我有一个list awaitables我希望传递给asyncio.AbstractEventLoop，但我需要限制对第三方API的请求。
我想避免等待将future传递给循环的东西，因为在此期间我阻止我的循环等待。我有什么选择？ Semaphores和ThreadPools将限制同时运行的数量，但这不是我的问题。我需要将我的请求限制为100 /秒，但完成请求所需的时间并不重要。
这是一个使用标准库的非常简洁（非）工作示例，它演示了该问题。这应该以100 /秒的速度节流，但节流速度为116.651 /秒。 在asyncio 中限制异步请求计划的最佳方法是什么？
工作代码：
import asyncio
from threading import Lock
class PTBNL:
def __init__(self):
self._req_id_seq = 0
self._futures = {}
self._results = {}
self.token_bucket = TokenBucket()
self.token_bucket.set_rate(100)
def run(self, *awaitables):
loop = asyncio.get_event_loop()
if not awaitables:
loop.run_forever()
elif len(awaitables) == 1:
return loop.run_until_complete(*awaitables)
else:
future = asyncio.gather(*awaitables)
return loop.run_until_complete(future)
def sleep(self, secs) -> True:
self.run(asyncio.sleep(secs))
return True
def get_req_id(self) -> int:
new_id = self._req_id_seq
self._req_id_seq += 1
return new_id
def start_req(self, key):
loop = asyncio.get_event_loop()
future = loop.create_future()
self._futures[key] = future
return future
def end_req(self, key, result=None):
future = self._futures.pop(key, None)
if future:
if result is None:
result = self._results.pop(key, [])
if not future.done():
future.set_result(result)
def req_data(self, req_id, obj):
# Do Some Work Here
self.req_data_end(req_id)
pass
def req_data_end(self, req_id):
print(req_id, " has ended")
self.end_req(req_id)
async def req_data_async(self, obj):
req_id = self.get_req_id()
future = self.start_req(req_id)
self.req_data(req_id, obj)
await future
return future.result()
async def req_data_batch_async(self, contracts):
futures = []
FLAG = False
for contract in contracts:
req_id = self.get_req_id()
future = self.start_req(req_id)
futures.append(future)
nap = self.token_bucket.consume(1)
if FLAG is False:
FLAG = True
start = asyncio.get_event_loop().time()
asyncio.get_event_loop().call_later(nap, self.req_data, req_id, contract)
await asyncio.gather(*futures)
elapsed = asyncio.get_event_loop().time() - start
return futures, len(contracts)/elapsed
class TokenBucket:
def __init__(self):
self.tokens = 0
self.rate = 0
self.last = asyncio.get_event_loop().time()
self.lock = Lock()
def set_rate(self, rate):
with self.lock:
self.rate = rate
self.tokens = self.rate
def consume(self, tokens):
with self.lock:
if not self.rate:
return 0
now = asyncio.get_event_loop().time()
lapse = now - self.last
self.last = now
self.tokens += lapse * self.rate
if self.tokens > self.rate:
self.tokens = self.rate
self.tokens -= tokens
if self.tokens >= 0:
return 0
else:
return -self.tokens / self.rate
if __name__ == '__main__':
asyncio.get_event_loop().set_debug(True)
app = PTBNL()
objs = [obj for obj in range(500)]
l,t = app.run(app.req_data_batch_async(objs))
print(l)
print(t)
编辑：我在这里使用信号量添加了TrottleTestApp的简单示例，但仍然无法限制执行：
import asyncio
import time
class ThrottleTestApp:
def __init__(self):
self._req_id_seq = 0
self._futures = {}
self._results = {}
self.sem = asyncio.Semaphore()
async def allow_requests(self, sem):
"""Permit 100 requests per second; call
loop.create_task(allow_requests())
at the beginning of the program to start this routine. That call returns
a task handle that can be canceled to end this routine.
asyncio.Semaphore doesn't give us a great way to get at the value other
than accessing sem._value. We do that here, but creating a wrapper that
adds a current_value method would make this cleaner"""
while True:
while sem._value < 100: sem.release()
await asyncio.sleep(1) # Or spread more evenly
# with a shorter sleep and
# increasing the value less
async def do_request(self, req_id, obj):
await self.sem.acquire()
# this is the work for the request
self.req_data(req_id, obj)
def run(self, *awaitables):
loop = asyncio.get_event_loop()
if not awaitables:
loop.run_forever()
elif len(awaitables) == 1:
return loop.run_until_complete(*awaitables)
else:
future = asyncio.gather(*awaitables)
return loop.run_until_complete(future)
def sleep(self, secs: [float]=0.02) -> True:
self.run(asyncio.sleep(secs))
return True
def get_req_id(self) -> int:
new_id = self._req_id_seq
self._req_id_seq += 1
return new_id
def start_req(self, key):
loop = asyncio.get_event_loop()
future = loop.create_future()
self._futures[key] = future
return future
def end_req(self, key, result=None):
future = self._futures.pop(key, None)
if future:
if result is None:
result = self._results.pop(key, [])
if not future.done():
future.set_result(result)
def req_data(self, req_id, obj):
# This is the method that "does" something
self.req_data_end(req_id)
pass
def req_data_end(self, req_id):
print(req_id, " has ended")
self.end_req(req_id)
async def req_data_batch_async(self, objs):
futures = []
FLAG = False
for obj in objs:
req_id = self.get_req_id()
future = self.start_req(req_id)
futures.append(future)
if FLAG is False:
FLAG = True
start = time.time()
self.do_request(req_id, obj)
await asyncio.gather(*futures)
elapsed = time.time() - start
print("Roughly %s per second" % (len(objs)/elapsed))
return futures
if __name__ == '__main__':
asyncio.get_event_loop().set_debug(True)
app = ThrottleTestApp()
objs = [obj for obj in range(10000)]
app.run(app.req_data_batch_async(objs))
答案 0 :(得分：26)
您可以通过实施leaky bucket algorithm：
来实现这一目标import asyncio
import contextlib
import collections
import time
from types import TracebackType
from typing import Dict, Optional, Type
try: # Python 3.7
base = contextlib.AbstractAsyncContextManager
_current_task = asyncio.current_task
except AttributeError:
base = object # type: ignore
_current_task = asyncio.Task.current_task # type: ignore
class AsyncLeakyBucket(base):
"""A leaky bucket rate limiter.
Allows up to max_rate / time_period acquisitions before blocking.
time_period is measured in seconds; the default is 60.
"""
def __init__(
self,
max_rate: float,
time_period: float = 60,
loop: Optional[asyncio.AbstractEventLoop] = None
) -> None:
self._loop = loop
self._max_level = max_rate
self._rate_per_sec = max_rate / time_period
self._level = 0.0
self._last_check = 0.0
# queue of waiting futures to signal capacity to
self._waiters: Dict[asyncio.Task, asyncio.Future] = collections.OrderedDict()
def _leak(self) -> None:
"""Drip out capacity from the bucket."""
if self._level:
# drip out enough level for the elapsed time since
# we last checked
elapsed = time.time() - self._last_check
decrement = elapsed * self._rate_per_sec
self._level = max(self._level - decrement, 0)
self._last_check = time.time()
def has_capacity(self, amount: float = 1) -> bool:
"""Check if there is enough space remaining in the bucket"""
self._leak()
requested = self._level + amount
# if there are tasks waiting for capacity, signal to the first
# there there may be some now (they won't wake up until this task
# yields with an await)
if requested < self._max_level:
for fut in self._waiters.values():
if not fut.done():
fut.set_result(True)
break
return self._level + amount <= self._max_level
async def acquire(self, amount: float = 1) -> None:
"""Acquire space in the bucket.
If the bucket is full, block until there is space.
"""
if amount > self._max_level:
raise ValueError("Can't acquire more than the bucket capacity")
loop = self._loop or asyncio.get_event_loop()
task = _current_task(loop)
assert task is not None
while not self.has_capacity(amount):
# wait for the next drip to have left the bucket
# add a future to the _waiters map to be notified
# 'early' if capacity has come up
fut = loop.create_future()
self._waiters[task] = fut
try:
await asyncio.wait_for(
asyncio.shield(fut),
1 / self._rate_per_sec * amount,
loop=loop
)
except asyncio.TimeoutError:
pass
fut.cancel()
self._waiters.pop(task, None)
self._level += amount
return None
async def __aenter__(self) -> None:
await self.acquire()
return None
async def __aexit__(
self,
exc_type: Optional[Type[BaseException]],
exc: Optional[BaseException],
tb: Optional[TracebackType]
) -> None:
return None
请注意，我们机会性地从桶中泄漏容量，不需要运行单独的异步任务只是为了降低级别;相反，在测试足够的剩余容量时，容量会被泄露出来。
请注意，等待容量的任务保存在有序字典中，当有可能再次备用容量时，第一个仍在等待的任务会提前唤醒。
您可以将其用作上下文管理器;尝试在满桶时获取存储桶，直到再次释放足够的容量为止：
bucket = AsyncLeakyBucket(100)
# ...
async with bucket:
# only reached once the bucket is no longer full
或者您可以直接致电acquire()：
await bucket.acquire() # blocks until there is space in the bucket
或者您可以直接测试是否有空间：
if bucket.has_capacity():
# reject a request due to rate limiting
请注意，您可以将一些请求计算为“更重”的请求。或者打火机＆＃39;通过增加或减少你滴落的量来减少进入水桶：
await bucket.acquire(10)
if bucket.has_capacity(0.5):
但要小心这一点;当混合大小滴水时，当滴水达到或接近最大速率时，小滴水倾向于在大滴水之前运行，因为更大的可能性是在有较大滴水的空间之前有足够的自由容量用于较小的滴水。
演示：
>>> import asyncio, time
>>> bucket = AsyncLeakyBucket(5, 10)
>>> async def task(id):
... await asyncio.sleep(id * 0.01)
... async with bucket:
... print(f'{id:>2d}: Drip! {time.time() - ref:>5.2f}')
...
>>> ref = time.time()
>>> tasks = [task(i) for i in range(15)]
>>> result = asyncio.run(asyncio.wait(tasks))
0: Drip! 0.00
1: Drip! 0.02
2: Drip! 0.02
3: Drip! 0.03
4: Drip! 0.04
5: Drip! 2.05
6: Drip! 4.06
7: Drip! 6.06
8: Drip! 8.06
9: Drip! 10.07
10: Drip! 12.07
11: Drip! 14.08
12: Drip! 16.08
13: Drip! 18.08
14: Drip! 20.09
铲斗在开始时快速填满，使其余任务更均匀地展开;每2秒就可以释放足够的容量来处理另一个任务。
最大突发大小等于最大速率值，在上面设置为5的演示中。如果您不想允许突发，请将最大速率设置为1，将时间段设置为最小时间间隔滴水：
>>> bucket = AsyncLeakyBucket(1, 1.5) # no bursts, drip every 1.5 seconds
>>> async def task():
... async with bucket:
... print(f'Drip! {time.time() - ref:>5.2f}')
...
>>> ref = time.time()
>>> tasks = [task() for _ in range(5)]
>>> result = asyncio.run(asyncio.wait(tasks))
Drip! 0.00
Drip! 1.50
Drip! 3.01
Drip! 4.51
Drip! 6.02
答案 1 :(得分：0)
另一个解决方案 - 使用有界信号量 - 由同事，导师和朋友提供，如下：
import asyncio
class AsyncLeakyBucket(object):
def __init__(self, max_tasks: float, time_period: float = 60, loop: asyncio.events=None):
self._delay_time = time_period / max_tasks
self._sem = asyncio.BoundedSemaphore(max_tasks)
self._loop = loop or asyncio.get_event_loop()
self._loop.create_task(self._leak_sem())
async def _leak_sem(self):
"""
Background task that leaks semaphore releases based on the desired rate of tasks per time_period
"""
while True:
await asyncio.sleep(self._delay_time)
try:
self._sem.release()
except ValueError:
pass
async def __aenter__(self) -> None:
await self._sem.acquire()
async def __aexit__(self, exc_type, exc, tb) -> None:
pass
仍然可以使用与@ Martijn的答案相同的async with bucket代码