PEP 525 – Asynchronous Generators | peps.python.org (original) (raw)

Author:

Yury Selivanov

Discussions-To:

Python-Dev list

Status:

Final

Type:

Standards Track

Created:

28-Jul-2016

Python-Version:

3.6

Post-History:

02-Aug-2016, 23-Aug-2016, 01-Sep-2016, 06-Sep-2016

Table of Contents

Abstract
Rationale and Goals
Specification
Performance
- Regular Generators
- Improvements over asynchronous iterators
Design Considerations
Example
Acceptance
Implementation
References
Acknowledgments
Copyright

Abstract

PEP 492 introduced support for native coroutines and async/awaitsyntax 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__ and __anext__ 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: """Yield 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): """Yield numbers from 0 to to every delay seconds.""" for i in range(to): yield i 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 yieldexpressions:

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 42

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:

An __aiter__ method returning an asynchronous iterator.
An __anext__ method returning an awaitable object, which usesStopIteration exception to “yield” values, andStopAsyncIteration exception to signal the end of the iteration.

Asynchronous generators define both of these methods. Let’s manually iterate over a simple asynchronous generator:

async def genfunc(): yield 1 yield 2

gen = genfunc()

assert gen.aiter() is gen

assert await gen.anext() == 1 assert await gen.anext() == 2

await gen.anext() # This line will raise StopAsyncIteration.

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 asasync 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, 1000): if i == 100: break

The above code defines an asynchronous generator that usesasync 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:

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 executeaclose().
Raise a RuntimeError, when an asynchronous generator executes a yield expression in its finally block (using awaitis fine, though):
async def gen():
try:
yield
finally:
await asyncio.sleep(1) # Can use 'await'.
yield # Cannot use 'yield',
# this line will trigger a
# RuntimeError.
Add two new methods to the sys module:set_asyncgen_hooks() and get_asyncgen_hooks().

The idea behind sys.set_asyncgen_hooks() is to allow event loops to intercept asynchronous generators iteration and finalization, so that the end user does not need to care about the finalization problem, and everything just works.

sys.set_asyncgen_hooks() accepts two arguments:

firstiter: a callable which will be called when an asynchronous generator is iterated for the first time.
finalizer: a callable which will be called when an asynchronous generator is about to be GCed.

When an asynchronous generator is iterated for the first time, it stores a reference to the current finalizer.

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 is modified to allow safe finalization of asynchronous generators:

asyncio/base_events.py

class BaseEventLoop:

def run_forever(self):
    ...
    old_hooks = sys.get_asyncgen_hooks()
    sys.set_asyncgen_hooks(finalizer=self._finalize_asyncgen)
    try:
        ...
    finally:
        sys.set_asyncgen_hooks(*old_hooks)
        ...

def _finalize_asyncgen(self, gen):
    self.create_task(gen.aclose())

The second argument, firstiter, allows event loops to maintain a weak set of asynchronous generators instantiated under their control. This makes it possible to implement “shutdown” mechanisms to safely finalize all open generators and close the event loop.

sys.set_asyncgen_hooks() is thread-specific, so several event loops running in parallel threads can use it safely.

sys.get_asyncgen_hooks() returns a namedtuple-like structure with firstiter and finalizer fields.

asyncio

The asyncio event loop will use sys.set_asyncgen_hooks() API to maintain a weak set of all scheduled asynchronous generators, and to schedule their aclose() coroutine methods when it is time for generators to be GCed.

To make sure that asyncio programs can finalize all scheduled asynchronous generators reliably, we propose to add a new event loop coroutine method loop.shutdown_asyncgens(). The method will schedule all currently open asynchronous generators to close with anaclose() call.

After calling the loop.shutdown_asyncgens() method, the event loop will issue a warning whenever a new asynchronous generator is iterated for the first time. The idea is that after requesting all asynchronous generators to be shutdown, the program should not execute code that iterates over new asynchronous generators.

An example of how shutdown_asyncgens coroutine should be used:

try: loop.run_forever() finally: loop.run_until_complete(loop.shutdown_asyncgens()) loop.close()

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:

agen.__aiter__(): Returns agen.
agen.__anext__(): Returns an awaitable, that performs one asynchronous generator iteration when awaited.
agen.asend(val): Returns an awaitable, that pushes theval 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(0.2)
g = gen()
await g.asend(None) # Will return 42 after sleeping
# for 0.1 seconds.

await g.asend('hello') # Will print 'hello' and
# raise StopAsyncIteration
# (after sleeping for 0.2 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 for 0.1 seconds.
v = await g.athrow(ZeroDivisionError)
print(v) # Will print 'world' after
# sleeping 0.2 seconds. 5. agen.aclose(): Returns an awaitable, that throws aGeneratorExit 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. This is similar to the currently availablegi_yieldfrom for generators and cr_await for coroutines. 8. agen.ag_frame, agen.ag_running, and agen.ag_code: defined in the same way as similar attributes of standard generators.

StopIteration and StopAsyncIteration are not propagated out of asynchronous generators, and are replaced with a RuntimeError.

Implementation Details

Asynchronous generator object (PyAsyncGenObject) shares the struct layout with PyGenObject. In addition to that, the reference implementation introduces three new objects:

PyAsyncGenASend: the awaitable object that implements__anext__ and asend() methods.
PyAsyncGenAThrow: the awaitable object that implementsathrow() and aclose() methods.
_PyAsyncGenWrappedValue: every directly yielded object from an asynchronous generator is implicitly boxed into this structure. This is how the generator implementation can separate objects that are yielded using regular iteration protocol from objects that are yielded using asynchronous iteration protocol.

PyAsyncGenASend and PyAsyncGenAThrow are awaitables (they have__await__ methods returning self) and are coroutine-like objects (implementing __iter__, __next__, send() and throw()methods). Essentially, they control how asynchronous generators are iterated:

PyAsyncGenASend and PyAsyncGenAThrow

PyAsyncGenASend is a coroutine-like object that drives __anext__and asend() methods and implements the asynchronous iteration protocol.

agen.asend(val) and agen.__anext__() return instances ofPyAsyncGenASend (which hold references back to the parentagen object.)

The data flow is defined as follows:

When PyAsyncGenASend.send(val) is called for the first time,val is pushed to the parent agen object (using existing facilities of PyGenObject.)
Subsequent iterations over the PyAsyncGenASend objects, pushNone to agen.
When a _PyAsyncGenWrappedValue object is yielded, it is unboxed, and a StopIteration exception is raised with the unwrapped value as an argument.
When PyAsyncGenASend.throw(*exc) is called for the first time,*exc is thrown into the parent agen object.
Subsequent iterations over the PyAsyncGenASend objects, pushNone to agen.
When a _PyAsyncGenWrappedValue object is yielded, it is unboxed, and a StopIteration exception is raised with the unwrapped value as an argument.
return statements in asynchronous generators raiseStopAsyncIteration exception, which is propagated throughPyAsyncGenASend.send() and PyAsyncGenASend.throw() methods.

PyAsyncGenAThrow is very similar to PyAsyncGenASend. The only difference is that PyAsyncGenAThrow.send(), when called first time, throws an exception into the parent agen object (instead of pushing a value into it.)

New Standard Library Functions and Types

types.AsyncGeneratorType – type of asynchronous generator object.
sys.set_asyncgen_hooks() and sys.get_asyncgen_hooks()methods to set up asynchronous generators finalizers and iteration interceptors in event loops.
inspect.isasyncgen() and inspect.isasyncgenfunction()introspection functions.
New method for asyncio event loop: loop.shutdown_asyncgens().
New collections.abc.AsyncGenerator abstract base class.

Backwards Compatibility

The proposal is fully backwards compatible.

In Python 3.5 it is a SyntaxError to define an async deffunction 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 2.3x faster than asynchronous iterators implemented in pure Python:

N = 10 ** 7

async def agen(): for i in range(N): yield i

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 generators 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. And 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 tocontextlib.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.

In terms of implementation, asend() is a slightly more generic version of __anext__, and athrow() is very similar toaclose(). Therefore, having these methods defined for asynchronous generators does not add any extra complexity.

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): for i in range(to): yield i 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()