Concurrency

Concurrency#

Concurrent execution means that multiple tasks are in progress at the same time, but not necessarily running at the exact same time.

Asyncio#

Python offers tools for organizing asynchronous programming. The main components are the async and await keywords, along with the asyncio library.

The following cell demonstrates a classical asynchronous program. It uses the functions fun1 and fun2, which are essentially the same—each prints a number in a loop. Each record identifies which function produced it.

import asyncio
from random import uniform

async def fun1():
    for i in range(10):
        print(f"fun 1|iteration {i}")
        await asyncio.sleep(uniform(0, 0.1))

async def fun2():
    for i in range(10):
        print(f"fun 2|iteration {i}")
        await asyncio.sleep(uniform(0, 0.1))

print("=========begin cycle=========")
await asyncio.gather(fun1(), fun2())
print("=========end cycle=========")

=========begin cycle=========
fun 1|iteration 0
fun 2|iteration 0
fun 2|iteration 1
fun 1|iteration 1
fun 1|iteration 2
fun 1|iteration 3
fun 2|iteration 2
fun 2|iteration 3
fun 2|iteration 4
fun 1|iteration 4
fun 2|iteration 5
fun 2|iteration 6
fun 2|iteration 7
fun 2|iteration 8
fun 1|iteration 5
fun 1|iteration 6
fun 2|iteration 9
fun 1|iteration 7
fun 1|iteration 8
fun 1|iteration 9
=========end cycle=========

Multiprocessing#

Runs a sompletely spearate process in the system and executes the code you need there. Find our more in:

The following cell defines a funciton that prints the id of the current process. And run it in the main process and in multiprocessing.

import os
import multiprocessing

def print_pid():
    print(os.getpid())

print_pid()

p = multiprocessing.Process(target=print_pid)
p.start()
p.join()

22512
23316

As the result there are two different numbers, which showe that the second execution of the function was recognized by the system as completely different program.

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