Throttling Async Functions in Python Asyncio

Question

I have a list of awaitables that I want to pass to the asyncio.AbstractEventLoop but I need to throttle the requests to a third party API.

I would like to avoid something that waits to pass the future to the loop because in the meantime I block my loop waiting. What options do I have? Semaphores and ThreadPools will limit how many are running concurrently, but that's not my problem. I need to throttle my requests to 100/sec, but it doesn't matter how long it takes to complete the request.

This is a very concise (non)working example using the standard library, that demonstrates the problem. This is supposed to throttle at 100/sec but throttles at 116.651/sec. What's the best way to throttle the scheduling of an asynchronous request in asyncio?

Working code:

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)

Edit: I've added a simple example of TrottleTestApp here using semaphores, but still can't throttle the execution:

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))

Answer 1

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