python多进程优化软件_性能测试工具开发基础:python库介绍-multiprocessing:多进程...

简介

进程是运行的程序,每个进程有自己的系统状态,包含了内存、打开文件列表、程序计数器(跟踪执行的指令)、存储函数本地调用变量的堆栈。

使用os或subprocess可以创建新进程,比如:os.fork(), subprocess.Popen()。子进程和父进程是相互独立执行的。

interprocess communication (IPC)进程间的通信: 最常见的形式是基于消息传递(message passing)。message是原始字节的缓存,通过I/O channel比如网络socket和管道,使用原语比如send() and recv()来发送接收消息。次常用的有内存映射区:memory-mapped regions,见mmap模块,实际上是共享内存。

线程有自己的控制流和执行堆栈,但是共享系统资源和数据。

并发的难点:同步和数据共享。解决的方法一般是使用互斥锁。

write_lock = Lock()

...

# Critical section where writing occurs

write_lock.acquire()

f.write("Here's some data.\n")

f.write("Here's more data.\n")

...

write_lock.release()

python的并发程序设计

多数系统上,Python支持消息传递和基于线程的并发程序设计。global interpreter lock (the GIL)机制实际每个时间单元只允许单个线程执行,哪怕有多个CPU。如果瓶颈在I/O,使用多线程效果不错;如果在cpu,效果则会更差。还不如使用子进程和消息传递。线程数一多经常出现以下怪异的问题,比如100个线程工作良好,1000个线程就可能出问题了,这种情况一般需要使用异步事件处理系统,比如中央事件循环可能使用select模块监控I/O资源和分发异步到大量的I/O 处理器。asyncore和流行的第三方的Twisted (http://twistedmatrix/com)可以实现这点。

消息传递在python使用很广,甚至在线程中。它难于出错,减少了锁和同步原语的使用。可以扩展至网络和分布式系统。Python的高级特性比如协程序(coroutines)也使用消息传递抽象。

multiprocessing支持子进程、通信和共享数据、执行不同形式的同步。

multiprocessing

Process类

这个类表示子进程中运行的任务:Process([group [, target [, name [, args [, kwargs]]]]]),构造函数中必须使用关键字参数,target表示可调用对象,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。

方法有:is_alive()、.join([timeout])、run()、start()、terminate()。

属性有: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。

Process类中,注意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。

创建函数并将其作为单个进程。

import multiprocessing

import time

def clock(interval):

for i in range(3):

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

time.sleep(interval)

if __name__ == '__main__':

p = multiprocessing.Process(target=clock, args=(2,))

p.start()

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):

for i in range(3):

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

time.sleep(self.interval)

if __name__ == '__main__':

p = ClockProcess(2)

p.start()

注意,要在命令行才能执行,用IDE是不行的。

进程通信

multiprocessing支持管道和队列,都是用消息传递来实现的,队列接口和线程中的队列类似。

Queue([maxsize]):默认不限制大小,队列实质是用管道和锁来实现的。支持线程会给底层管道传送数据。

方法有:cancel_join_thread()、close()、empty()、full()、get([block [, timeout]])、get_nowait()(等同于get(False))、join_thread()、put(item [, block [, timeout]])、put_nowait(item)(等同于put(item, False))、qsize()、JoinableQueue([maxsize])、task_done()、join()

下例使用队列进行通信:

JoinableQueue创建连接的进程队列。队列和普通队列基本一样,不过消费者在处理完毕之后可以通知生产者(q.task_done())。使用共享信号和条件变量实现。join()由生产者使用,等待所有成员都收到task_done。

import multiprocessing

def consumer(input_q):

while True:

item = input_q.get()

print(item)

input_q.task_done()

def producer(sequence, output_q):

for item in sequence:

output_q.put(item)

if __name__ == '__main__':

q = multiprocessing.JoinableQueue()

cons_p = multiprocessing.Process(target=consumer, args=(q,))

cons_p.daemon = True

cons_p.start()

sequence = [1, 2, 3, 4]

producer(sequence, q)

q.join()

这里控制多进程的关键在于队列get()之后,使用task_done()指示该元素处理完毕;进程启动之前设置了daemon为True;对队列使用join()。

这种方法可以启动多个进程,如下:

process = []

key_list = multiprocessing.JoinableQueue()

# Launch the consumer process

for i in range(10):

t = multiprocessing.Process(target=consumer,args=(key_list,lock))

t.daemon=True

process.append(t)

for i in range(10):

process[i].start()

producer( key_list )

key_list.join()

下面有个应用实例:

在某些程序中,生产者需要告知消费者没有更多项目了,消费者可以关闭了。这时需要使用哨兵(sentinel)。

#!/usr/bin/env python

# -*- coding: utf-8 -*-

# multiprocessing_sentinel.py

# Author Rongzhong Xu 2016-08-11 wechat: pythontesting

"""

multiprocessing sentinel demo,

Tesed in python2.7/3.5/2.6

"""

import multiprocessing

def consumer(input_q):

while True:

item = input_q.get()

if item is None:

break

# Process item

print(item) # Replace with useful work

# Shutdown

print("Consumer done")

def producer(sequence, output_q):

for item in sequence:

# Put the item on the queue

output_q.put(item)

if __name__ == '__main__':

q = multiprocessing.Queue()

# Launch the consumer process

cons_p = multiprocessing.Process(target=consumer, args=(q,))

cons_p.start()

# Produce items

sequence = [1, 2, 3, 4]

producer(sequence, q)

# Signal completion by putting the sentinel on the queue

q.put(None)

# Wait for the consumer process to shutdown

cons_p.join()

注意:每个消费者都需要一个:sentinel,可以使用for语句来实现

for i in range(10):

q.put(None)

实际使用中不局限于使用None,使用其他特殊符号等也是可以的。上面程序从表面看比使用JoinableQueue要复杂,实现的效果又是一样的。实际上这种场景应用更广泛,在consumer比较耗时的情况下,JoinableQueue如果锁住整个函数则互相等待的时间太长,如果不锁,后面几次执行可能丢失数据。

管道

使用管道:Pipe([duplex]),返回值:元组(conn1, conn2)。conn1和conn2为Connection对象,代表管道的末端。管道默认是双向的,如果设置duplex为False,conn1只能接收,conn2只能发送。

Connection对象的方法和属性如下:

close()、fileno()、poll([timeout])、recv()、recv_bytes([maxlength])、recv_bytes_into(buffer [, offset])、send(obj)、send_bytes(buffer [, offset [, size]])

下面例子实现和之前类似的功能:

def consumer(pipe):

output_p, input_p = pipe

input_p.close() # Close the input end of the pipe

while True:

try:

item = output_p.recv()

except EOFError:

break

# Process item

print(item) # Replace with useful work

# Shutdown

print("Consumer done")

# Produce items and put on a queue. sequence is an

# iterable representing items to be processed.

def producer(sequence, input_p):

for item in sequence:

# Put the item on the queue

input_p.send(item)

if __name__ == '__main__':

(output_p, input_p) = multiprocessing.Pipe()

# Launch the consumer process

cons_p = multiprocessing.Process(

target=consumer, args=((output_p, input_p),))

cons_p.start()

# Close the output pipe in the producer

output_p.close()

# Produce items

sequence = [1, 2, 3, 4]

producer(sequence, input_p)

# Signal completion by closing the input pipe

input_p.close()

# Wait for the consumer process to shutdown

cons_p.join()

管道还可以用于双向通信,比如下例的C/S模式:

import multiprocessing

# A server process

def adder(pipe):

server_p, client_p = pipe

client_p.close()

while True:

try:

x, y = server_p.recv()

except EOFError:

break

result = x + y

server_p.send(result)

# Shutdown

print("Server done")

if __name__ == '__main__':

(server_p, client_p) = multiprocessing.Pipe()

# Launch the server process

adder_p = multiprocessing.Process(

target=adder, args=((server_p, client_p),))

adder_p.start()

# Close the server pipe in the client

server_p.close()

# Make some requests on the server

client_p.send((3, 4))

print(client_p.recv())

client_p.send(('Hello', 'World'))

print(client_p.recv())

# Done. Close the pipe

client_p.close()

# Wait for the consumer process to shutdown

adder_p.join()

send()和recv()使用pickle序列化对象。更高级的程序需要使用远程过程调用,需要使用到进程池。

进程池

Pool类在简单的情况下可用于管理固定数量的消费者。进程池的功能和列表解析及函数式编程中的map-reduce类似。

import multiprocessing

import time

def do_calculation(data):

return data * 2

def start_process():

print('Starting {0}'.format(multiprocessing.current_process().name))

if __name__ == '__main__':

# convert range to list for python3

inputs = list(range(100))

time1 = time.time()

builtin_outputs = map(do_calculation, inputs)

# convert to list for python3

print('Built-in: {0}'.format(list(builtin_outputs)))

time2 = time.time()

print(time2 - time1)

pool_size = multiprocessing.cpu_count() * 2

pool = multiprocessing.Pool(processes=pool_size,

initializer=start_process,

)

pool_outputs = pool.map(do_calculation, inputs)

pool.close() # no more tasks

pool.join() # wrap up current tasks

time3 = time.time()

print('Pool : {0}'.format(pool_outputs))

print(time3 - time2)

执行结果:

$ python3 multiprocessing_pool.py

Built-in: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198]

3.790855407714844e-05

Starting ForkPoolWorker-1

Starting ForkPoolWorker-2

Starting ForkPoolWorker-3

Starting ForkPoolWorker-4

Starting ForkPoolWorker-5

Starting ForkPoolWorker-6

Starting ForkPoolWorker-7

Starting ForkPoolWorker-8

Starting ForkPoolWorker-9

Starting ForkPoolWorker-10

Starting ForkPoolWorker-11

Starting ForkPoolWorker-12

Starting ForkPoolWorker-13

Starting ForkPoolWorker-14

Starting ForkPoolWorker-15

Starting ForkPoolWorker-16

Pool : [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198]

0.2203056812286377

上面例子先计算map的时间,然后用进程池的map,计算出时间。在列表数比较少的情况下,多进程的执行时间更短。列表数比较多的情况下,多进程的执行时间更长,可见python内置的map是效率比较高的。

如果消费者函数有内存泄露,可以在执行任务之后重启,设定maxtasksperchild参数即可。

import time

def do_calculation(data):

return data * 2

def start_process():

print('Starting {0}'.format(multiprocessing.current_process().name))

if __name__ == '__main__':

# convert range to list for python3

inputs = list(range(100))

time1 = time.time()

builtin_outputs = map(do_calculation, inputs)

# convert to list for python3

print('Built-in: {0}'.format(list(builtin_outputs)))

time2 = time.time()

print(time2 - time1)

pool_size = multiprocessing.cpu_count() * 2

pool = multiprocessing.Pool(processes=pool_size,

initializer=start_process,

maxtasksperchild=3,

)

pool_outputs = pool.map(do_calculation, inputs)

pool.close() # no more tasks

pool.join() # wrap up current tasks

time3 = time.time()

print('Pool : {0}'.format(pool_outputs))

print(time3 - time2)

执行结果:

$ python3 multiprocessing_pool2.py

Built-in: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198]

3.600120544433594e-05

Starting ForkPoolWorker-1

Starting ForkPoolWorker-3

Starting ForkPoolWorker-2

Starting ForkPoolWorker-4

Starting ForkPoolWorker-5

Starting ForkPoolWorker-6

Starting ForkPoolWorker-7

Starting ForkPoolWorker-8

Starting ForkPoolWorker-9

Starting ForkPoolWorker-10

Starting ForkPoolWorker-11

Starting ForkPoolWorker-12

Starting ForkPoolWorker-13

Starting ForkPoolWorker-14

Starting ForkPoolWorker-15

Starting ForkPoolWorker-16

Starting ForkPoolWorker-17

Starting ForkPoolWorker-18

Starting ForkPoolWorker-19

Starting ForkPoolWorker-20

Starting ForkPoolWorker-21

Starting ForkPoolWorker-22

Starting ForkPoolWorker-23

Starting ForkPoolWorker-24

Starting ForkPoolWorker-25

Starting ForkPoolWorker-26

Starting ForkPoolWorker-27

Starting ForkPoolWorker-28

Starting ForkPoolWorker-29

Starting ForkPoolWorker-30

Starting ForkPoolWorker-31

Starting ForkPoolWorker-32

Pool : [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198]

0.23842501640319824

从结果看,进程数有所增加。(注意,进程数似乎比预期的要少)

Pool([numprocess [,initializer [, initargs]]])

numprocess的默认值是cpu_count()。方法有:apply(func [, args [, kwargs]]),apply_async(func [, args [, kwargs [, callback]]]),close(),join(),imap(func, iterable [, chunksize]),imap_unordered(func, iterable [, chunksize]]),map(func, iterable [, chunksize]),map_async(func, iterable [, chunksize [, callback]]),terminate().

返回结果AsyncResult的方法:get([timeout])、ready()、sucessful()、wait([timeout])、wait([timeout])

以下代码生成指定目录的文件名和SHA512对应表的字典。

import multiprocessing

import hashlib

import binascii

# Some parameters you can tweak

BUFSIZE = 8192 # Read buffer size

POOLSIZE = 2 # Number of workers

def compute_digest(filename):

try:

f = open(filename, "rb")

except IOError:

return None

digest = hashlib.sha512()

while True:

chunk = f.read(BUFSIZE)

if not chunk:

break

digest.update(chunk)

f.close()

return filename, digest.digest()

def build_digest_map(topdir):

digest_pool = multiprocessing.Pool(POOLSIZE)

allfiles = (os.path.join(path, name)

for path, dirs, files in os.walk(topdir)

for name in files)

digest_map = dict(digest_pool.imap_unordered(compute_digest, allfiles, 20))

digest_pool.close()

return digest_map

# Try it out. Change the directory name as desired.

if __name__ == '__main__':

digest_map = build_digest_map("/home/andrew/data/code/python/\

python-chinese-library/libraries/multiprocessing")

print(len(digest_map))

for key in digest_map.keys():

print("{0}: {1}".format(key, binascii.hexlify(digest_map[key])))

共享数据和同步

共享内存通过mmap实现。共享内存中创建的是ctypes对象,不需要管道中的序列化。

Value(typecode, arg1, ... argN, lock),RawValue(typecode, arg1, ..., argN),Array(typecode, initializer, lock),RawArray(typecode, initializer)

原语有: Lock,Rlock,Semaphore,BoundedSemaphore,Event,Condition.

import multiprocessing

class FloatChannel(object):

def __init__(self, maxsize):

self.buffer = multiprocessing.RawArray('d', maxsize)

self.buffer_len = multiprocessing.Value('i')

self.empty = multiprocessing.Semaphore(1)

self.full = multiprocessing.Semaphore(0)

def send(self, values):

self.empty.acquire() # Only proceed if buffer empty

nitems = len(values)

self.buffer_len = nitems # Set the buffer size

self.buffer[:nitems] = values # Copy values into the buffer

self.full.release() # Signal that buffer is full

def recv(self):

self.full.acquire() # Only proceed if buffer full

values = self.buffer[:self.buffer_len.value] # Copy values

self.empty.release() # Signal that buffer is empty

return values

# Performance test. Receive a bunch of messages

def consume_test(count, ch):

for i in range(count):

values = ch.recv()

# Performance test. Send a bunch of messages

def produce_test(count, values, ch):

for i in range(count):

ch.send(values)

if __name__ == '__main__':

ch = FloatChannel(100000)

p = multiprocessing.Process(target=consume_test,

args=(1000, ch))

p.start()

values = [float(x) for x in range(100000)]

produce_test(1000, values, ch)

print("Done")

p.join()

参考资料

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