A LITTLE BIT MORE FAST,A LITTLE BIT MORE STRONG。
实现python的多线程编程有很多模块可以使用，比如thread模块，threading模块。但thread模块已经过时，不推荐使用，threading 模块提供了比thread模块更方便的同步机制，推荐使用.
以下代码实现threading.Thread的一个子类，实现了线程对不同函数的通用行。将其保存为MyThread.py以供随时使用1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30#!/usr/bin/env python
#coding=utf-8
import threading
from time import ctime
class MyThread(threading.Thread):
'''
make a threading class for some function
'''
def __init__(self, func, args, name = ''):
threading.Thread.__init__(self)
self.name = name
self.func = func
self.args = args
def getResult(self):
return self.res
def run(self):
print 'starting', self.name, 'at:', ctime()
self.res = apply(self.func, self.args)
print self.name,'finished at:', ctime()
test_mythread.py脚本，比较了递归求斐波那契数列，阶乘和累加和函数的运行。脚本先在单线程中运行着三个函数，然后在多线程中做同样的事。以说明多线程的好处。在脚本中使用了上面子类化过的MyThread1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61#!/usr/bin/env python
from MyThread import MyThread
from time import sleep,ctime
def fib(x):
sleep(0.005)
if x < 2: return 1
return (fib(x-2)+fib(x-1))
def fac(n):
sleep(0.1)
if n < 2:
return 1
return n*fac(n-1)
def sum(n):
sleep(0.1)
if n<1:
return 0
return n+sum(n-1)
funcs = [fib, fac, sum]
n=12
def main():
nfuncs = range(len(funcs))
print '*** SINGLE TRHEADS'
for i in nfuncs:
print 'starting ',funcs[i].__name__, 'at:', ctime()
print funcs[i](n)
print funcs[i].__name__,'finished at:', ctime()
print '\n### MULTIPLE THREADS'
threads = []
for i in nfuncs:
t = MyThread(funcs[i], (n,), name = funcs[i].__name__)
threads.append(t)
for i in nfuncs:
threads[i].start()
for i in nfuncs:
threads[i].join()
print threads[i].getResult()
print 'ALL DONE'
if __name__ == '__main__':
main()