Skip to main content

Simple async w/o async

Project description

MultiTasking: Non-blocking Python methods using decorators

Python version Travis-CI build status PyPi version PyPi status Star this repo Follow me on twitter

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

Example

# example.py
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))
    time.sleep(sleep)
    print("Goodbye %s (after for %ss)" % (count, sleep))

if __name__ == "__main__":
    for i in range(0, 10):
        hello(i+1)

The output would look something like this:

$ python example.py

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)

Settings

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
multitasking.set_max_threads(10)

…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.__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"

Installation

Install multitasking using pip:

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

multitasking-0.0.4.tar.gz (7.2 kB view details)

Uploaded Source

File details

Details for the file multitasking-0.0.4.tar.gz.

File metadata

File hashes

Hashes for multitasking-0.0.4.tar.gz
Algorithm Hash digest
SHA256 c520f28fc683ec4f11cbb0fff1d94cad3c75a7422107909ae7318a6be3ba6f07
MD5 8cab5de5cf9d28048f39b8bb5aeb3838
BLAKE2b-256 f16aaf9b61bf8ddf1105468636224309e44c5fabd74d53f0adf162a54e8a9ffa

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page