forklib 0.2.2

Basic example

import forklib
import logging
import os
from time import sleep


logging.basicConfig(level=logging.DEBUG)

def run():
    print(
        "Proceess #{id} has PID: {pid}".format(
            id=forklib.get_id(),
            pid=os.getpid()
        )
    )
    sleep(1)


def main():
    print("Master proccess has PID: {0}".format(os.getpid()))
    forklib.fork(4, run)



if __name__ == '__main__':
    main()

This code makes 4 forks. When you try to run it you will see something like this

Master proccess has PID: 39485
DEBUG:forklib.forking:Starting 4 processes
Proceess #1 has PID: 39487
Proceess #0 has PID: 39486
Proceess #2 has PID: 39488
Proceess #3 has PID: 39489
DEBUG:forklib.forking:Child with PID: 39487 Number: 1 exited normally
DEBUG:forklib.forking:Child with PID: 39489 Number: 3 exited normally
DEBUG:forklib.forking:Child with PID: 39488 Number: 2 exited normally
DEBUG:forklib.forking:Child with PID: 39486 Number: 0 exited normally

Forkme will be control forks. When subprocess will be killed or will exit with non-zero code it will be restarted immediately. e.g.:

Master proccess has PID: 7579
INFO:forklib:Starting 4 processes
Proceess #0 has PID: 7580
Proceess #1 has PID: 7581
Proceess #2 has PID: 7582
Proceess #3 has PID: 7583
WARNING:forklib:Child with PID: 7580 Number: 0 killed by signal 9, restarting
Proceess #0 has PID: 7584

Parallel iteration

You can load the large array of elements on the memory and process it in multiple processes. After forking the memory will not be copied, instead this the copy-on-write mechanism will be used.

from time import sleep

from forklib import fork_map, fork
import logging


logging.basicConfig(level=logging.INFO)


def map_func(item):
    return item + 1


def main():
    for item in fork_map(map_func, range(20000), workers=10):
        print(item)

    fork(2, lambda: sleep(1), auto_restart=True)


if __name__ == '__main__':
    main()