Throttling Async Functions in Python Asyncio

Another solution - using bounded semaphores - by a coworker, mentor, and friend, is the following:

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

Can still be used with the same async with bucket code as in @Martijn's answer

You can do this by implementing the leaky bucket algorithm:

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

Note that we leak capacity from the bucket opportunistically, there is no need to run a separate async task just to lower the level; instead, capacity are leaked out when testing for sufficient remaining capacity.

Note that tasks that wait for capacity are kept in an ordered dictionary, and when there might be capacity to spare again, the first still-waiting task is woken up early.

You can use this as a context manager; trying to acquire the bucket when it is full blocks until enough capacity has been freed again:

bucket = AsyncLeakyBucket(100)

# ...

async with bucket:
    # only reached once the bucket is no longer full

or you can call acquire() directly:

await bucket.acquire()  # blocks until there is space in the bucket

or you can simply test if there is space first:

if bucket.has_capacity():
    # reject a request due to rate limiting

Note that you can count some requests as 'heavier' or 'lighter' by increasing or decreasing the amount you 'drip' into the bucket:

await bucket.acquire(10)
if bucket.has_capacity(0.5):

Do be careful with this though; when mixing large and small drips, small drips tend to get run before large drips when at or close to the maximum rate, because there is a greater likelyhood that there is enough free capacity for a smaller drip before there is space for a larger one.

Demo:

>>> 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

The bucket is filled up quickly at the start in a burst, causing the rest of the tasks to be spread out more evenly; every 2 seconds enough capacity is freed for another task to be handled.

The maximum burst size is equal to the maximum rate value, in the above demo that was set to 5. If you do not want to permit bursts, set the maximum rate to 1, and the time period to the minimum time between drips:

>>> 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

I've gotten round to packaging this up as a Python project: https://github.com/mjpieters/aiolimiter