python进阶——多进程

因为GIL(全局解释器锁)的限制(GIL是用来保证在任意时刻只能有一个控制线程在执行),所以python中的多线程并非真正的多线程。只有python程序是I/O密集型应用时,多线程才会对运行效率有显著提高(因在等待I/O的时,会释放GIL允许其他线程继续执行),而在计算密集型应用中,多线程并没有什么用处。考虑到要充分利用多核CPU的资源,允许python可以并行处理一些任务,这里就用到了python多进程编程了。multiprocessing是python中的多进程模块,使用这个模块可以方便地进行多进程应用程序开发。multiprocessing模块中提供了:Process、Pool、Queue、Manager等组件。

1 Process类

1.1 构造方法

def __init__(self, group=None, target=None, name=None, args=(), kwargs={})

group:进程所属组,基本不用
target:进程调用对象(可以是一个函数名,也可以是一个可调用的对象(实现了__call__方法的类))
args:调用对象的位置参数元组
name:别名
kwargs:调用对象的关键字参数字典

1.2 实例方法

is_alive():返回进程是否在运行
start():启动进程,等待CPU调度
join([timeout]):阻塞当前上下文环境,直到调用此方法的进程终止或者到达指定timeout
terminate():不管任务是否完成,立即停止该进程
run():start()调用该方法,当实例进程没有传入target参数,stat()将执行默认的run()方法

1.3 属性

authkey
daemon:守护进程标识,在start()调用之前可以对其进行修改
exitcode:进程的退出状态码
name:进程名
pid:进程id

1.4 实例

实例一:传入的target为一个函数

#!/usr/bin/python
#coding=utf-8

import time
import random
from multiprocessing import Process

def foo(i):
    print time.ctime(), "process the %d begin ......" %i
    time.sleep(random.uniform(1,3))
    print time.ctime(), "process the %d end !!!!!!" %i

if __name__ == "__main__":
    print time.ctime(), "process begin......"
    
    p_lst = list()
    for i in range(4):
        p_lst.append(Process(target=foo, args=(i,)))    #创建4个子进程
    #启动子进程
    for p in p_lst:
        p.start()
    #等待子进程全部结束
    for p in p_lst:
        p.join()

    print time.ctime(), "process end!!!!!"   

实例二:传入的target为一个可调用对象

#!/usr/bin/python
#coding=utf-8

import time
import random
from multiprocessing import Process

class Foo(object):
    def __init__(self, i):
        self.i = i

    def __call__(self):
        '''
        使Foo的实例对象成为可调用对象
        '''                                                                                                        
        print time.ctime(), "process the %d begin ......" %self.i
        time.sleep(random.uniform(1,3))
        print time.ctime(), "process the %d end !!!!!!" %self.i

if __name__ == "__main__":
    print time.ctime(), "process begin......"
    
    p_lst = list()
    for i in range(4):
        p_lst.append(Process(target=Foo(i)))    #创建4个子进程
    #启动子进程
    for p in p_lst:
        p.start()
    #等待子进程全部结束
    for p in p_lst:
        p.join()

    print time.ctime(), "process end!!!!!"

实例三:派生Process子类,并创建子类的实例

#!/usr/bin/python                                                                                                  
#coding=utf-8

import time
import random
from multiprocessing import Process

class MyProcess(Process):

    def __init__(self, i):
        Process.__init__(self)
        self.i = i

    def run(self):
        '''
        #重写run方法,当调用start方法时,就会调用当前重写的run方法中的程序
        '''
        print time.ctime(), "process the %d begin ......" %self.i
        time.sleep(random.uniform(1,3))
        print time.ctime(), "process the %d end !!!!!!" %self.i

if __name__ == "__main__":
    print time.ctime(), "process begin......"

    p_lst = list()
    for i in range(4):
        p_lst.append(MyProcess(i))  #创建4个子进程
    #启动子进程
    for p in p_lst:
        p.start()
    #等待子进程全部结束
    for p in p_lst:
        p.join()

    print time.ctime(), "process end!!!!!"

2 Pool类

当使用Process类管理非常多(几十上百个)的进程时,就会显得比较繁琐,这是就可以使用Pool(进程池)来对进程进行统一管理。当池中进程已满时,有新进程请求执行时,就会被阻塞,直到池中有进程执行结束,新的进程请求才会被放入池中并执行。

2.1 构造方法

def __init__(self, processes=None, initializer=None, initargs=(),                 maxtasksperchild=None)

processes:池中可容纳的工作进程数量,默认情况使用os.cpu_count()返回的数值,一般默认即可
其他参数暂不清楚有什么用处......

2.2 实例方法

apply(self, func, args=(), kwds={}):阻塞型进程池,会阻塞主进程,直到工作进程全部退出,一般不用这个
apply_async(self, func, args=(), kwds={}, callback=None):非阻塞型进程池
map(self, func, iterable, chunksize=None):与内置map行为一致,它会阻塞主进程,直到map运行结束
map_async(self, func, iterable, chunksize=None, callback=None):非阻塞版本的map
close():关闭进程池,不在接受新任务
terminate():结束工作进程
join():阻塞主进程等待子进程退出,该方法必须在close或terminate之后执行

2.3 实例

#!/usr/bin/python
#coding=utf-8

import time
import random
from multiprocessing import Pool

def foo(i):
    print time.ctime(), "process the %d begin ......" %i
    time.sleep(random.uniform(1,3))
    print time.ctime(), "process the %d end !!!!!!" %i

if __name__ == "__main__":

    print time.ctime(), "process begin......"
    pool = Pool(processes = 2)  #设置进程池中最大并行工作进程数为2                                                 
    for i in range(4):
        pool.apply_async(foo, args=(i,))    #提交4个子进程任务

    pool.close()
    pool.join()

    print time.ctime(), "process end!!!!!"

结果:

Fri Nov 18 13:57:22 2016 process begin......
Fri Nov 18 13:57:22 2016 process the 0 begin ......
Fri Nov 18 13:57:22 2016 process the 1 begin ......
Fri Nov 18 13:57:23 2016 process the 1 end !!!!!!
Fri Nov 18 13:57:23 2016 process the 2 begin ......
Fri Nov 18 13:57:24 2016 process the 0 end !!!!!!
Fri Nov 18 13:57:24 2016 process the 3 begin ......
Fri Nov 18 13:57:25 2016 process the 2 end !!!!!!
Fri Nov 18 13:57:25 2016 process the 3 end !!!!!!
Fri Nov 18 13:57:25 2016 process end!!!!!

3 Queue类

Queue主要提供进程间通信以及共享数据等功能。除Queue外还可以使用Pipes实现进程间通信(Pipes是两个进程间进行通信)

3.1 构造方法

def __init__(self, maxsize=0)

maxsize:用于设置队列最大长度,当为maxsize<=0时,队列的最大长度会被设置为一个非常大的值(我的系统中队列最大长度被设置为2147483647)

3.2 实例方法

put(self, obj, block=True, timeout=None)

1、block为True,若队列已满,并且timeout为正值,该方法会阻塞timeout指定的时间,直到队列中有出现剩余空间,如果超时,会抛出Queue.Full异常
2、block为False,若队列已满,立即抛出Queue.Full异常

get(self, block=True, timeout=None)

block为True,若队列为空,并且timeout为正值,该方法会阻塞timeout指定的时间,直到队列中有出现新的数据,如果超时,会抛出Queue.Empty异常
block为False,若队列为空,立即抛出Queue.Empty异常

3.3 实例

#!/usr/bin/python
#coding=utf-8

import time
import random
from multiprocessing import Process, Queue

def write(q):
    for value in "abcd":
        print time.ctime(), "put %s to queue" %value
        q.put(value)
        time.sleep(random.random())

def read(q):
    while True:
        value = q.get()
        print time.ctime(), "get %s from queue" %value

if __name__ == "__main__":
    #主进程创建Queue,并作为参数传递给子进程
    q = Queue()
    pw = Process(target=write, args=(q,))
    pr = Process(target=read, args=(q,))
    #启动子进程pw,往Queue中写入
    pw.start()
    #启动子进程pr,从Queue中读取
    pr.start()
    #等待写进程执行结束
    pw.join()
    #终止读取进程                                                                                                  
    pr.terminate()

运行结果:

Fri Nov 18 15:04:13 2016 put a to queue
Fri Nov 18 15:04:13 2016 get a from queue
Fri Nov 18 15:04:13 2016 put b to queue
Fri Nov 18 15:04:13 2016 get b from queue
Fri Nov 18 15:04:13 2016 put c to queue
Fri Nov 18 15:04:13 2016 get c from queue
Fri Nov 18 15:04:13 2016 put d to queue
Fri Nov 18 15:04:13 2016 get d from queue

4 Manager类

Manager是进程间数据共享的高级接口。
Manager()返回的manager对象控制了一个server进程,此进程包含的python对象可以被其他的进程通过proxies来访问。从而达到多进程间数据通信且安全。Manager支持的类型有list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Queue, Value和Array。
如下是使用Manager管理一个用于多进程共享的dict数据

#!/usr/bin/python
#coding=utf-8

import time
import random
from multiprocessing import Manager, Pool

def worker(d, key, value):
    print time.ctime(), "insert the k-v pair to dict begin: {%d: %d}" %(key, value)
    time.sleep(random.uniform(1,2))
    d[key] = value  #访问共享数据
    print time.ctime(), "insert the k-v pair to dict end: {%d: %d}" %(key, value)


if __name__ == "__main__":
    print time.ctime(), "process for manager begin"
    mgr = Manager()
    d = mgr.dict()
    pool = Pool(processes=4)                                                                                       
    for i in range(10):
        pool.apply_async(worker, args=(d, i, i*i))

    pool.close()
    pool.join()
    print "Result:"
    print d
    print time.ctime(), "process for manager end"

运行结果

Fri Nov 18 16:36:19 2016 process for manager begin
Fri Nov 18 16:36:19 2016 insert the k-v pair to dict begin: {0: 0}
Fri Nov 18 16:36:19 2016 insert the k-v pair to dict begin: {1: 1}
Fri Nov 18 16:36:19 2016 insert the k-v pair to dict begin: {2: 4}
Fri Nov 18 16:36:19 2016 insert the k-v pair to dict begin: {3: 9}
Fri Nov 18 16:36:20 2016 insert the k-v pair to dict end: {3: 9}
Fri Nov 18 16:36:20 2016 insert the k-v pair to dict begin: {4: 16}
Fri Nov 18 16:36:20 2016 insert the k-v pair to dict end: {0: 0}
Fri Nov 18 16:36:20 2016 insert the k-v pair to dict begin: {5: 25}
Fri Nov 18 16:36:21 2016 insert the k-v pair to dict end: {2: 4}
Fri Nov 18 16:36:21 2016 insert the k-v pair to dict begin: {6: 36}
Fri Nov 18 16:36:21 2016 insert the k-v pair to dict end: {1: 1}
Fri Nov 18 16:36:21 2016 insert the k-v pair to dict begin: {7: 49}
Fri Nov 18 16:36:21 2016 insert the k-v pair to dict end: {5: 25}
Fri Nov 18 16:36:21 2016 insert the k-v pair to dict begin: {8: 64}
Fri Nov 18 16:36:22 2016 insert the k-v pair to dict end: {4: 16}
Fri Nov 18 16:36:22 2016 insert the k-v pair to dict begin: {9: 81}
Fri Nov 18 16:36:23 2016 insert the k-v pair to dict end: {8: 64}
Fri Nov 18 16:36:23 2016 insert the k-v pair to dict end: {6: 36}
Fri Nov 18 16:36:23 2016 insert the k-v pair to dict end: {7: 49}
Fri Nov 18 16:36:23 2016 insert the k-v pair to dict end: {9: 81}
Result:
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
Fri Nov 18 16:36:23 2016 process for manager end

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