Mailman 3 April 2015 - Python-ideas

Make range objects orderable
by Riley Banks 25 Apr '15

25 Apr '15

I propose adding the ability to compare range objects using methods (e.g. range.issubrange) and/or regular operators. Example: In [56]: range(0, 10, 3).issubrange(range(10)) Out[56]: True In [57]: range(0, 10, 3) <= range(10) Out[57]: True In [58]: range(10) <= range(0, 10, 3) Out[58]: False I'll write a patch if you decide that this idea is worth implementing.

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concurrency-local namespaces
by Eric Snow 25 Apr '15

25 Apr '15

threading.local provides thread-local namespaces. As far as I can tell, there isn't an equivalent for coroutines (asyncio), even though I would expect they would provide the same benefits. I'd like to see coroutine-local namespaces added and would be happy to work on the problem. I plan on working on a feature that will rely on applying a thread-local context and realized that coroutines would need a similar treatment. Also, there are probably a few spots in the stdlib that would benefit (e.g. decimal contexts). Perhaps I'm missing something. Is the concurrency story with coroutines different enough that coroutine-local namespaces do not make sense? If they still make sense, is there a mechanism to uniquely identify the "currently running" coroutine like you can with threads? That would be necessary to do a naive port of thread-local namespaces. -eric

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Issue with sys.exit and a long
by Ryan Gonzalez 24 Apr '15

24 Apr '15

Observe: ryan@DevPC-LX:~/bfort$ python -c 'import sys; sys.exit(1)' ryan@DevPC-LX:~/bfort$ python -c 'import sys; sys.exit(1L)' 1 ryan@DevPC-LX:~/bfort$ If I call sys.exit with a long value under Python 2, it prints it. I count this as a bug because: 1. int and long are supposed to be as similar as possible. 2. When you're using Hy (which implicitly makes all ints longs), this sucks. -- Ryan [ERROR]: Your autotools build scripts are 200 lines longer than your program. Something’s wrong. http://kirbyfan64.github.io/

4 3

async/await in Python
by Yury Selivanov 22 Apr '15

22 Apr '15

Hello python-ideas, Here's my proposal to add async/await in Python. I believe that PEPs 380 and 3156 were a major breakthrough for Python 3, and I hope that this PEP can be another big step. Who knows, maybe it will be one of the reasons to drive people's interest towards Python 3. PEP: XXX Title: Coroutines with async and await syntax Version: $Revision$ Last-Modified: $Date$ Author: Yury Selivanov <yselivanov(a)sprymix.com> Discussions-To: Python-Dev <python-dev(a)python.org> Python-Version: 3.5 Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 09-Apr-2015 Post-History: Resolution: 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: * it is easy to confuse coroutines with regular generators, since they share the same syntax; async libraries often attempt to alleviate this by using decorators (e.g. ``(a)asyncio.coroutine`` [1]_); * it is not possible to natively define a coroutine which has no ``yield`` or ``yield from`` statements, again requiring the use of decorators to fix potential refactoring issues; * support for asynchronous calls is limited to expressions where ``yield`` is allowed syntactically, limiting the usefulness of syntactic features, such as ``with`` and ``for`` statements. 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). 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: * Coroutines are always generators, even if they do not contain ``await`` expressions. * It is a ``SyntaxError`` to have ``yield`` or ``yield from`` expressions in an ``async`` function. * Internally, a new code object flag - ``CO_ASYNC`` - is introduced to enable runtime detection of coroutines (and migrating existing code). All coroutines have both ``CO_ASYNC`` and ``CO_GENERATOR`` flags set. * Regular generators, when called, return a *generator object*; similarly, coroutines return a *coroutine object*. * ``StopIteration`` exceptions are not propagated out of coroutines, and are replaced with a ``RuntimeError``. For regular generators such behavior requires a future import (see PEP 479). types.async_def() ----------------- A new function ``async_def(gen)`` is added to the ``types`` module. It applies ``CO_ASYNC`` flag to the passed generator's code object, so that it returns 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: * A *coroutine object* returned from a coroutine or a generator decorated with ``types.async_def()``. * An object with an ``__await__`` method returning an iterator. Any ``yield from`` chain of calls ends with a ``yield``. This is a fundamental mechanism of how *Futures* are implemented. Since, internally, coroutines are a special kind of generators, every ``await`` is suspended by a ``yield`` somewhere down the chain of ``await`` calls (please refer to PEP 3156 for a detailed explanation.) To enable this behavior for coroutines, a new magic method called ``__await__`` is added. In asyncio, for instance, to enable Future objects in ``await`` statements, the only change is to add ``__await__ = __iter__`` line to ``asyncio.Future`` class. Objects with ``__await__`` method are called *Future-like* objects in the rest of this PEP. Also, please note that ``__aiter__`` method (see its definition below) cannot be used for this purpose. It is a different protocol, and would be like using ``__iter__`` instead of ``__call__`` for regular callables. 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 data = "abc" it = AsyncIteratorWrapper("abc") async for item in it: print(it) 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. A new method ``set_async_wrapper`` is 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_async_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_async_wrapper(None) # <- this unsets any previously set wrapper assert not isinstance(debug_me(), AsyncDebugWrapper) If ``sys.set_async_wrapper()`` is called twice, the new wrapper replaces the previous wrapper. ``sys.set_async_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. It is consumed by ``await`` in a coroutine. A coroutine waiting for a Future-like object is suspended until the Future-like object's ``__await__`` completes. ``await`` returns the result of the Future-like object. 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 ``(a)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 Future-like await def __await__ yield, yield from, return iterable await generator yield, yield from, return value await ================= ======================================= ================= Where: * ""async def func": coroutine; * "async def __a*__": ``__aiter__``, ``__anext__``, ``__aenter__``, ``__aexit__`` defined with the ``async`` keyword; * "def __a*__": ``__aiter__``, ``__anext__``, ``__aenter__``, ``__aexit__`` defined without the ``async`` keyword, must return an *awaitable*; * "def __await__": ``__await__`` method to implement *Future-like* objects; * generator: a "regular" generator, function defined with ``def`` and which contains a least one ``yield`` or ``yield from`` expression. *Future-like* is an object with an ``__await__`` method, see `Await Expression`_ section for details. 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: * recognizes ``async def`` name tokens combination (start of a coroutine); * keeps track of regular functions and coroutines; * replaces ``'async'`` token with ``ASYNC`` and ``'await'`` token with ``AWAIT`` when in the process of yielding tokens for coroutines. 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 ----------------------- The only backwards incompatible change is an extra argument ``is_async`` to ``FunctionDef`` AST node. But since it is a documented fact that the structure of AST nodes is an implementation detail and subject to change, this should not be considered a serious issue. 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) # will add for_stmt later 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 ``(a)asyncio.coroutine`` decorator to use new ``types.async_def()`` 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 ===================== 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: * C# has it since long time ago [5]_; * proposal to add async/await in ECMAScript 7 [2]_; see also Traceur project [9]_; * Facebook's Hack/HHVM [6]_; * Google's Dart language [7]_; * Scala [8]_; * proposal to add async/await to C++ [10]_; * and many other less popular 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: * as most of the ``__anext__``, ``__aenter__``, and ``__aexit__`` methods are coroutines, users would often make a mistake defining it as ``async`` anyways; * there might be a need to run some asynchronous operations in ``__aiter__``, for instance to prepare DB queries or do some file operation. 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 ``(a)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: * it would not be possible to create an object that works in both ``with`` and ``async with`` statements; * it would look confusing and would require some implicit magic behind the scenes in the interpreter; * one of the main points of this proposal is to make coroutines as simple and foolproof as possible. 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. 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: ``AsyncFor``, ``AsyncWith``, ``Await``; ``FunctionDef`` AST node got a new argument ``is_async``. 6. New functions: ``sys.set_async_wrapper(callback)`` and ``types.async_def(gen)``. 7. New ``CO_ASYNC`` 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-func… .. [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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PEP 492 update
by Yury Selivanov 21 Apr '15

21 Apr '15

Hi python-ideas, I'm moving the discussion of updated PEP 492 to python-dev. Thanks, Yury

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Re: [Python-ideas] async/await in Python
by Luciano Ramalho 20 Apr '15

20 Apr '15

Yuri, thank you for writing PEP 492! Victor Stinner reviewed the asyncio chapter in my book, Fluent Python [1], and he saw how much I complained about the overloading of `def` to define objects as different as functions, generators and coroutines, and about `yield from` which doesn't say anything to me or to many others to whom I've talked. With `async def` and `await` you solve both these problems! Also, `async with` and `async for` are brilliant. For the first time ever coroutines will be real first class citizens in Python! [1] http://shop.oreilly.com/product/0636920032519.do Alas, the book [1] is entering final production so it won't cover Python 3.5, but I do hope to see what you propose in Python 3.5, and if there is a second edition, it will be a pleasure to explain `async` and `await`! I did have time to add a paragraph about PEP 492 in a Further reading section, citing your name as the author. Cheers, Luciano -- Luciano Ramalho | Author of Fluent Python (O'Reilly, 2015) | http://shop.oreilly.com/product/0636920032519.do | Professor em: http://python.pro.br | Twitter: @ramalhoorg

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Fix that broken callable builtin
by Ionel Cristian Mărieș 19 Apr '15

19 Apr '15

Hello, I had an issue today with the `callable` builtin because it doesn't correctly check that the object has the __call__ attribute. Effectively what `callable(a)` does is `hasattr(type(a), '__call__')` but that's not very straightforward. A more straightforward implementation would do something like `hasattr(a, '__call__')`. For example: Python 3.4.3 (v3.4.3:9b73f1c3e601, Feb 24 2015, 22:44:40) [MSC v.1600 64 > bit (AMD64)] on win32 > Type "help", "copyright", "credits" or "license" for more information. > >>> callable > <built-in function callable> > >>> class A: > ... @property > ... def __call__(self): > ... raise AttributeError('go away') > ... > >>> a = A() > >>> a > <__main__.A object at 0x000000000365B5C0> > >>> a.__call__ > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > File "<stdin>", line 4, in __call__ > AttributeError: go away > >>> callable(a) > True > >>> # it should be False :( > So it boils down to this: > >>> hasattr(a, "__call__") > False > >>> hasattr(type(a), "__call__") > True My issue is that I didn't call `callable(type(a))` but just `callable(a)`. Clearly mismatching what happens when you do hasattr(a, "__call__"). To put in contrast, this is legal and clearly indicates the descriptors are being used as expected: >>> class B: > ... @property > ... def __call__(self): > ... return lambda: 1 > ... > >>> b = B() > >>> b() > 1 > There 's some more discussing in issue 23990 <http://bugs.python.org/issue23990> where I get slightly angry, sorry. So were is this change actually useful? Proxies! Since new-style objects in Python you cannot really proxy the callable aspect of objects, because `callable` just checks that a field is set in a C struct. This is fairly inconvenient because you have to know upfront if your target is going to be callable or not. Thanks, -- Ionel Cristian Mărieș, http://blog.ionelmc.ro

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with-except-finally blocks
by Kale Kundert 19 Apr '15

19 Apr '15

Hello everyone, I'd like to propose a little bit of syntactic sugar: allowing with-blocks to be followed by except- and finally-blocks just like try-blocks. For example: with open('spam.txt') as file: print(file.read()) except IOError: print('No spam here...') finally: print('done.') This proposed syntax is semantically equivalent to wrapping a with-block within a try-block like so: try: with open('spam.txt') as file: print(file.read()) finally: print('done.') except IOError: print('No spam here...') I see two advantages to the proposed syntax. First and most obviously, it saves an extra line and an extra indentation level. One line may not be a big deal, but one indentation level can really affect readability. Second and more conceptually, it makes sense to think about exception handling in the context of a with-block. More often than not, if you're using a with-block, you're expecting that something in that block could throw an exception. Usually with-blocks are used to make sure resources (e.g. files, database sessions, mutexes, etc.) are properly closed before the exception is propogated. But I very often want to do some custom clean-up as well (alert the user, etc.). Currently that requires wrapping the whole thing in a try-block, but allowing with-blocks to behave as try-blocks is a more direct way to express what is meant. I was curious how often with-blocks are actually wrapped in try-blocks for no other purpose than catching exceptions raised in the with-block. So I searched through a number of open source projects looking (roughly) for that pattern: Project with [1] try-with [2] ============== ======== ============ django 230 17 ipython 541 8 matplotlib 112 3 moinmoin 10 0 numpy 166 1 pillow/pil 1 0 pypy 254 4 scipy 163 2 sqlalchemy 36 0 twisted 72 1 ============== ======== ============ total 1585 36 (2.27%) [1]: grep -Po '^\s*with .*:' **/*.py [2]: grep -Poz 'try:\s*with .*:' **/*.py Assuming these projects are representative, about 2% of the with-blocks are directly wrapped by try-blocks. That's not a huge proportion, but it clearly shows that this pattern is being used "in the wild". Whether or not it's worth changing the language for the benefit of 2% of with-blocks is something to debate though. What do people think of this idea? -Kale Kundert

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Re: [Python-ideas] async/await in Python
by Piotr Jurkiewicz 19 Apr '15

19 Apr '15

1. Overall I like the proposal very much. However, I have got one semantic remark. You propose `async for` as a syntax for asynchronous iterators: async for row in Cursor(): print(row) Wouldn't it be more semantically correct to use `await for` instead of `async for`? await for row in Cursor(): print(row) For me the word 'await' is an indicator that I am awaiting for some value being returned. For example, with simple `await` expression I am awaiting for a data being fetched from db: data = await db.fetch('SELECT ...') When I use asynchronous iterator I am awaiting for a value being returned as well. For example I am awaiting (in each iteration) for a row from a cursor. Therefore, it seems to me to be natural to use word 'await' instead of 'async'. Furthermore syntax 'await for row in cursor' reassembles natural English language. On the other hand, when I use context manager, I am not awaiting for any value, so syntax `async with` seems to be proper in that case: async with session.transaction(): ... await session.update(data) Dart, for example, goes that way. They use `await` expression for awaiting single Future and `await for` statement for asynchronous iterators: await for (variable declaration in expression) { // Executes each time the stream emits a value. } 2. I would like to go little beyond this proposal and think about composition of async coroutines (aka waiting for multiple coroutines). For example C# has helper functions WhenAll and WhenAny for that: await Task.WhenAll(tasks_list); await Task.WhenAny(tasks_list); In asyncio module there is a function asyncio.wait() which can be used to achieve similar result: asyncio.wait(fs, timeout=None, return_when=ALL_COMPLETED) asyncio.wait(fs, timeout=None, return_when=FIRST_COMPLETED) However, after introduction of `await` its name becomes problematic. First, it reassembles `await` too much and can cause a confusion. Second, its usage would result in an awkward 'await wait': done, pending = await asyncio.wait(coroutines_list) results = [] for task in done: results.append(task.result()) Another problem with asyncio.wait() is that it returns Tasks, not their results directly, so user has to unpack them. There is function asyncio.gather(*coros_or_futures) which return results list directly, however it can be only used for ALL_COMPLETED case. There is also a function asyncio.wait_for() which (unlike asyncio.wait()) unpacks the result, but can only be used for one coroutine (so what is the difference from `await` expression?). Finally, there is asyncio.as_completed() which returns iterator for iterating over coroutines results as they complete (but I don't know how exactly this iterator relates to async iterators proposed here). I can imagine the set of three functions being exposed to user to control waiting for multiple coroutines: asynctools.as_done() # returns asynchronous iterator for iterating over the results of coroutines as they complete asynctools.all_done() # returns a future aggregating results from the given coroutine objects, which awaited returns list of results (like asyncio.gather()) asynctools.any_done() # returns a future, which awaited returns result of first completed coroutine Example: from asynctools import as_done, all_done, any_done corobj0 = async_sql_query("SELECT...") corobj1 = async_memcached_get("someid") corobj2 = async_http_get("http://python.org") # ------------------------------------------------ # Iterate over results as coroutines complete # using async iterator await for result in as_done([corobj0, corobj1, corobj2]): print(result) # ------------------------------------------------ # Await for results of all coroutines # using async iterator results = [] await for result in as_done([corobj0, corobj1, corobj2]): results.append(result) # or using shorthand coroutine all_done() results = await all_done([corobj0, corobj1, corobj2]) # ------------------------------------------------ # Await for a result of first completed coroutine # using async iterator await for result in as_done([corobj0, corobj1, corobj2]): first_result = result break # or using shorthand coroutine any_done() first_result = await any_done([corobj0, corobj1, corobj2]) I deliberately placed these functions in a new asynctools module, not in the asyncio module. I find asyncio module being too much complicated to expose it to an ordinary user. There are four very similar concepts used in it: Coroutine (function), Coroutine (object), Future and Task. In addition many functions accept both coroutines and Futures in the same argument, Task is a subclass of Future -- it makes people very confused. It is difficult to grasp what are differences between them and how they relate to each other. For comparison in JavaScript that are only two concepts: async functions and Promises. (Furthermore, after this PEP being accepted there will be fifth concept: old-style coroutines. And there are also concurrent.futures.Futures...) Personally, I think that asyncio module should be refactored and broken into two separate modules, named for example: - asyncloop # consisting low-level loop-related things, mostly not intended to be used by the average user (apart from get_event_loop() and run_until_xxx()) - asynctools # consisting high-level helper functions, like described before As with this PEP async/await will become first class member of Python environment, all rest high-level functions should be in my opinion moved from asyncio to appropriate modules, like socket or subprocess. These are the places where users will be looking for them. For example: socket.socket.recv() socket.socket.recv_async() socket.socket.sendall() socket.socket.sendall_async() socket.getaddrinfo() socket.getaddrinfo_async() Finally, concurrent.futures should either be renamed to avoid the usage of word 'future', or be made compatible with async/await. I know that I went far beyond scope of this PEP, but I think that these are the issues which will pop up after acceptance of this PEP sooner or later. Finally, I remind about my proposal from the beginning of this email, to use `await for` instead of `async for` for asynchronous iterators. What's your opinion about that? Piotr

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PEP 484 (Type Hints) discussion moved to python-dev
by Guido van Rossum 18 Apr '15

18 Apr '15

I've posted a new draft of PEP 484, but in order to reach the right audience from now on I'm posting to python-dev. See you there! Please don't reply here, it's best to have all discussion in one place. -- --Guido van Rossum (python.org/~guido)

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Python-ideas April 2015