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()
网友评论