending {:.10}... back'.format(repr(data)))
         writer.write(data)

 loop = asyncio.get_event_loop()
 loop.run_until_complete(echo_server())
 try:
     loop.run_forever()
 finally:
     loop.close()

References

.. [1] https://docs.python.org/3/library/asyncio-task.html#asyncio.coroutine

.. [2] http://wiki.ecmascript.org/doku.php?id=strawman:async_functions

.. [3] https://github.com/1st1/cpython/tree/await

.. [4] https://hg.python.org/benchmarks

.. [5] https://msdn.microsoft.com/en-us/library/hh191443.aspx

.. [6] http://docs.hhvm.com/manual/en/hack.async.php

.. [7] https://www.dartlang.org/articles/await-async/

.. [8] http://docs.scala-lang.org/sips/pending/async.html

.. [9] https://github.com/google/traceur-compiler/wiki/LanguageFeatures#async-functions-experimental

.. [10] http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2013/n3722.pdf (PDF)

Acknowledgments

I thank Guido van Rossum, Victor Stinner, Elvis Pranskevichus, Andrew Svetlov, and Łukasz Langa for their initial feedback.

Copyright

This document has been placed in the public domain.



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