I've read many examples, blog posts, questions/answers about asyncio / async / await in Python 3.5+, many were complex, the simplest I found was probably this one.
Still it uses ensure_future, and for learning purposes about asynchronous programming in Python, I would like to see an even more minimal example, and what are the minimal tools necessary to do a basic async / await example.
Question: is it possible to give a simple example showing how async / await works, by using only these two keywords + code to run the async loop + other Python code but no other asyncio functions?
Example: something like this:
import asyncio
async def async_foo():
print("async_foo started")
await asyncio.sleep(5)
print("async_foo done")
async def main():
asyncio.ensure_future(async_foo()) # fire and forget async_foo()
print('Do some actions 1')
await asyncio.sleep(5)
print('Do some actions 2')
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
but without ensure_future, and still demonstrates how await / async works.
To answer your questions, I will provide three different solutions to the same problem.
import time
def sleep():
print(f'Time: {time.time() - start:.2f}')
time.sleep(1)
def sum(name, numbers):
total = 0
for number in numbers:
print(f'Task {name}: Computing {total}+{number}')
sleep()
total += number
print(f'Task {name}: Sum = {total}\n')
start = time.time()
tasks = [
sum("A", [1, 2]),
sum("B", [1, 2, 3]),
]
end = time.time()
print(f'Time: {end-start:.2f} sec')
Output:
Task A: Computing 0+1
Time: 0.00
Task A: Computing 1+2
Time: 1.00
Task A: Sum = 3
Task B: Computing 0+1
Time: 2.00
Task B: Computing 1+2
Time: 3.00
Task B: Computing 3+3
Time: 4.00
Task B: Sum = 6
Time: 5.00 sec
import asyncio
import time
async def sleep():
print(f'Time: {time.time() - start:.2f}')
time.sleep(1)
async def sum(name, numbers):
total = 0
for number in numbers:
print(f'Task {name}: Computing {total}+{number}')
await sleep()
total += number
print(f'Task {name}: Sum = {total}\n')
start = time.time()
loop = asyncio.new_event_loop()
tasks = [
loop.create_task(sum("A", [1, 2])),
loop.create_task(sum("B", [1, 2, 3])),
]
loop.run_until_complete(asyncio.wait(tasks))
loop.close()
end = time.time()
print(f'Time: {end-start:.2f} sec')
Output:
Task A: Computing 0+1
Time: 0.00
Task A: Computing 1+2
Time: 1.00
Task A: Sum = 3
Task B: Computing 0+1
Time: 2.00
Task B: Computing 1+2
Time: 3.00
Task B: Computing 3+3
Time: 4.00
Task B: Sum = 6
Time: 5.00 sec
The same as case 2, except the sleep function:
async def sleep():
print(f'Time: {time.time() - start:.2f}')
await asyncio.sleep(1)
Output:
Task A: Computing 0+1
Time: 0.00
Task B: Computing 0+1
Time: 0.00
Task A: Computing 1+2
Time: 1.01
Task B: Computing 1+2
Time: 1.01
Task A: Sum = 3
Task B: Computing 3+3
Time: 2.01
Task B: Sum = 6
Time: 3.02 sec
Case 1 and case 2 give the same 5 seconds, whereas case 3 just 3 seconds. So the async/await done right is faster.
The reason for the difference is within the implementation of the sleep function.
# Case 1
def sleep():
...
time.sleep(1)
# Case 2
async def sleep():
...
time.sleep(1)
# Case 3
async def sleep():
...
await asyncio.sleep(1)
In case 1 and case 2, they are the "same": they "sleep" without allowing others to use the resources. Whereas in case 3, it allows access to the resources when it is asleep.
In case 2, we added async to the normal function. However the event loop will run it without interruption.
Why? Because we didn't say where the loop is allowed to interrupt your function to run another task.
In case 3, we told the event loop exactly where to interrupt the function to run another task. Where exactly? Right here!
await asyncio.sleep(1)
For more on this, read here.
Consider reading