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python 多进程使用总结

时间:2016-12-16 19:22来源:www.chengxuyuans.com 点击:

  python中的多进程主要使用到 multiprocessing 这个库。这个库在使用 multiprocessing.Manager().Queue时会出问题,建议大家升级到高版本python,如2.7.11,可具体参考《python版本升级》。

  python使用线程池可参考《python线程池实现》

一、多进程使用

1、linux下可使用 fork 函数

#!/bin/env python
import os

print 'Process (%s) start...' % os.getpid()
pid = os.fork()
if pid==0:
    print 'I am child process (%s) and my parent is %s.' % (os.getpid(), os.getppid())
    os._exit(1)
else:
    print 'I (%s) just created a child process (%s).' % (os.getpid(), pid)

输出 

Process (22246) start...
I (22246) just created a child process (22247).
I am child process (22247) and my parent is 22246.

2、使用 multiprocessing

#!/bin/env python
from multiprocessing import Process
import os
import time

def run_proc(name):
    time.sleep(3)
    print 'Run child process %s (%s)...' % (name, os.getpid())

if __name__=='__main__':
    print 'Parent process %s.' % os.getpid()
    processes = list()
    for i in range(5):
        p = Process(target=run_proc, args=('test',))
        print 'Process will start.'
        p.start()
        processes.append(p)
    
    for p in processes:
        p.join()
    print 'Process end.'

输出

Parent process 38140.
Process will start.
Process will start.
Process will start.
Process will start.
Process will start.
Run child process test (38141)...
Run child process test (38142)...
Run child process test (38143)...
Run child process test (38145)...
Run child process test (38144)...
Process end.

real    0m3.028s
user    0m0.021s
sys     0m0.004s

 

二、进程池

1、使用 multiprocessing.Pool 非阻塞

#!/bin/env python

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 xrange(3):
        msg = "hello %d" %(i)
        pool.apply_async(func, (msg, ))

    print "Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~"
    pool.close()
    pool.join()    # behind close() or terminate()
    print "Sub-process(es) done."

运行结果

Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~
msg: hello 0
msg: hello 1
msg: hello 2
end
end
end
Sub-process(es) done.

real    0m3.493s
user    0m0.056s
sys     0m0.022s

2、使用 multiprocessing.Pool 阻塞版本

#!/bin/env python

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 xrange(3):
        msg = "hello %d" %(i)
        pool.apply(func, (msg, ))      

    print "Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~"
    pool.close()
    pool.join()    # behind close() or terminate()
    print "Sub-process(es) done."

运行结果

msg: hello 0
end
msg: hello 1
end
msg: hello 2
end
Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~
Sub-process(es) done.

real    0m9.061s
user    0m0.036s
sys     0m0.019s

区别主要是 apply_async和 apply函数,前者是非阻塞的,后者是阻塞。可以看出运行时间相差的倍数正是进程池数量

3、使用 multiprocessing.Pool 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 xrange(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."

运行结果

msg: hello 0
msg: hello 1
msg: hello 2
end
end
end
::: donehello 0
::: donehello 1
::: donehello 2
Sub-process(es) done.

real    0m3.526s
user    0m0.054s
sys     0m0.024s

4、在类中使用 multiprocessing.Pool

类中使用进程池会一般会出现错误

PicklingError: Can't pickle <type 'instancemethod'>: attribute lookup __builtin__.instancemethod failed

这个提示是因为 multiprocessing.Pool中使用了Queue通信,所有进入队列的数据必须可序列化(picklable),包括自定义类实例等。如下:

#!/bin/env python

import multiprocessing

class SomeClass(object):
    def __init__(self):
        pass

    def f(self, x):
        return x*x

    def go(self):
        pool = multiprocessing.Pool(processes=4)
        #result = pool.apply_async(self.f, [10])     
        #print result.get(timeout=1)           
        print pool.map(self.f, range(10))

SomeClass().go()

运行提示

Traceback (most recent call last):
  File "4.py", line 18, in <module>
    SomeClass().go()
  File "4.py", line 16, in go
    print pool.map(self.f, range(10))
  File "/usr/local/lib/python2.7/multiprocessing/pool.py", line 251, in map
    return self.map_async(func, iterable, chunksize).get()
  File "/usr/local/lib/python2.7/multiprocessing/pool.py", line 567, in get
    raise self._value
cPickle.PicklingError: Can't pickle <type 'instancemethod'>: attribute lookup __builtin__.instancemethod failed

解决如下:(1)

#!/bin/env python
import multiprocessing

def func(x):
    return x*x

class SomeClass(object):
    def __init__(self,func):
        self.f = func

    def go(self):
        pool = multiprocessing.Pool(processes=4)
        #result = pool.apply_async(self.f, [10])
        #print result.get(timeout=1)
        print pool.map(self.f, range(10))

SomeClass(func).go()

输出结果:

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

(2)一般情况下我们如果在类中写好了处理逻辑,想要尽可能减少代码变动则可以使用下面方法

#!/bin/env python

import multiprocessing

class SomeClass(object):
    def __init__(self):
        pass

    def f(self, x):
        return x*x

    def go(self):
        result = list()
        pool = multiprocessing.Pool(processes=4)
        for i in range(10):
            result.append(pool.apply_async(func, [self, i]))
        pool.close()
        pool.join()
        for res in result:
            print res.get(timeout=1)   

def func(client, x):
    return client.f(x)

SomeClass().go()

输出结果:

0
1
4
9
16
25
36
49
64
81

使用(2)的解决方法需要注意,如果SomeClass实例中有包含任何不可序列化的数据则会一直报错,一般是到res.get()报错,这时候你就要重新查看代码是否有不可序列化的变量了。如果有的话可以更改成全局变量解决。

 

三、多进程中使用线程池

有一种情景下需要使用到多进程和多线程:在CPU密集型的情况下一个ip的处理速度是0.04秒前后,单线程运行的时间大概是3m32s,单个CPU使用率100%;使用进程池(size=10)时间大概是6m50s,其中只有1个进程的CPU使用率达到90%,其他均是在30%左右;使用线程池(size=10)时间大概是4m39s,单个CPU使用率100%

可以看出使用多进程在这时候并不占优势,反而更慢。因为进程间的切换消耗了大部分资源和时间,而一个ip只需要0.04秒。而使用线程池由于只能利用单核CPU,则再怎么加大线程数量都没法提升速度,所以这时候应该使用多进程加多线程结合。

def run(self):
    self.getData()
    ipNums = len(self.ipInfo)
    step = ipNums / multiprocessing.cpu_count()
    ipList = list()
    i = 0
    j = 1
    processList = list()
    for ip in self.ipInfo:
        ipList.append(ip)
        i += 1
        if i == step * j or i == ipNums:
            j += 1
            def innerRun():
                wm = Pool.ThreadPool(CONF.POOL_SIZE)
                for myIp in ipList:
                    wm.addJob(self.handleOne, myIp)
                wm.waitForComplete()
            process = multiprocessing.Process(target=innerRun)
            process.start()
            processList.append(process)
            ipList = list()
    for process in processList:
        process.join()

机器有8个CPU,则使用8个进程加线程池,速度提升到35s,8个CPU的利用率均在50%左右,机器平均CPU75%左右。

 

四、多进程间通信

个人使用的比较多的是Manager,其他特别是分布式多进程可学习 廖雪峰官方网站 http://www.liaoxuefeng.com/wiki/001374738125095c955c1e6d8bb493182103fac9270762a000/001386832973658c780d8bfa4c6406f83b2b3097aed5df6000

 

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