Sleep
import logging
import time
import random
import typing
import awaitable
@awaitable.awaitable
def do_process(name: str,
t: typing.Union[int, float]) -> None:
"""
Await some time before returning
:param name: task name
:param t: time to sleep in seconds
:return: None
"""
logging.info(f'From {name}: awaiting {t} seconds...')
starting_time = time.time()
time.sleep(t)
logging.info(f'From {name}: task complete '
f'in {time.time() - starting_time:.2f} seconds')
The do_process function is marked as awaitable and it will simply log a
descriptive text and sleep an amount of seconds as passed from the argument
t.
async def process(count: int) -> None:
"""
Run some processes asynchronously
Each process will await a random number of seconds
:param count: number of tasks to process
:return: None
"""
logging.info(f'Running {count} processes')
tasks = awaitable.AsyncioGather()
for i in range(count):
tasks.add(do_process(name=f'task_{tasks.count() + 1}',
t=random.randint(0, count)))
logging.info(f'Starting to process {len(tasks)} tasks')
await tasks.run()
The process function is an asynchronous function which will gather a number
of processes before processing them at once in asynchronous way.
As the result, all the sleep (see the do_process function) calls will be
executed more or less at the same time and the whole process will be completed
when the longest sleep will be completed.
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO,
format='%(levelname)-8s '
'%(filename)-15s '
'line: %(lineno)-5d '
'%(funcName)-20s '
'%(message)s')
starting_time = time.time()
awaitable.run_awaitable(func=process,
count=10)
ending_time = time.time()
logging.info(f'Operation completed in {ending_time - starting_time:.2f} '
'seconds')
The function call run_awaitable will execute the asynchronous function
process passing the number of parallel workers 10 to process.
At the end the elapsed seconds needed to complete the operation will be printed.
INFO 01_sleep.py line: 53 process Running 10 processes
INFO 01_sleep.py line: 58 process Starting to process 10 tasks
INFO 01_sleep.py line: 38 do_process From task_1: awaiting 10 seconds...
INFO 01_sleep.py line: 38 do_process From task_2: awaiting 0 seconds...
INFO 01_sleep.py line: 41 do_process From task_2: task complete in 0.00 seconds
INFO 01_sleep.py line: 38 do_process From task_3: awaiting 2 seconds...
INFO 01_sleep.py line: 38 do_process From task_4: awaiting 9 seconds...
INFO 01_sleep.py line: 38 do_process From task_5: awaiting 7 seconds...
INFO 01_sleep.py line: 38 do_process From task_6: awaiting 10 seconds...
INFO 01_sleep.py line: 38 do_process From task_7: awaiting 5 seconds...
INFO 01_sleep.py line: 38 do_process From task_8: awaiting 5 seconds...
INFO 01_sleep.py line: 38 do_process From task_9: awaiting 8 seconds...
INFO 01_sleep.py line: 38 do_process From task_10: awaiting 0 seconds...
INFO 01_sleep.py line: 41 do_process From task_10: task complete in 0.00 seconds
INFO 01_sleep.py line: 41 do_process From task_3: task complete in 2.00 seconds
INFO 01_sleep.py line: 41 do_process From task_8: task complete in 5.00 seconds
INFO 01_sleep.py line: 41 do_process From task_7: task complete in 5.00 seconds
INFO 01_sleep.py line: 41 do_process From task_5: task complete in 7.01 seconds
INFO 01_sleep.py line: 41 do_process From task_9: task complete in 8.01 seconds
INFO 01_sleep.py line: 41 do_process From task_4: task complete in 9.01 seconds
INFO 01_sleep.py line: 41 do_process From task_1: task complete in 10.01 seconds
INFO 01_sleep.py line: 41 do_process From task_6: task complete in 10.01 seconds
INFO 01_sleep.py line: 74 <module> Operation completed in 10.02 seconds
It will execute 10 jobs with a random sleep from 0 to 10 seconds and the whole operation will be completed in about 10.02 seconds. Without using async or awaitable the synchronous operation would have needed about 56 seconds.