从零开始的-Python-AsyncIO-生活
From Zero to Python AsyncIO Life
I've been using AsyncIO for asynchronous programming in Python, but I've never thought about why. Let's take this opportunity to understand AsyncIO better.
Iterable
First of all, we need to understand what an Iterable is, which is basically an object that can be used in a for loop. Common examples of Iterable include list, str, tuple, and dict.
In Python, how does it determine if an object is an Iterable? We can use the dir() function to check its attribute list.
By running the following code, we can see their common interface:
from typing import Iterable
iterable = [
"", # str
[], # list
{}, # dict
(), # tuple
set() # set
]
def show_diff(*objects: Iterable):
"""Print the attribute differences between Iterable and object"""
assert objects
attrs = set(dir(objects[0]))
for obj in objects[1:]:
attrs &= set(dir(obj)) # Get the intersection of Iterables
attrs -= set(dir(object)) # Get the difference between Iterable and object
print(attrs)
show_diff(*iterable)
# {'__iter__', '__contains__', '__len__'}
As we can see, the key attribute is __iter__. In fact, for any object that has the __iter__ method specified, it will be considered an Iterable. Attributes like __len__ and __contains__ are common to container type Iterables.
If we add a non-container type Iterable, the result becomes obvious:
iterable = [
"", # str
[], # list
{}, # dict
(), # tuple
set(), # set
open(__file__) # IO
]
show_diff(*iterable)
# {'__iter__'}
Iterator
In Python, methods like __iter__ in Iterables have corresponding calling methods, which is iter().
Let's see the results when we use iter() on the container type Iterables listed above:
for i in iterable:
print(iter(i))
"""
<str_iterator object at 0x7f7bd06fafe0>
<list_iterator object at 0x7f7bd06fafe0>
<dict_keyiterator object at 0x7f7bd08c4b80>
<tuple_iterator object at 0x7f7bd06fafe0>
<set_iterator object at 0x7f7bd0720440>
"""
We can see that they all return an Iterator object. As demonstrated in the Iterable section, let's once again find the attribute differences among them:
# {'__next__', '__iter__'}
So, compared to Iterable, there is an additional __next__ method in Iterator, which is used to return data in the next iteration.
In the end, after all values have been iterated, it will raise a StopIteration error to indicate the end of the iteration.
We can build a custom Iterator with the following code:
class MyIterator:
def __init__(self, Iter):
self.index = 0
self.data = Iter
def __next__(self):
while self.index < len(self.data):
data = self.data[self.index]
self.index += 1
return data
raise StopIteration
def __iter__(self):
"""Iterators must be iterable"""
return self
things = ["I", "AM", "ITERABLE", "GOD"]
for i in MyIterator(things):
print(i)
Stay tuned for the next parts!```python task...") t1 = time.time() await Awaitable(sleep, 2) assert time.time() - t1 > 2, "You didn't block, silly pig" print(" I'm finished") return 123
class Awaitable: def init(self, *obj): self.obj = obj
def __await__(self):
yield self.obj
class Task: def init(self, _task): self.coro = _task
def run(self):
while True:
try:
x = self.coro.send(None)
except StopIteration as _e:
result = _e.value
break
else:
func, arg = x
func(arg)
return result
Task(task()).run()
Returning to our `small_step`, we are using a hard-coded blocking mechanism `sleep(2)`, but in reality, there are more types of blocking than just this one. We should aim for a more general mechanism for blocking.
In `Awaitable`, we are directly yielding `self`.
```python
class Awaitable:
def __init__(self, *obj):
self.obj = obj
def __await__(self):
yield self
class Task:
def __init__(self, _task):
self.coro = _task
def run(self):
while True:
try:
x = self.coro.send(None)
except StopIteration as _e:
result = _e.value
break
else:
func, arg = x.obj
func(arg)
return result
Now, notice one thing: our Task.run() function is still blocking, and we haven't completely yielded control of our program's execution. Let's continue to modify the Task code.
class Task:
def __init__(self, _task):
self.coro = _task
self._done = False
self._result = None
def run(self):
if not self._done:
try:
x = self.coro.send(None)
except StopIteration as _e:
self._result = _e.value
self._done = True
else:
... # This should not happen, an exception should be raised
t = Task(task())
t.run()
for i in range(10): # During sleep(2), we can do other things.
print("doing something", i)
sleep(0.2)
t.run()
We are manually scheduling multiple tasks here. In reality, we should schedule tasks automatically through an event loop (Event Loop).
Event Loop
Firstly, tasks must have a queue. We can use a deque double-ended queue to store tasks.
class Event:
def __init__(self):
self._queue = collections.deque()
def call_soon(self, callback, *args, **kwargs):
self._queue.append((callback, args, kwargs))
Next, we add scheduled tasks. Due to the special nature of scheduled tasks, we use a heap to store them. Here, we leverage heapq for operations.
class Event:
def __init__(self):
self._queue = collections.deque()
self._scheduled = []
def call_soon(self, callback, *args, **kwargs):
self._queue.append((callback, args, kwargs))
def call_later(self, delay, callback, *args, **kwargs):
_t = time.time() + delay
heapq.heappush(self._scheduled, (_t, callback, args, kwargs))
Let's write the event scheduling function.
class Event:
def __init__(self):
self._queue = collections.deque()
self._scheduled = []
self._stopping = False
def call_soon(self, callback, *args, **kwargs):
self._queue.append((callback, args, kwargs))
def call_later(self, delay, callback, *args, **kwargs):
_t = time.time() + delay
heapq.heappush(self._scheduled, (_t, callback, args, kwargs))
def stop(self):
self._stopping = True
def run_forever(self):
while True:
self.run_once() # At least one execution is necessary, so put the condition check below
if self._stopping:
break
def run_once(self):
now = time.time()
if self._scheduled and now > self._scheduled[0][0]:
_, cb, args, kwargs = heapq.heappop(self._scheduled)
self._queue.append((cb, args, kwargs))
task_num = len(self._queue) # Prevent adding more tasks to the queue during execution
for _ in range(task_num):
cb, args, kwargs = self._queue.popleft()
cb(*args, **kwargs)
t = Task(task())
loop = Event()
loop.call_soon(t.run)
loop.call_later(2, t.run)
loop.call_later(2.1, loop.stop)
loop.run_forever()
Now, let's modify small_step
async def small_step():
t1 = time.time()
time_ = random.randint(1, 3)
await Awaitable(time_)
assert time.time() - t1 > time_, f"{time_} You didn't block, silly pig {time.time() - t1}"
return time_
As this time has been passed to Task, we need to handle it in Task, which means adding a loop.call_later() while blocking.
class Task:
def __init__(self, _task):
self.coro = _task
self._done = False
self._result = None
def run(self):
if not self._done:
try:
x = self.coro.send(None)
except StopIteration as _e:
self._result = _e.value
self._done = True
else:
loop.call_later(*x.obj, self.run)
else:
... # This should not happen, an exception should be raised
Now, we can remove the manually specified call_later
t = Task(task())
loop = Event()
loop.call_soon(t.run)
loop.call_later(1.1, loop.stop) # random() will only yield values between 0 and 1
loop.run_forever()
Finally, let's try implementing multiple tasks and actually demonstrate the async effect through some parameters.
import collections
import heapq
import itertools
import random
import time
from time import sleep
count = itertools.count(0)
total = 0
async def task():
""" Create a new task """
print("TASK BEGIN...")
print(" MainStep...")
main_result = await main_step()
print(f" MainStep Finished with result {main_result}")
print("TASK END")
async def main_step():
print(" SmallStep(s)...")
small_result = await small_step()
print(f" SmallStep(s) Finished with result {small_result}")
return small_result * 100
async def small_step():
t1 = time.time()
time_ = random.random()
await Awaitable(time_)
assert time.time() - t1 > time_, f"{time_} You didn't block, silly pig {time.time() - t1}"
return time_
class Awaitable:
def __init__(self, *obj):
self.obj = obj
def __await__(self):
yield self
class Task:
def __init__(self, _task):
self.coro = _task
self._done = False
self._result = None
self._id = f"Task-{next(count)}"
def run(self):
print(f"--------- {self._id} --------")
if not self._done:
try:
x = self.coro.send(None)
except StopIteration as _e:
self._result = _e.value
self._done = True
else:
loop.call_later(*x.obj, self.run)
else:
... # This should not happen, an exception should be raised
print("-------------------------")
class Event:
def __init__(self):
self._queue = collections.deque()
self._scheduled = []
self._stopping = False
def call_soon(self, callback, *args, **kwargs):
self._queue.append((callback, args, kwargs))
def call_later(self, delay, callback, *args, **kwargs):
_t = time.time() + delay
global total
total += delay
heapq.heappush(self._scheduled, (_t, callback, args, kwargs))
def stop(self):
self._stopping = True
def run_forever(self):
while True:
self.run_once() # At least one execution is necessary, so put the condition check below
if self._stopping:
break
def run_once(self):
now = time.time()
if self._scheduled and now > self._scheduled[0][0]:
_, cb, args, kwargs = heapq.heappop(self._scheduled)
self._queue.append((cb, args, kwargs))
task_num = len(self._queue) # Prevent adding more tasks to the queue during execution
for _ in range(task_num):
cb, args, kwargs = self._queue.popleft()
cb(*args, **kwargs)
t = Task(task())
loop = Event()
loop.call_soon(t.run)
loop.call_later(1.1, loop.stop)
loop.run_forever()
Here, we can see that while we would normally need around 509.3s to run all the tasks, thanks to the concurrent execution achieved through task scheduling, we finished running all 1000 tasks within just 1 second.
Future
Finally, our code actively uses sleep to simulate blocking. How should we do this in a real-world scenario?
Typically, we want to perform an operation and obtain a value, as shown below:
async def small_step():
result = await Awaitable(...)
return result
In this situation, we should introduce Future. What is a Future? It's a result that will happen in the future, as opposed to Awaitable, where we can't pass the result at the time of creation.
class Future:
def __init__(self):
self._result = None
self._done = False
def set_result(self, result):
if self._done:
raise RuntimeError() # Disallowed operation
self._result = result
self._done = True
@property
def result(self):
if self._done:
return self._result
raise RuntimeError()
def __await__(self):
yield self
Therefore, we need something to designate when to execute set_result.
async def small_step():
fut = Future()
# do something that will call set_result
...
result = await fut
return result
In this case, Task should receive this future, but the future doesn't have any information, only a flag telling us the task is not yet completed.
How does our Task know when to resume execution?
We can add a callback record in Future to signify this.
class Future:
def __init__(self):
self._result = None
self._done = False
self._callbacks = []
def add_done_callback(self, cb):
self._callbacks.append(cb)
def set_result(self, result):
if self._done:
raise RuntimeError() # Disallowed operation
self._result = result
self._done = True
for cb in self._callbacks:
cb() # May have other parameters
@property
def result(self):
if self._done:
return self._result
raise RuntimeError()
def __await__(self):
yield self
return self.result # result = await fut will retrieve this value
class Task:
def __init__(self, _task):
self.coro = _task
self._done = False
self._result = None
self._id = f"Task-{next(count)}"
def run(self):
print(f"--------- {self._id} --------")
if not self._done:
try:
x = self.coro.send(None)
except StopIteration as _e:
self._result = _e.value
self._done = True
else:
x.add_done_callback(self.run)
else:
... # This should not happen, an exception should be raised
print("-------------------------")
Now, we can observe Task and Future
We can see that Task can simply inherit from Future.
class Task(Future):
def __init__(self, _task):
super().__init__()
self.coro = _task
self._id = f"Task-{next(count)}"
def run(self):
print(f"--------- {self._id} --------")
if not self._done:
try:
x = self.coro.send(None)
except StopIteration as _e:
self.set_result(_e.value)
else:
x.add_done_callback(self.run)
else:
... # This should not happen, an exception should be raised
print("-------------------------")
At this point, AsyncIO is basically implemented. However, compared to Python's own AsyncIO, our code could be considered very basic. It lacks in performance (since it's not written in C) and has issues in exception handling and other areas. Finally, here is the optimized code. (Didn't mention the hook-up between Task and loop, but it's written)
import collections
import heapq
import itertools
import random
import threading
import time
from time import sleep
count = itertools.count(0)
blocked = 0
async def task():
""" Create a new task """
print("TASK BEGIN...")
print(" MainStep...")
main_result = await main_step()
print(f" MainStep Finished with result {main_result}")
print("TASK END")
async def main_step():
print(" SmallStep(s)...")
small_result = await small_step()
print(f" SmallStep(s) Finished with result {small_result}")
return small_result * 100
async def small_step():
fut = Future()
fake_io(fut)
result = await fut
return result
class Future:
def __init__(self):
self._result = None
self._done = False
self._callbacks = []
def add_done_callback(self, cb):
self._callbacks.append(cb)
def set_result(self, result):
if self._done:
raise RuntimeError() # Disallowed operation
self._result = result
self._done = True
for cb in self._callbacks:
cb() # May have other parameters
@property
def result(self):
if self._done:
return self._result
raise RuntimeError()
def __await__(self):
yield self
return self.result
class Task(Future):
def __init__(self, _task):
super().__init__()
self._loop = loop
self.coro = _task
self._id = f"Task-{next(count)}"
self._loop.call_soon(self.run)
self._start_time = time.time()
def run(self):
print(f"--------- {self._id} --------")
if not self._done:
try:
x = self.coro.send(None)
except StopIteration as _e:
self.set_result(_e.value)
global blocked
blocked += time.time() - self._start_time
else:
x.add_done_callback(self.run)
else:
... # This should not happen, an exception should be raised
print("-------------------------")
class Event:
def __init__(self):
self._queue = collections.deque()
self._scheduled = []
self._stopping = False
def call_soon(self, callback, *args, **kwargs):
self._queue.append((callback, args, kwargs))
def call_later(self, delay, callback, *args, **kwargs):
_t = time.time() + delay
heapq.heappush(self._scheduled, (_t, callback, args, kwargs))
def stop(self):
self._stopping = True
def run_forever(self):
while True:
self.run_once() # At least one execution is necessary, so put the condition check below
if self._stopping:
break
def run_once(self):
now = time.time()
if self._scheduled and now > self._scheduled[0][0] + (10 ** -5):
_, cb, args, kwargs = heapq.heappop(self._scheduled)
self._queue.append((cb, args, kwargs))
task_num = len(self._queue) # Prevent adding more tasks to the queue during execution
for _ in range(task_num):
cb, args, kwargs = self._queue.popleft()
cb(*args, **kwargs)
def fake_io(fut):
def read():
sleep(t_ := random.random()) # IO blocking
fut.set_result(t_)
threading.Thread(target=read).start()
def run_until_all_task(tasks):
if tasks := [_task for _task in tasks if not _task._done]:
loop.call_soon(run_until_all_task, tasks)
else:
loop.call_soon(loop.stop)
loop = Event()
all_tasks = [Task(task()) for _ in range(1000)]
loop.call_soon(run_until_all_task, all_tasks)
t1 = time.time()
loop.run_forever()
print(time.time() - t1, blocked)
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