python多进程

python多进程

序.multiprocessing

python中的多线程其实并不是真正的多线程，如果想要充分地使用多核CPU的资源，在python中大部分情况需要使用多进程。Python提供了非常好用的多进程包multiprocessing，只需要定义一个函数，Python会完成其他所有事情。借助这个包，可以轻松完成从单进程到并发执行的转换。multiprocessing支持子进程、通信和共享数据、执行不同形式的同步，提供了Process、Queue、Pipe、Lock等组件。

一、Process

process介绍

创建进程的类：Process([group [, target [, name [, args [, kwargs]]]]])，target表示调用对象，args表示调用对象的位置参数元组。kwargs表示调用对象的字典。name为别名。group实质上不使用。
方法：is_alive()、join([timeout])、run()、start()、terminate()。其中，Process以start()启动某个进程。
属性：authkey、daemon（要通过start()设置）、exitcode(进程在运行时为None、如果为–N，表示被信号N结束）、name、pid。其中daemon是父进程终止后自动终止，且自己不能产生新进程，必须在start()之前设置。

例1.1：创建函数并将其作为单个进程

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())

------------------------------------------------

>>> p.pid: 1004

>>> p.name: Process-1

>>> p.is_alive: True

>>> The time is Mon Jul 29 21:31:11 2019

>>> The time is Mon Jul 29 21:31:14 2019

>>> The time is Mon Jul 29 21:31:17 2019

>>> The time is Mon Jul 29 21:31:20 2019

>>> The time is Mon Jul 29 21:31:23 2019

例1.2：创建函数并将其作为多个进程

import multiprocessing

import time

def worker_1(interval):

    print("worker_1")

    time.sleep(interval)

    print("end worker_1")

def worker_2(interval):

    print("worker_2")

    time.sleep(interval)

    print("end worker_2")

def worker_3(interval):

    print("worker_3")

    time.sleep(interval)

    print("end worker_3")

if __name__ == "__main__":

    p1 = multiprocessing.Process(target = worker_1, args = (2,))

    p2 = multiprocessing.Process(target = worker_2, args = (3,))

    p3 = multiprocessing.Process(target = worker_3, args = (4,))

    p1.start()

    p2.start()

    p3.start()

    print("The number of CPU is:" + str(multiprocessing.cpu_count()))

    for p in multiprocessing.active_children():

        print("child   p.name:" + p.name + "\tp.id" + str(p.pid))

    print("END")

------------------------------------------------

>>> The number of CPU is:8

>>> child   p.name:Process-3	p.id18208

>>> child   p.name:Process-2	p.id1404

>>> child   p.name:Process-1	p.id11684

>>> END

>>> worker_1

>>> worker_2

>>> worker_3

>>> end worker_1

>>> end worker_2

>>> end worker_3

例1.3：将进程定义为类

import multiprocessing

import time

class ClockProcess(multiprocessing.Process):

    def __init__(self, interval):

        multiprocessing.Process.__init__(self)

        self.interval = interval

    def run(self):

        n = 5

        while n > 0:

            print("the time is {0}".format(time.ctime()))

            time.sleep(self.interval)

            n -= 1

if __name__ == '__main__':

    p = ClockProcess(3)

    p.start() 

------------------------------------------------

>>> the time is Mon Jul 29 21:43:07 2019

>>> the time is Mon Jul 29 21:43:10 2019

>>> the time is Mon Jul 29 21:43:13 2019

>>> the time is Mon Jul 29 21:43:16 2019

>>> the time is Mon Jul 29 21:43:19 2019

注：进程p调用start()时，自动调用run()

例1.4：daemon程序对比结果

1.4-1 不加daemon属性

import multiprocessing

import time

def worker(interval):

    print("work start:{0}".format(time.ctime()));

    time.sleep(interval)

    print("work end:{0}".format(time.ctime()));

if __name__ == "__main__":

    p = multiprocessing.Process(target = worker, args = (3,))

    p.start()

    print("end!")

------------------------------------------------

>>> end!

>>> work start:Tue Jul 29 21:29:10 2019

>>> work end:Tue Jul 29 21:29:13 2019

1.4-2 加上daemon属性

import multiprocessing

import time

def worker(interval):

    print("work start:{0}".format(time.ctime()));

    time.sleep(interval)

    print("work end:{0}".format(time.ctime()));

if __name__ == "__main__":

    p = multiprocessing.Process(target = worker, args = (3,))

    p.daemon = True

    p.start()

    print("end!")

------------------------------------------------

>>> end!

注：因子进程设置了daemon属性，主进程结束，它们就随着结束了。

1.4-3 设置daemon执行完结束的方法

import multiprocessing

import time

def worker(interval):

    print("work start:{0}".format(time.ctime()));

    time.sleep(interval)

    print("work end:{0}".format(time.ctime()));

if __name__ == "__main__":

    p = multiprocessing.Process(target = worker, args = (3,))

    p.daemon = True

    p.start()

    p.join()

    print("end!")

------------------------------------------------

>>> work start:Tue Jul 29 22:16:32 2019

>>> work end:Tue Jul 29 22:16:35 2019

>>> end!

二、Lock

当多个进程需要访问共享资源的时候，Lock可以用来避免访问的冲突。

import multiprocessing

import sys

def worker_with(lock, f):

    with lock:

        fs = open(f, 'a+')

        n = 10

        while n > 1:

            fs.write("Lockd acquired via with\n")

            n -= 1

        fs.close()

def worker_no_with(lock, f):

    lock.acquire()

    try:

        fs = open(f, 'a+')

        n = 10

        while n > 1:

            fs.write("Lock acquired directly\n")

            n -= 1

        fs.close()

    finally:

        lock.release()

if __name__ == "__main__":

    lock = multiprocessing.Lock()

    f = "file.txt"

    w = multiprocessing.Process(target = worker_with, args=(lock, f))

    nw = multiprocessing.Process(target = worker_no_with, args=(lock, f))

    w.start()

    nw.start()

    print("end")

------------------------------------------------

>>> Lockd acquired via with

>>> Lockd acquired via with

>>> Lockd acquired via with

>>> Lockd acquired via with

>>> Lockd acquired via with

>>> Lockd acquired via with

>>> Lockd acquired via with

>>> Lockd acquired via with

>>> Lockd acquired via with

>>> Lock acquired directly

>>> Lock acquired directly

>>> Lock acquired directly

>>> Lock acquired directly

>>> Lock acquired directly

>>> Lock acquired directly

>>> Lock acquired directly

>>> Lock acquired directly

>>> Lock acquired directly

三、Semaphore

Semaphore用来控制对共享资源的访问数量，例如池的最大连接数。

import multiprocessing

import time

def worker(s, i):

    s.acquire()

    print(multiprocessing.current_process().name + "acquire");

    time.sleep(i)

    print(multiprocessing.current_process().name + "release\n");

    s.release()

if __name__ == "__main__":

    s = multiprocessing.Semaphore(2)

    for i in range(5):

        p = multiprocessing.Process(target = worker, args=(s, i*2))

        p.start()

------------------------------------------------

>>> Process-1acquire

>>> Process-1release

>>>

>>> Process-2acquire

>>> Process-3acquire

>>> Process-2release

>>>

>>> Process-5acquire

>>> Process-3release

>>>

>>> Process-4acquire

>>> Process-5release

>>>

>>> Process-4release

四、Event

Event用来实现进程间同步通信。

import multiprocessing

import time

def wait_for_event(e):

    print("wait_for_event: starting")

    e.wait()

    print("wairt_for_event: e.is_set()->" + str(e.is_set()))

def wait_for_event_timeout(e, t):

    print("wait_for_event_timeout:starting")

    e.wait(t)

    print("wait_for_event_timeout:e.is_set->" + str(e.is_set()))

if __name__ == "__main__":

    e = multiprocessing.Event()

    w1 = multiprocessing.Process(name = "block",

            target = wait_for_event,

            args = (e,))

    w2 = multiprocessing.Process(name = "non-block",

            target = wait_for_event_timeout,

            args = (e, 2))

    w1.start()

    w2.start()

    time.sleep(3)

    e.set()

    print("main: event is set")

------------------------------------------------

>>> wait_for_event: starting

>>> wait_for_event_timeout:starting

>>> wait_for_event_timeout:e.is_set->False

>>> main: event is set

>>> wairt_for_event: e.is_set()->True

五、Queue

Queue是多进程安全的队列，可以使用Queue实现多进程之间的数据传递。put方法用以插入数据到队列中，put方法还有两个可选参数：blocked和timeout。如果blocked为True（默认值），并且timeout为正值，该方法会阻塞timeout指定的时间，直到该队列有剩余的空间。如果超时，会抛出Queue.Full异常。如果blocked为False，但该Queue已满，会立即抛出Queue.Full异常。

get方法可以从队列读取并且删除一个元素。同样，get方法有两个可选参数：blocked和timeout。如果blocked为True（默认值），并且timeout为正值，那么在等待时间内没有取到任何元素，会抛出Queue.Empty异常。如果blocked为False，有两种情况存在，如果Queue有一个值可用，则立即返回该值，否则，如果队列为空，则立即抛出Queue.Empty异常。Queue的一段示例代码：

import multiprocessing

def writer_proc(q):

    try:

        q.put(1, block = False)

    except:

        pass   

def reader_proc(q):

    try:

        print(q.get(block = False))

    except:

        pass

if __name__ == "__main__":

    q = multiprocessing.Queue()

    writer = multiprocessing.Process(target=writer_proc, args=(q,))

    writer.start()   

    reader = multiprocessing.Process(target=reader_proc, args=(q,))

    reader.start()  

    reader.join()

    writer.join()

------------------------------------------------

>>> 1

六、Pipe

Pipe方法返回(conn1, conn2)代表一个管道的两个端。Pipe方法有duplex参数，如果duplex参数为True(默认值)，那么这个管道是全双工模式，也就是说conn1和conn2均可收发。duplex为False，conn1只负责接受消息，conn2只负责发送消息。

send和recv方法分别是发送和接受消息的方法。例如，在全双工模式下，可以调用conn1.send发送消息，conn1.recv接收消息。如果没有消息可接收，recv方法会一直阻塞。如果管道已经被关闭，那么recv方法会抛出EOFError。

import multiprocessing

import time

def proc1(pipe):

    while True:

        for i in range(10000):

            print("send: %s" %(i))

            pipe.send(i)

            time.sleep(1)

def proc2(pipe):

    while True:

        print("proc2 rev:", pipe.recv())

        time.sleep(1)

def proc3(pipe):

    while True:

        print("PROC3 rev:", pipe.recv())

        time.sleep(1)

if __name__ == "__main__":

    pipe = multiprocessing.Pipe()

    p1 = multiprocessing.Process(target=proc1, args=(pipe[0],))

    p2 = multiprocessing.Process(target=proc2, args=(pipe[1],))

    # p3 = multiprocessing.Process(target=proc3, args=(pipe[1],))

    p1.start()

    p2.start()

    # p3.start()

    p1.join()

    p2.join()

    # p3.join()

------------------------------------------------

>>> send: 0

>>> roc2 rev: 0

>>> send: 1

>>> proc2 rev: 1

>>> send: 2

>>> proc2 rev: 2

>>> send: 3

>>> proc2 rev: 3

>>> send: 4

>>> proc2 rev: 4

>>> send: 5

>>> proc2 rev: 5

>>> send: 6

>>> proc2 rev: 6

>>> send: 7

>>> proc2 rev: 7

>>> send: 8

>>> proc2 rev: 8

     .

     .

     .

     .

     .

     .

七、Pool

在利用Python进行系统管理的时候，特别是同时操作多个文件目录，或者远程控制多台主机，并行操作可以节约大量的时间。当被操作对象数目不大时，可以直接利用multiprocessing中的Process动态成生多个进程，十几个还好，但如果是上百个，上千个目标，手动的去限制进程数量却又太过繁琐，此时可以发挥进程池的功效。

Pool可以提供指定数量的进程，供用户调用，当有新的请求提交到pool中时，如果池还没有满，那么就会创建一个新的进程用来执行该请求；但如果池中的进程数已经达到规定最大值，那么该请求就会等待，直到池中有进程结束，才会创建新的进程来它。

例7.1：使用进程池（非阻塞）

import multiprocessing

import time

def func(msg):

    print("msg:", msg)

    time.sleep(3)

    print("end")

if __name__ == "__main__":

    pool = multiprocessing.Pool(processes = 3)

    for i in range(4):

        msg = "hello %d" %(i)

        pool.apply_async(func, (msg, ))   #维持执行的进程总数为processes，当一个进程执行完毕后会添加新的进程进去

    print("Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~")

    pool.close()

    pool.join()   #调用join之前，先调用close函数，否则会出错。执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束

    print("Sub-process(es) done.")

------------------------------------------------

>>> Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~

>>> msg: hello 0

>>> msg: hello 1

>>> msg: hello 2

>>> end

>>> msg: hello 3

>>> end

>>> end

>>> end

>>> Sub-process(es) done.

函数解释：

apply_async(func[, args[, kwds[, callback]]]) 它是非阻塞，apply(func[, args[, kwds]])是阻塞的（理解区别，看例1例2结果区别）
close() 关闭pool，使其不在接受新的任务。
terminate() 结束工作进程，不在处理未完成的任务。
join() 主进程阻塞，等待子进程的退出， join方法要在close或terminate之后使用。

执行说明：创建一个进程池pool，并设定进程的数量为3，xrange(4)会相继产生四个对象[0, 1, 2, 4]，四个对象被提交到pool中，因pool指定进程数为3，所以0、1、2会直接送到进程中执行，当其中一个执行完事后才空出一个进程处理对象3，所以会出现输出“msg: hello 3”出现在"end"后。因为为非阻塞，主函数会自己执行自个的，不搭理进程的执行，所以运行完for循环后直接输出“mMsg: hark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~”，主程序在pool.join（）处等待各个进程的结束。

例7.2：使用进程池（阻塞）

import multiprocessing

import time

def func(msg):

    print("msg:", msg)

    time.sleep(3)

    print("end")

if __name__ == "__main__":

    pool = multiprocessing.Pool(processes = 3)

    for i in range(4):

        msg = "hello %d" %(i)

        pool.apply(func, (msg, ))   #维持执行的进程总数为processes，当一个进程执行完毕后会添加新的进程进去

    print("Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~")

    pool.close()

    pool.join()   #调用join之前，先调用close函数，否则会出错。执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束

    print("Sub-process(es) done.")

------------------------------------------------

>>> msg: hello 0

>>> end

>>> msg: hello 1

>>> end

>>> msg: hello 2

>>> end

>>> msg: hello 3

>>> end

>>> Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~

>>> Sub-process(es) done.

例7.3：使用进程池，并关注结果

import multiprocessing

import time

def func(msg):

    print("msg:", msg)

    time.sleep(3)

    print("end")

    return "done" + msg

if __name__ == "__main__":

    pool = multiprocessing.Pool(processes=4)

    result = []

    for i in range(3):

        msg = "hello %d" %(i)

        result.append(pool.apply_async(func, (msg, )))

    pool.close()

    pool.join()

    for res in result:

        print(":::", res.get())

    print("Sub-process(es) done.")

------------------------------------------------

>>> msg: hello 0

>>> msg: hello 1

>>> msg: hello 2

>>> end

>>> end

>>> end

>>> ::: donehello 0

>>> ::: donehello 1

>>> ::: donehello 2

>>> Sub-process(es) done.

例7.4：使用多个进程池

import multiprocessing

import os, time, random

def Lee():

    print("\nRun task Lee-%s" % (os.getpid()))  # os.getpid()获取当前的进程的ID

    start = time.time()

    time.sleep(random.random() * 10)  # random.random()随机生成0-1之间的小数

    end = time.time()

    print('Task Lee, runs %0.2f seconds.' % (end - start))

def Marlon():

    print("\nRun task Marlon-%s" % (os.getpid()))

    start = time.time()

    time.sleep(random.random() * 40)

    end = time.time()

    print('Task Marlon runs %0.2f seconds.' % (end - start))

def Allen():

    print("\nRun task Allen-%s" % (os.getpid()))

    start = time.time()

    time.sleep(random.random() * 30)

    end = time.time()

    print('Task Allen runs %0.2f seconds.' % (end - start))

def Frank():

    print("\nRun task Frank-%s" % (os.getpid()))

    start = time.time()

    time.sleep(random.random() * 20)

    end = time.time()

    print('Task Frank runs %0.2f seconds.' % (end - start))

if __name__ == '__main__':

    function_list = [Lee, Marlon, Allen, Frank]

    print("parent process %s" % (os.getpid()))

    pool = multiprocessing.Pool(4)

    for func in function_list:

        pool.apply_async(func)  # Pool执行函数，apply执行函数,当有一个进程执行完毕后，会添加一个新的进程到pool中

    print('Waiting for all subprocesses done...')

    pool.close()

    pool.join()  # 调用join之前，一定要先调用close() 函数，否则会出错, close()执行后不会有新的进程加入到pool,join函数等待素有子进程结束

    print('All subprocesses done.')

------------------------------------------------

>>> parent process 9828

>>> Waiting for all subprocesses done...

>>>

>>> Run task Lee-12948

>>>

>>> Run task Marlon-8948

>>>

>>> Run task Allen-18124

>>>

>>> Run task Frank-17404

>>> Task Frank runs 3.42 seconds.

>>> Task Lee, runs 6.69 seconds.

>>> Task Allen runs 8.38 seconds.

>>> Task Marlon runs 13.37 seconds.

>>> All subprocesses done.