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python多进程详解

程序员文章站 2023-12-24 13:21:39
对于python多进程的包multiprocessing作了一个详细的介绍。 ......

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

: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!

: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.

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