全局变量分别在多进程和多线程中的区别

全局变量在Python的多进程中无法共享,每个进程都有自己的副本,而多线程中全局变量是共享的。通过代码示例展示了在多进程和多线程环境下修改全局变量的不同结果,多进程情况下每个进程修改的变量互不影响,多线程则会共同影响全局变量的值。

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全局变量在多进程中是不能共享的,而在多线程当中是可以共享的。用代码来验证这个结果,思路就是设定一个全局变量,在多进程当中去修改该全局变量的值,其结果是各自的进程修改各自的全局变量。在多线程当中修改全局变量,如果两个线程同时修改全局变量+1操作,那么结果是全局变量变为加2。话不多说,来上代码。
先是多进程:
from threading import Thread
from multiprocessing import Process
g_num = 100
def work1():
global g_num
for i in range(3):
g_num += 1
print("-----in work1,g_num is %d"%(g_num))

def work2():
global g_num
for i in range(3):
g_num += 1
print("-----in work2,g_num is %d" % (g_num))
if name == ‘main’:
p1 = Process(target=work1) # 进程1
p1.start()
p2 = Process(target=work2) # 进程2
p2.start()
# 多进程之间内存独立,不能共享全局变量。如果通信的话,使用Queue
# 运行结果:
‘’’
----- in work2, g_num is 101
----- in work2, g_num is 102
----- in work2, g_num is 103
----- in work1, g_num is 101
----- in work1, g_num is 102
----- in work1, g_num is 103
‘’’

再来看多线程的代码:
from threading import Thread
from multiprocessing import Process
g_num = 100
def work1():
global g_num
for i in range(3):
g_num += 1
print("-----in work1,g_num is %d"%(g_num))

def work2():
global g_num
for i in range(3):
g_num += 1
print("-----in work2,g_num is %d" % (g_num))
if name == ‘main’:
t1 = Thread(target=work1)
t1.start()
t2 = Thread(target=work2)
t2.start()
# 由于多线程可以共享全局变量,所以g_num由原来的100变成了106
# 运行结果:
‘’’
-----in work1,g_num is 101
-----in work1,g_num is 102
-----in work1,g_num is 103
-----in work2,g_num is 104
-----in work2,g_num is 105
-----in work2,g_num is 106

‘’’

结果很明显,多进程各自加各自的,多线程就加到一起了。

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