全局变量分别在多进程和多线程中的区别

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。