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August 2016

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PEP: asynchronous generators
by Yury Selivanov Aug. 12, 2016

Aug. 12, 2016

Hi, I have been working on a PEP to add asynchronous generators to Python. The PEP is now ready for a review. It would be great to hear some initial feedback from async-sig, before I post it to python-ideas. I have a complete and working reference implementation of everything that PEP proposes here: https://github.com/1st1/cpython/tree/async_gen PEP: XXX Title: Asynchronous Generators Version: $Revision$ Last-Modified: $Date$ Author: Yury Selivanov <yury(a)magic.io> Discussions-To: <python-dev(a)python.org> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 28-Jul-2016 Python-Version: 3.6 Post-History: Abstract ======== PEP 492 introduced support for native coroutines and ``async``/``await`` syntax to Python 3.5. It is proposed here to extend Python's asynchronous capabilities by adding support for *asynchronous generators*. Rationale and Goals =================== Regular generators (introduced in PEP 255) enabled an elegant way of writing complex *data producers* and have them behave like an iterator. However, currently there is no equivalent concept for the *asynchronous iteration protocol* (``async for``). This makes writing asynchronous data producers unnecessarily complex, as one must define a class that implements ``__aiter__`` to be able to use it in an ``async for`` statement. Essentially, the goals and rationale for PEP 255, applied to the asynchronous execution case, hold true for this proposal as well. Performance is an additional point for this proposal: in our testing of the reference implementation, asynchronous generators are *2x* faster than an equivalent implemented as an asynchronous iterator. As an illustration of the code quality improvement, consider the following class that prints numbers with a given delay once iterated:: class ticker: """Print numbers from 0 to `to` every `delay` seconds.""" def __init__(self, delay, to): self.delay = delay self.i = 0 self.to = to def __aiter__(self): return self async def __anext__(self): i = self.i if i >= self.to: raise StopAsyncIteration self.i += 1 if i: await asyncio.sleep(self.delay) return i The same can be implemented as a much simpler asynchronous generator:: async def ticker(delay, to): """Print numbers from 0 to `to` every `delay` seconds.""" i = 0 while i < to: yield i i += 1 await asyncio.sleep(delay) Specification ============= This proposal introduces the concept of *asynchronous generators* to Python. This specification presumes knowledge of the implementation of generators and coroutines in Python (PEP 342, PEP 380 and PEP 492). Asynchronous Generators ----------------------- A Python *generator* is any function containing one or more ``yield`` expressions:: def func(): # a function return def genfunc(): # a generator function yield We propose to use the same approach to define *asynchronous generators*:: async def coro(): # a coroutine function await smth() async def asyncgen(): # an asynchronous generator function await smth() yield val The result of calling an *asynchronous generator function* is an *asynchronous generator object*, which implements the asynchronous iteration protocol defined in PEP 492. It is a ``SyntaxError`` to have a non-empty ``return`` statement in an asynchronous generator. Support for Asynchronous Iteration Protocol ------------------------------------------- The protocol requires two special methods to be implemented: 1. An ``__aiter__`` method returning an *asynchronous iterator*. 2. An ``__anext__`` method returning an *awaitable* object, which uses ``StopIteration`` exception to "yield" values, and ``StopAsyncIteration`` exception to signal the end of the iteration. Asynchronous generators define both of these methods:: async def genfunc(): yield 1 yield 2 gen = genfunc() assert gen.__aiter__() is gen assert await gen.__anext__() == 1 assert await gen.__anext__() == 2 with assertRaises(StopAsyncIteration): await gen.__anext__() Finalization ------------ PEP 492 requires an event loop or a scheduler to run coroutines. Because asynchronous generators are meant to be used from coroutines, they also require an event loop to run and finalize them. Asynchronous generators can have ``try..finally`` blocks, as well as ``async with``. It is important to provide a guarantee that, even when partially iterated, and then garbage collected, generators can be safely finalized. For example:: async def square_series(con, to): async with con.transaction(): cursor = con.cursor( 'SELECT generate_series(0, $1) AS i', to) async for row in cursor: yield row['i'] ** 2 async for i in square_series(con, 100): if i == 100: break The above code defines an asynchronous generator that uses ``async with`` to iterate over a database cursor in a transaction. The generator is then iterated over with ``async for``, which interrupts the iteration at some point. The ``square_series()`` generator will then be garbage collected, and without a mechanism to asynchronously close the generator, Python interpreter would not be able to do anything. To solve this problem we propose to do the following: 1. Implement an ``aclose`` method on asynchronous generators returning a special *awaitable*. When awaited it throws a ``GeneratorExit`` into the suspended generator and iterates over it until either a ``GeneratorExit`` or a ``StopAsyncIteration`` occur. This is very similar to what the ``close()`` method does to regular Python generators, except that an event loop is required to execute ``aclose()``. 2. Raise a ``RuntimeError``, when an asynchronous generator executes a ``yield`` expression in its ``finally`` block (using ``await`` is fine, though):: async def gen(): try: yield finally: yield # Cannot use 'yield' await asyncio.sleep(1) # Can use 'await' 3. Add two new methods to the ``sys`` module: ``set_asyncgen_finalizer`` and ``get_asyncgen_finalizer``. The idea behind ``sys.set_asyncgen_finalizer`` is to allow event loops to handle generators finalization, so that the end user does not need to care about the finalization problem, and it just works. When an asynchronous generator is iterated for the first time, it stores a reference to the current finalizer. If there is none, a ``RuntimeError`` is raised. This provides a strong guarantee that every asynchronous generator object will always have a finalizer installed by the correct event loop. When an asynchronous generator is about to be garbage collected, it calls its cached finalizer. The assumption is that the finalizer will schedule an ``aclose()`` call with the loop that was active when the iteration started. For instance, here is how asyncio can be modified to allow safe finalization of asynchronous generators:: # asyncio/base_events.py class BaseEventLoop: def run_forever(self): ... old_finalizer = sys.get_asyncgen_finalizer() sys.set_asyncgen_finalizer(self._finalize_asyncgen) try: ... finally: sys.set_asyncgen_finalizer(old_finalizer) ... def _finalize_asyncgen(self, gen): self.create_task(gen.aclose()) ``sys.set_asyncgen_finalizer`` is thread-specific, so several event loops running in parallel threads can use it safely. Asynchronous Generator Object ----------------------------- The object is modeled after the standard Python generator object. Essentially, the behaviour of asynchronous generators is designed to replicate the behaviour of synchronous generators, with the only difference in that the API is asynchronous. The following methods and properties are defined: 1. ``agen.__aiter__()``: Returns ``agen``. 2. ``agen.__anext__()``: Returns an *awaitable*, that performs one asynchronous generator iteration when awaited. 3. ``agen.anext(val)``: Returns an *awaitable*, that pushes the ``val`` object in the ``agen`` generator. When the ``agen`` has not yet been iterated, ``val`` must be ``None``. Example:: async def gen(): await asyncio.sleep(0.1) v = yield 42 print(v) await asyncio.sleep(0.1) g = gen() await g.send(None) # Will return 42 await g.send('hello') # Will print 'hello' and # raise StopAsyncIteration # (after sleeping for 0.1 seconds) 4. ``agen.athrow(typ, [val, [tb]])``: Returns an *awaitable*, that throws an exception into the ``agen`` generator. Example:: async def gen(): try: await asyncio.sleep(0.1) yield 'hello' except ZeroDivisionError: await asyncio.sleep(0.2) yield 'world' g = gen() v = await g.asend(None) print(v) # Will print 'hello' after sleeping 0.1s v = await g.athrow(ZeroDivisionError) print(v) # Will print 'world' after sleeping 0.2s 5. ``agen.aclose()``: Returns an *awaitable*, that throws a ``GeneratorExit`` exception into the generator. The *awaitable* can either return a yielded value, if ``agen`` handled the exception, or ``agen`` will be closed and the exception will propagate back to the caller. 6. ``agen.__name__`` and ``agen.__qualname__``: readable and writable name and qualified name attributes. 7. ``agen.ag_await``: The object that ``agen`` is currently awaiting on, or ``None``. 8. ``agen.ag_frame``, ``agen.ag_running``, and ``agen.ag_code``: defined in the same way as similar attributes of standard generators. New Standard Library Functions and Types ---------------------------------------- 1. ``types.AsyncGeneratorType`` -- type of asynchronous generator object. 2. ``sys.set_asyncgen_finalizer()`` and ``sys.get_asyncgen_finalizer()`` methods to set up asynchronous generators finalizers in event loops. 3. ``inspect.isasyncgen()`` and ``inspect.isasyncgenfunction()`` introspection functions. Backwards Compatibility ----------------------- The proposal is fully backwards compatible. In Python 3.5 it is a ``SyntaxError`` to define an ``async def`` function with a ``yield`` expression inside, therefore it's safe to introduce asynchronous generators in 3.6. Performance =========== Regular Generators ------------------ There is no performance degradation for regular generators. The following micro benchmark runs at the same speed on CPython with and without asynchronous generators:: def gen(): i = 0 while i < 100000000: yield i i += 1 list(gen()) Improvements over asynchronous iterators ---------------------------------------- The following micro-benchmark shows that asynchronous generators are about **2x faster** than asynchronous iterators implemented in pure Python: async def agen(): i = 0 while i < N: yield i i += 1 class AIter: def __init__(self): self.i = 0 def __aiter__(self): return self async def __anext__(self): i = self.i if i >= N: raise StopAsyncIteration self.i += 1 return i Design Considerations ===================== ``aiter()`` and ``anext()`` builtins ------------------------------------ Originally PEP 492 defined ``__aiter__`` as a method that should return an *awaitable* object, resulting in an asynchronous iterator. However, in CPython 3.5.2, ``__aiter__`` was redefined to return asynchronous iterators directly. To avoid breaking backwards compatibility, it was decided that Python 3.6 will support both ways: ``__aiter__`` can still return an *awaitable* with a ``DeprecationWarning`` being issued. Because of this dual nature of ``__aiter__`` in Python 3.6, we cannot add a synchronous implementation of ``aiter()`` built-in. Therefore, it is proposed to wait until Python 3.7. Asynchronous list/dict/set comprehensions ----------------------------------------- Syntax for asynchronous comprehensions is unrelated to the asynchronous generators machinery, and should be considered in a separate PEP. Asynchronous ``yield from`` --------------------------- While it is theoretically possible to implement ``yield from`` support for asynchronous generators, it would require a serious redesign of the generator implementation. ``yield from`` is also less critical for asynchronous generators, since there is no need provide a mechanism of implementing another coroutines protocol on top of coroutines. To compose asynchronous generators a simple ``async for`` loop can be used:: async def g1(): yield 1 yield 2 async def g2(): async for v in g1(): yield v Why the ``asend`` and ``athrow`` methods are necessary ------------------------------------------------------ They make it possible to implement concepts similar to ``contextlib.contextmanager`` using asynchronous generators. For instance, with the proposed design, it is possible to implement the following pattern:: @async_context_manager async def ctx(): await open() try: yield finally: await close() async with ctx(): await ... Another reason is that it is possible to push data and throw exceptions into asynchronous generators using the object returned from ``__anext__`` object, but it is hard to do that correctly. Adding explicit ``asend`` and ``athrow`` will pave a safe way to accomplish that. Example ======= A working example with the current reference implementation (will print numbers from 0 to 9 with one second delay):: async def ticker(delay, to): i = 0 while i < to: yield i i += 1 await asyncio.sleep(delay) async def run(): async for i in ticker(1, 10): print(i) import asyncio loop = asyncio.get_event_loop() try: loop.run_until_complete(run()) finally: loop.close() Implementation ============== The complete reference implementation is available at [1]_. References ========== .. [1] https://github.com/1st1/cpython/tree/async_gen Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End: Thank you! Yury

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Warts both in asyncio way and greenlet-based approach
by Andrew Svetlov Aug. 5, 2016

Aug. 5, 2016

Long story short: in async world every IO library should use asyncio. Unfortunately for the most popular libraries (requests, django, flask, sqlalchemy etc.) it's impossible to rewrite the code keeping backward compatibility. Much easier to create a new library than rewrite existing one. Regarding to gevent -- please read excellent Glyph's article 'Unyielding': https://glyph.twistedmatrix.com/2014/02/unyielding.html It covers pretty well why explicit yield points are better than implicit ones. Shortly explicit `await`'s give you 'atomic consistency' but using gevent you should expect context switch in every opcode instruction. It's much easier to test cover code with several (ok, many) context switch points. But test covering the code which may switch on every line and several times in the same line is practically impossible. Also my 2 cents. We used gevent. Even in very tiny application (command line client to upload huge files over HTTP, about 1k lines of code) under high load we got reports about gevent hub crashes. Even gevent core (hub is something like asyncio loop) is not 100% stable. Unfortunately we was not able to reproduce the problem by our test suite -- it requires really high load and occurs very rare. Well, the most gevent users can live with that -- gunicorn just restarts dead worker and that's it. But it is the sign for the problem: gevent crashes are extremely hard to debug. P.S. You might keep my writings about gevent as my private opinion, and it is. But from my perspective asyncio based solutions have much more predictable behavior and much more friendly to debug-and-fix problems. -- Thanks, Andrew Svetlov

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