我有一个
listawaitablesasyncio.AbstractEventLoop我想避免等待将
futureSemaphoresThreadPools这是一个使用标准库的非常简洁（非）工作示例，演示了该问题。本应以 100/秒的速度节流，但实际上以 116.651/秒的速度节流。 在 asyncio 中限制异步请求调度的最佳方法是什么？
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一个简单的
TrottleTestApp
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))
漏桶算法来做到这一点：
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
wait_time = 1 / self._rate_per_sec * (amount - self.max_rate + self._level)
try:
await asyncio.wait_for(asyncio.shield(fut), wait_time)
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，我们会机会性地从存储桶中泄漏容量，无需仅仅为了降低级别而运行单独的异步任务；相反，在测试是否有足够的剩余容量时，容量会泄漏。请注意，等待容量的任务保存在有序字典中，当可能再次有空闲容量时，第一个仍在等待的任务会提前被唤醒。
您可以将其用作上下文管理器；尝试在桶满时获取桶，直到再次释放足够的容量：
bucket = AsyncLeakyBucket(100)
# ...
async with bucket:
# only reached once the bucket is no longer full
或者您可以直接拨打acquire()
:
await bucket.acquire() # blocks until there is space in the bucket
或者您可以先简单测试是否有空间：
if bucket.has_capacity():
# reject a request due to rate limiting
请注意，您可以通过增加或减少“滴入”桶中的量来将某些请求计为“较重”或“较轻”：
await bucket.acquire(10)
if bucket.has_capacity(0.5):
但请务必小心；当混合大滴和小滴时，当达到或接近最大速率时，小滴往往会在大滴之前运行，因为在有空间容纳较大滴之前，更有可能有足够的自由容量容纳较小滴。 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
开始时桶会突然被快速填满，从而使其余任务分散得更均匀；每 2 秒就会释放足够的容量来处理另一项任务。最大突发大小等于最大速率值，在上面的演示中设置为 5。如果您不想允许突发，请将最大速率设置为 1，并将时间段设置为点滴之间的最短时间：
>>> 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
我已经抽出时间将其打包为一个 Python 项目：
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
仍然可以与@Martijn的答案中相同的
async with bucket
代码一起使用
import asyncio
import datetime as dt
import random
async def send_request(num):
print(f"Request {num:>2} at {dt.datetime.now()}")
await asyncio.sleep(random.choice([0.1, 0.2]))
print(f"Response {num:>2} at {dt.datetime.now()}")
def requests_per_second(request_datetimes):
rps = 0
if len(request_datetimes) > 0:
rps = 1 / (dt.datetime.now() - request_datetimes[-1]).total_seconds()
return rps
async def rate_limited_gather(*args, rate_limit=50, max_connections=10):
"""Manage max requests per second and max open connections for an API"""
awaitables = []
request_datetimes = []
loop = asyncio.get_event_loop()
connections = 0
for arg in args:
while (
requests_per_second(request_datetimes) > rate_limit or connections >= max_connections
):
await asyncio.sleep(1 / rate_limit)
connections = sum([not a.done() for a in awaitables])
print(f"Requests per second: {requests_per_second(request_datetimes)}")
request_datetimes.append(dt.datetime.now())
awaitables.append(loop.create_task(arg))
connections = sum([not a.done() for a in awaitables])
await asyncio.gather(*awaitables, return_exceptions=True)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(rate_limited_gather(*[send_request(x) for x in range(10)]))
输出示例：
Requests per second: 0
Request 0 at 2023-03-11 10:34:49.348671
Requests per second: 49.696849219759464
Request 1 at 2023-03-11 10:34:49.368800
Requests per second: 49.69931911932807
Request 2 at 2023-03-11 10:34:49.388930
Requests per second: 49.69931911932807
Request 3 at 2023-03-11 10:34:49.409057
Requests per second: 49.72403162448411
Request 4 at 2023-03-11 10:34:49.429170
Response 0 at 2023-03-11 10:34:49.449260
Requests per second: 49.691910157026435
Request 5 at 2023-03-11 10:34:49.449298
Response 1 at 2023-03-11 10:34:49.469389
Requests per second: 49.68450340338848
Request 6 at 2023-03-11 10:34:49.469436
Response 2 at 2023-03-11 10:34:49.489529
Requests per second: 49.67956679417755
Request 7 at 2023-03-11 10:34:49.489566
Requests per second: 49.73392350922564
Request 8 at 2023-03-11 10:34:49.509682
Requests per second: 49.7116723006562
Request 9 at 2023-03-11 10:34:49.529858
Response 6 at 2023-03-11 10:34:49.569973
Response 7 at 2023-03-11 10:34:49.590072
Response 3 at 2023-03-11 10:34:49.609170
Response 4 at 2023-03-11 10:34:49.629267
Response 5 at 2023-03-11 10:34:49.650361
Response 8 at 2023-03-11 10:34:49.710456
Response 9 at 2023-03-11 10:34:49.730560