Thread is the smallest unit that the operating system can schedule operations. It is included in the process and is the actual operating unit of the process. A thread refers to a single sequential control flow in a process. Multiple threads can be concurrent in a process. Each thread performs different tasks in parallel.

Using the threading module

Method 1:

import threading
import time

def foo(n):
    print('foo %s'%n)
    time.sleep(1)
    print('end foo')


def bar(n):
    print('bar %s'%n)
    time.sleep(2)
    print('end bar')

t1 = threading.Thread(target=foo, args=(1,))
t2 = threading.Thread(target=bar, args=(2,))
t1.start()
t2.start()

print('........in the main..........')

//Operation result:
foo 1
bar 2
........in the main..........
end foo
end bar

Method two:

import time, threading

class MyThread(threading.Thread):

    def __init__(self, num):
        threading.Thread.__init__(self)
        self.num = num

    def run(self):             #Define the functions the thread will run
        print("running on number:%s" % self.num)
        time.sleep(3)

if __name__ == '__main__':
    t1 = MyThread(1)
    t2 = MyThread(2)
    t1.start()
    t2.start()

//Operation result:
running on number:1
running on number:2

The join method causes the main thread to wait for the child thread to complete before continuing

import threading
import time

begin = time.time()

def foo(n):
    print('foo %s'%n)
    time.sleep(1)
    print('end foo')


def bar(n):
    print('bar %s'%n)
    time.sleep(2)
    print('end bar')

t1 = threading.Thread(target=foo, args=(1,))
t2 = threading.Thread(target=bar, args=(2,))
t1.start()
t2.start()


t1.join()
t2.join()
print('........in the main..........')

//Operation result:
foo 1
bar 2
end foo
end bar
........in the main..........

Serial versus multithreading in compute intensive tasks

import threading, time

begin = time.time()

def add(n):
    sum = 0
    for i in range(n):
        sum += i
    print(sum)

add(100000000)
add(200000000)
end = time.time()
print(end-begin)

//Operation result:
4999999950000000
19999999900000000
17.66856598854065


import threading, time

begin = time.time()

def add(n):
    sum = 0
    for i in range(n):
        sum += i
    print(sum)

t1 = threading.Thread(target=add, args=(100000000,))
t1.start()
t2 = threading.Thread(target=add, args=(200000000,))
t2.start()
t1.join()
t2.join()
end = time.time()
print(end-begin)

//Operation result:
4999999950000000
19999999900000000
21.088160276412964

# The result is that serial runs are faster than multithreading

There is GIL (Global Interpreter Lock) in Cpython, so at the same time, only one thread can enter the schedule. If the task is IO intensive, multithreading can be used; if the task is compute intensive, the optimal method is to change to C.

setDaemon()

Call this method as long as the main thread finishes, no matter whether the sub thread finishes or not, exit together with the main thread.

threading.currentThread()

Returns the current thread variable.

threading.active_count()

Returns the number of running threads.

import threading, time
from time import ctime,sleep

def music(func):
    print(threading.current_thread())
    for i in range(2):
        print("Begin listening to %s. %s" %(func, ctime()))
        sleep(2)
        print("end listening %s" %ctime())


def movie(func):
    print(threading.current_thread())
    for i in range(2):
        print("Begin watching at the %s %s" %{func, ctime()})
        sleep(4)
        print("end watching %s" %ctime())


threads = []
t1 = threading.Thread(target=music, args=('klvchen',))
threads.append(t1)
t2 = threading.Thread(target=movie, args=('lili',))
threads.append(t2)


if __name__ == '__main__':
    for t in threads:
        t.setDaemon(True)
        t.start()

    print(threading.current_thread())
    print(threading.active_count())
    print("all over %s" %ctime())

//Operation result:
<Thread(Thread-1, started daemon 5856)>
Begin listening to klvchen. Wed Jul 11 23:43:51 2018
<Thread(Thread-2, started daemon 9124)>
<_MainThread(MainThread, started 9444)>
3
all over Wed Jul 11 23:43:51 2018