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Non-blocking Python methods using decorators

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MultiTasking: Non-blocking Python methods using decorators

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MultiTasking is a tiny Python library lets you convert your Python methods into asynchronous, non-blocking methods simply by using a decorator.


import multitasking
import time
import random
import signal

# kill all tasks on ctrl-c
signal.signal(signal.SIGINT, multitasking.killall)

# or, wait for task to finish on ctrl-c:
# signal.signal(signal.SIGINT, multitasking.wait_for_tasks)

@multitasking.task # <== this is all it takes :-)
def hello(count):
    sleep = random.randint(1,10)/2
    print("Hello %s (sleeping for %ss)" % (count, sleep))
    print("Goodbye %s (after for %ss)" % (count, sleep))

if __name__ == "__main__":
    for i in range(0, 10):

The output would look something like this:

$ python

Hello 1 (sleeping for 0.5s)
Hello 2 (sleeping for 1.0s)
Hello 3 (sleeping for 5.0s)
Hello 4 (sleeping for 0.5s)
Hello 5 (sleeping for 2.5s)
Hello 6 (sleeping for 3.0s)
Hello 7 (sleeping for 0.5s)
Hello 8 (sleeping for 4.0s)
Hello 9 (sleeping for 3.0s)
Hello 10 (sleeping for 1.0s)
Goodbye 1 (after for 0.5s)
Goodbye 4 (after for 0.5s)
Goodbye 7 (after for 0.5s)
Goodbye 2 (after for 1.0s)
Goodbye 10 (after for 1.0s)
Goodbye 5 (after for 2.5s)
Goodbye 6 (after for 3.0s)
Goodbye 9 (after for 3.0s)
Goodbye 8 (after for 4.0s)
Goodbye 3 (after for 5.0s)


The default maximum threads is equal to the # of CPU Cores. This is just a rule of thumb! The Thread module isn’t actually using more than one core at a time.

You can change the default maximum number of threads using:

import multitasking

…or, if you want to set the maximum number of threads based on the number of CPU Cores, you can:

import multitasking
multitasking.set_max_threads(multitasking.config["CPU_CORES"] * 5)

For applications that doesn’t require access to shared resources, you can set MultiTasking to use multiprocessing.Process() instead of the threading.Thread(), thus avoiding some of the GIL constraints.

import multitasking
multitasking.set_engine("process") # "process" or "thread"


Install multitasking using pip:

$ pip install multitasking --upgrade --no-cache-dir

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