How to run multiple threads together in simpler code in Python?-CodePudding

I'm trying to simplify the code of threads below:

import threading

def test1():
    print("test1")

def test2():
    print('test2')

thread1 = threading.Thread(target=test1)
thread2 = threading.Thread(target=test2)
thread1.start()
thread2.start()
thread1.join()
thread2.join()

So, I want to simplify this part of code below to:

# ...

thread1 = threading.Thread(target=test1)
thread2 = threading.Thread(target=test2)
thread1.start()
thread2.start()
thread1.join()
thread2.join()

Something like one line of code below:

# ...

threading.Threads(test1, test2).start().join()

Are there any ways to do this? and it's ok if it's not one line of code as long as it's simpler.

CodePudding user response：

just write it yourself ...

class Threads:
    def __init__(self,*functions):
        self._functions = functions
        self._threads = []

    def start(self):
        for func in self._functions:
            thread = threading.Thread(target=func)
            self._threads.append(thread)
            thread.start()
        return self

    def join(self):
        for thread in self._threads:
            thread.join()

usage:

Threads(test1, test2).start().join()

Edit: using threadpool is more pythonic

from operator import methodcaller
from multiprocessing.pool import ThreadPool

caller = methodcaller("__call__")
with ThreadPool() as pool:
     results = pool.map(caller, [test1, test2])

CodePudding user response：

import threading

threads_list = []
for i in range(thread_count)
    thread = threading.Thread(target=target_func, args=(args,))
    thread.start()
    threads_list.append(thread)

CodePudding user response：

You could refactor the functions so that they are the same function but with different inputs, e.g.

def test(s):
    print(s)

Then, you can use higher level ThreadPool api. This calls the test function with each of the elements in the list ['test1', 'test2'] via different threads. So you don't need to manage details such as starting or joining the threads.

from multiprocessing.pool import ThreadPool

with ThreadPool as pool:
     results = pool.map(test, ['test1', 'test2'])