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3.线程间分享数据.md

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线程间分享数据

一.利用全局变量进行数据的分享

from threading import Thread
import time

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
    print("----in work2, g_num is %d---" % g_num)


print("---线程创建之前g_num is %d---" % g_num)

if __name__ == '__main__':
    t1 = Thread(target=work1)
    t1.start()

    # 延时一会，保证t1线程中的事情做完
    time.sleep(1)

    t2 = Thread(target=work2)
    t2.start()

运行结果：

---线程创建之前g_num is 100---
----in work1, g_num is 103---
----in work2, g_num is 103---

可能会引起资源竞争

二.利用可变类型进行数据的分享

from threading import Thread
import time


def work1(nums):
    nums.append(44)
    print("----in work1---", nums)


def work2(nums):
    # 延时一会，保证t1线程中的事情做完
    time.sleep(1)
    print("----in work2---", nums)


g_nums = [11, 22, 33]

t1 = Thread(target=work1, args=(g_nums,))
t1.start()

t2 = Thread(target=work2, args=(g_nums,))
t2.start()

运行结果：

----in work1--- [11, 22, 33, 44]
----in work2--- [11, 22, 33, 44]

注意：可能会引发资源竞争的问题

总结:

在一个进程内的所有线程共享全局变量，很方便在多个线程间共享数据

缺点就是，线程是对全局变量随意遂改可能造成多线程之间对全局变量的混乱（即线程非安全）

三.多线程-共享全局变量问题

资源竞争问题

假设两个线程t1和t2都要对全局变量g_num(默认是0)进行加1运算，t1和t2都各对g_num加10次，g_num的最终的结果应该为20。

但是由于是多线程同时操作，有可能出现下面情况：

  1. 在g_num=0时，t1取得g_num=0。此时系统把t1调度为”sleeping”状态，把t2转换为”running”状态，t2也获得g_num=0
  2. 然后t2对得到的值进行加1并赋给g_num，使得g_num=1
  3. 然后系统又把t2调度为”sleeping”，把t1转为”running”。线程t1又把它之前得到的0加1后赋值给g_num。
  4. 这样导致虽然t1和t2都对g_num加1，但结果仍然是g_num=1

Demo01:

import threading
import time

g_num = 0


def work1(num):
    global g_num
    for i in range(num):
        g_num += 1
    print("----in work1, g_num is %d---" % g_num)


def work2(num):
    global g_num
    for i in range(num):
        g_num += 1
    print("----in work2, g_num is %d---" % g_num)


print("---线程创建之前g_num is %d---" % g_num)

t1 = threading.Thread(target=work1, args=(10,))
t1.start()

t2 = threading.Thread(target=work2, args=(10,))
t2.start()

while len(threading.enumerate()) != 1:
    time.sleep(1)

print("2个线程对同一个全局变量操作之后的最终结果是:%s" % g_num)

运行结果：

---线程创建之前g_num is 0---
----in work1, g_num is 10---
----in work2, g_num is 20---
2个线程对同一个全局变量操作之后的最终结果是:20

数据量较小无法展示具体的效果，下列案例较好的展示出问题

demo02:

import threading
import time

g_num = 0


def work1(num):
    global g_num
    for i in range(num):
        g_num += 1
    print("----in work1, g_num is %d---" % g_num)


def work2(num):
    global g_num
    for i in range(num):
        g_num += 1
    print("----in work2, g_num is %d---" % g_num)


print("---线程创建之前g_num is %d---" % g_num)

t1 = threading.Thread(target=work1, args=(1000000,))
t1.start()

t2 = threading.Thread(target=work2, args=(1000000,))
t2.start()

while len(threading.enumerate()) != 1:
    time.sleep(1)

print("2个线程对同一个全局变量操作之后的最终结果是:%s" % g_num)

运行结果：

---线程创建之前g_num is 0---
----in work1, g_num is 1348580---
----in work2, g_num is 1482666---
2个线程对同一个全局变量操作之后的最终结果是:1482666

结论：

如果多个线程同时对同一个全局变量操作，会出现资源竞争问题，从而数据结果会不正确