本文实例讲述了Python多进程multiprocessing用法。分享给大家供大家参考，具体如下：mutilprocess
本文实例讲述了Python多进程multiprocessing用法。分享给大家供大家参考，具体如下：
mutilprocess简介
像线程一样管理进程，这个是mutilprocess的核心，他与threading很是相像，对多核CPU的利用率会比threading好的多。
简单的创建进程：
import multiprocessing
def worker(num):
"""thread worker function"""
print 'Worker:', num
return
if __name__ == '__main__':
jobs = []
for i in range(5):
p = multiprocessing.Process(target=worker, args=(i,))
jobs.append(p)
p.start()
确定当前的进程，即是给进程命名，方便标识区分，跟踪
import multiprocessing
import time
def worker():
name = multiprocessing.current_process().name
print name, 'Starting'
time.sleep(2)
print name, 'Exiting'
def my_service():
name = multiprocessing.current_process().name
print name, 'Starting'
time.sleep(3)
print name, 'Exiting'
if __name__ == '__main__':
service = multiprocessing.Process(name='my_service',
target=my_service)
worker_1 = multiprocessing.Process(name='worker 1',
target=worker)
worker_2 = multiprocessing.Process(target=worker) # default name
worker_1.start()
worker_2.start()
service.start()
守护进程就是不阻挡主程序退出，自己干自己的 mutilprocess.setDaemon(True)就这句等待守护进程退出，要加上join,join可以传入浮点数值，等待n久就不等了
守护进程：
import multiprocessing
import time
import sys
def daemon():
name = multiprocessing.current_process().name
print 'Starting:', name
time.sleep(2)
print 'Exiting :', name
def non_daemon():
name = multiprocessing.current_process().name
print 'Starting:', name
print 'Exiting :', name
if __name__ == '__main__':
d = multiprocessing.Process(name='daemon',
target=daemon)
d.daemon = True
n = multiprocessing.Process(name='non-daemon',
target=non_daemon)
n.daemon = False
d.start()
n.start()
d.join(1)
print 'd.is_alive()', d.is_alive()
n.join()
最好使用 poison pill，强制的使用terminate()注意 terminate之后要join，使其可以更新状态
终止进程：
import multiprocessing
import time
def slow_worker():
print 'Starting worker'
time.sleep(0.1)
print 'Finished worker'
if __name__ == '__main__':
p = multiprocessing.Process(target=slow_worker)
print 'BEFORE:', p, p.is_alive()
p.start()
print 'DURING:', p, p.is_alive()
p.terminate()
print 'TERMINATED:', p, p.is_alive()
p.join()
print 'JOINED:', p, p.is_alive()
①. == 0 未生成任何错误 ②. 0 进程有一个错误，并以该错误码退出 ③. < 0 进程由一个-1 * exitcode信号结束
进程的退出状态：
import multiprocessing
import sys
import time
def exit_error():
sys.exit(1)
def exit_ok():
return
def return_value():
return 1
def raises():
raise RuntimeError('There was an error!')
def terminated():
time.sleep(3)
if __name__ == '__main__':
jobs = []
for f in [exit_error, exit_ok, return_value, raises, terminated]:
print 'Starting process for', f.func_name
j = multiprocessing.Process(target=f, name=f.func_name)
jobs.append(j)
j.start()
jobs[-1].terminate()
for j in jobs:
j.join()
print '%15s.exitcode = %s' % (j.name, j.exitcode)
方便的调试，可以用logging
日志：
import multiprocessing
import logging
import sys
def worker():
print 'Doing some work'
sys.stdout.flush()
if __name__ == '__main__':
multiprocessing.log_to_stderr()
logger = multiprocessing.get_logger()
logger.setLevel(logging.INFO)
p = multiprocessing.Process(target=worker)
p.start()
p.join()
利用class来创建进程，定制子类
派生进程：
import multiprocessing
class Worker(multiprocessing.Process):
def run(self):
print 'In %s' % self.name
return
if __name__ == '__main__':
jobs = []
for i in range(5):
p = Worker()
jobs.append(p)
p.start()
for j in jobs:
j.join()
python进程间传递消息：
import multiprocessing
class MyFancyClass(object):
def __init__(self, name):
self.name = name
def do_something(self):
proc_name = multiprocessing.current_process().name
print 'Doing something fancy in %s for %s!' % \
(proc_name, self.name)
def worker(q):
obj = q.get()
obj.do_something()
if __name__ == '__main__':
queue = multiprocessing.Queue()
p = multiprocessing.Process(target=worker, args=(queue,))
p.start()
queue.put(MyFancyClass('Fancy Dan'))
# Wait for the worker to finish
queue.close()
queue.join_thread()
p.join()
import multiprocessing
import time
class Consumer(multiprocessing.Process):
def __init__(self, task_queue, result_queue):
multiprocessing.Process.__init__(self)
self.task_queue = task_queue
self.result_queue = result_queue
def run(self):
proc_name = self.name
while True:
next_task = self.task_queue.get()
if next_task is None:
# Poison pill means shutdown
print '%s: Exiting' % proc_name
self.task_queue.task_done()
break
print '%s: %s' % (proc_name, next_task)
answer = next_task()
self.task_queue.task_done()
self.result_queue.put(answer)
return
class Task(object):
def __init__(self, a, b):
self.a = a
self.b = b
def __call__(self):
time.sleep(0.1) # pretend to take some time to do the work
return '%s * %s = %s' % (self.a, self.b, self.a * self.b)
def __str__(self):
return '%s * %s' % (self.a, self.b)
if __name__ == '__main__':
# Establish communication queues
tasks = multiprocessing.JoinableQueue()
results = multiprocessing.Queue()
# Start consumers
num_consumers = multiprocessing.cpu_count() * 2
print 'Creating %d consumers' % num_consumers
consumers = [ Consumer(tasks, results)
for i in xrange(num_consumers) ]
for w in consumers:
w.start()
# Enqueue jobs
num_jobs = 10
for i in xrange(num_jobs):
tasks.put(Task(i, i))
# Add a poison pill for each consumer
for i in xrange(num_consumers):
tasks.put(None)
# Wait for all of the tasks to finish
tasks.join()
# Start printing results
while num_jobs:
result = results.get()
print 'Result:', result
num_jobs -= 1
Event提供一种简单的方法，可以在进程间传递状态信息。事件可以切换设置和未设置状态。通过使用一个可选的超时值，时间对象的用户可以等待其状态从未设置变为设置。
进程间信号传递：
import multiprocessing
import time
def wait_for_event(e):
"""Wait for the event to be set before doing anything"""
print 'wait_for_event: starting'
e.wait()
print 'wait_for_event: e.is_set()->', e.is_set()
def wait_for_event_timeout(e, t):
"""Wait t seconds and then timeout"""
print 'wait_for_event_timeout: starting'
e.wait(t)
print 'wait_for_event_timeout: e.is_set()->', e.is_set()
if __name__ == '__main__':
e = multiprocessing.Event()
w1 = multiprocessing.Process(name='block',
target=wait_for_event,
args=(e,))
w1.start()
w2 = multiprocessing.Process(name='nonblock',
target=wait_for_event_timeout,
args=(e, 2))
w2.start()
print 'main: waiting before calling Event.set()'
time.sleep(3)
e.set()
print 'main: event is set'
Python多进程，一般的情况是Queue来传递。
Queue：
from multiprocessing import Process, Queue
def f(q):
q.put([42, None, 'hello'])
if __name__ == '__main__':
q = Queue()
p = Process(target=f, args=(q,))
p.start()
print q.get() # prints "[42, None, 'hello']"
p.join()
多线程优先队列Queue：
import Queue
import threading
import time
exitFlag = 0
class myThread (threading.Thread):
def __init__(self, threadID, name, q):
threading.Thread.__init__(self)
self.threadID = threadID
self.name = name
self.q = q
def run(self):
print "Starting " + self.name
process_data(self.name, self.q)
print "Exiting " + self.name
def process_data(threadName, q):
while not exitFlag:
queueLock.acquire()
if not workQueue.empty():
data = q.get()
queueLock.release()
print "%s processing %s" % (threadName, data)
else:
queueLock.release()
time.sleep(1)
threadList = ["Thread-1", "Thread-2", "Thread-3"]
nameList = ["One", "Two", "Three", "Four", "Five"]
queueLock = threading.Lock()
workQueue = Queue.Queue(10)
threads = []
threadID = 1
# Create new threads
for tName in threadList:
thread = myThread(threadID, tName, workQueue)
thread.start()
threads.append(thread)
threadID += 1
# Fill the queue
queueLock.acquire()
for word in nameList:
workQueue.put(word)
queueLock.release()
# Wait for queue to empty
while not workQueue.empty():
pass
# Notify threads it's time to exit
exitFlag = 1
# Wait for all threads to complete
for t in threads:
t.join()
print "Exiting Main Thread"
多进程使用Queue通信的例子
import time
from multiprocessing import Process,Queue
MSG_QUEUE = Queue(5)
def startA(msgQueue):
while True:
if msgQueue.empty() > 0:
print ('queue is empty %d' % (msgQueue.qsize()))
else:
msg = msgQueue.get()
print( 'get msg %s' % (msg,))
time.sleep(1)
def startB(msgQueue):
while True:
msgQueue.put('hello world')
print( 'put hello world queue size is %d' % (msgQueue.qsize(),))
time.sleep(3)
if __name__ == '__main__':
processA = Process(target=startA,args=(MSG_QUEUE,))
processB = Process(target=startB,args=(MSG_QUEUE,))
processA.start()
print( 'processA start..')
主进程定义了一个Queue类型的变量，并作为Process的args参数传给子进程processA和processB，两个进程一个向队列中写数据，一个读数据。
Python 多进程 multiprocessing