开发笔记:Python中多进程的使用

#queue 多线程各个线程的运算的值放到一个队列中，到主线程的时候再拿出来,以此来代替
#return的功能，因为在线程是不能返回一个值的
import time
import threading
from Queue import Queue

def job(l,q):
q.put([i**2 for i in l])

def multithreading(data):
q = Queue()
threads = []
for i in xrange(4):
t = threading.Thread(target = job,args = (data[i],q))
t.start()
threads.append(t)
for thread in threads:
thread.join()
results = []
for _ in range(4):
results.append(q.get())
print results

if __name__ == "__main__":
data = [[1,2,3],[4,5,6],[3,4,3],[5,5,5]]
multithreading(data)

[[1, 4, 9], [16, 25, 36], [9, 16, 9], [25, 25, 25]]

import multiprocessing
import time
def worker(interval):
n = 5
while n > 0:
print("The time is {0}".format(time.ctime()))
time.sleep(interval)
n -= 1
if __name__ == "__main__":
p = multiprocessing.Process(target = worker, args = (3,))
p.start()
print "p.pid:", p.pid
print "p.name:", p.name
print "p.is_alive:", p.is_alive()

# 进程池 ,Pool中是有return的
import multiprocessing as mp
def job(x):
return x ** 2
def multiprocess():
pool = mp.Pool() # 默认是有几个核就用几个，可以自己设置processes = ？
res = pool.map(job, range(10)) # 可以放入可迭代对象，自动分配进程
print(res)
# apply_async(func[, args[, kwds[, callback]]]) 它是非阻塞，apply(func[, args[, kwds]])是阻塞的
res = pool.apply_async(job, (2,)) # 一次只能在一个进程里计算，要达到map的效果，要迭代
print(res.get())
multi_res = [pool.apply_async(job, (i,)) for i in range(10)] # 迭代器
print([res.get() for res in multi_res])
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
# 4
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]