python 进程同步；

线程之间是共享内存的，进程之间是相互独立的，如何使进程之间相互通信？

#！/usr/bin/python
#_*_ coding:UTF-8 _*_


from multiprocessing import Pool,Queue,Process

'''
def run(info_list,n):
    info_list.append(n)
    print(info_list)


info_list = []
for i in range(10):
    p = Process(target=run,args=[info_list,i])
    p.start()

'''

def f(q,n):
    q.put(n + "hello")

#multiprocessing自带的queue 
q = Queue()

for i in range(10):
    p = Process(target=f,args=[q,i])
    p.start()
    while True：
        print(q.get())

打印结果：

1  hello
2  hello
3  hello
4  hello
5  hello
6  hello

在同一队列中；

通过Value Array 实现数据交互；

#！/usr/bin/python
#_*_ coding:UTF-8 _*_


from multiprocessing import Process,Value,Array

def f(n,a,raw_list):
    n.value = 3.1415926
    for i in range(5):
        a[i] = -a[i]
    raw_list.append(100)
    print(raw_list)

if __name__ == "__main__":
    #f父进程
    num = Value('d',0.0)
    array = Array('i',range(10))
    raw_list = [1,2,3,4,5]
    print(num.value)
    print(array[:])
    print("_____________________")
    print(raw_list)

    #父进程产生的子进程，子进程克隆父进程的数据
    p = Process(target=f,args=[num,array,raw_list])
    p.start()
    p.join()
    #父进程通过打印能够看到父进程的数据已经被子进程修改了，默认是不能够修改的
    print(num.value)
    print(array[:])
    print(raw_list)

、打印结果：

#父进程中的数据
0.0
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
_____________________
#创建的原生态数据
[1, 2, 3, 4, 5]
#子进程中的copy的内容仅仅修改了子进程中的数据，不能与父进程数据进行交互；
[1, 2, 3, 4, 5, 100]

通过子进程更改后的数据
3.1415926
[0, -1, -2, -3, -4, 5, 6, 7, 8, 9]
#创建的原生态数据，子进程没有改变父进程中的数据
[1, 2, 3, 4, 5]

进程已结束,退出代码0

manager

Manager返回的Manager（）对象控制一个承载Python对象的服务器进程，并允许其他进程使用代理来操纵它们。

支持类型：

list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Barrier, Queue, Value and Array.

from multiprocessing import Process, Manager

def f(d, l):
    d[1] = '1'
    d['2'] = 2
    d[0.25] = None
    l.reverse()

if __name__ == '__main__':
    with Manager() as manager:
        d = manager.dict()
        l = manager.list(range(10))

        p = Process(target=f, args=(d, l))
        p.start()
        p.join()

        print(d)
        print(l)

打印结果：

{1: '1', '2': 2, 0.25: None}
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

进程已结束,退出代码0

pool

from multiprocessing import Pool, TimeoutError
import time
import os

def f(x):
    return x*x

if __name__ == '__main__':
    # start 4 worker processes
    with Pool(processes=4) as pool:

        # print "[0, 1, 4,..., 81]"
        print(pool.map(f, range(10)))

        # print same numbers in arbitrary order
        for i in pool.imap_unordered(f, range(10)):
            print(i)

        # evaluate "f(20)" asynchronously
        res = pool.apply_async(f, (20,))      # runs in *only* one process
        print(res.get(timeout=1))             # prints "400"

        # evaluate "os.getpid()" asynchronously
        res = pool.apply_async(os.getpid, ()) # runs in *only* one process
        print(res.get(timeout=1))             # prints the PID of that process

        # launching multiple evaluations asynchronously *may* use more processes
        multiple_results = [pool.apply_async(os.getpid, ()) for i in range(4)]
        print([res.get(timeout=1) for res in multiple_results])

        # make a single worker sleep for 10 secs
        res = pool.apply_async(time.sleep, (10,))
        try:
            print(res.get(timeout=1))
        except TimeoutError:
            print("We lacked patience and got a multiprocessing.TimeoutError")

        print("For the moment, the pool remains available for more work")

    # exiting the 'with'-block has stopped the pool
    print("Now the pool is closed and no longer available")

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