Python--进程

　　　　　　　　使用multiprocessing进行进程管理

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

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

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()

终止进程

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

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()

进程的退出状态

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

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)

日志

方便的调试，可以用logging

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()

派生进程

利用class来创建进程，定制子类

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

这一块我之前结合SocketServer写过一点，见Python多进程

一般的情况是Queue来传递。

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

进程间信号传递

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。

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'

################################################################################################################

################################################################################################################

先使用:
j.start()
j_1.start()
再使用:
j.join()　　　　　　#也就是先启动后上锁
j_1.join()　　　　　#join的意思是把当前的分进程运行完了之后才运行下面的进程

################################################################################################################

################################################################################################################

multiprocess.Lock()
当多线程需要共享资源的时候，Lock可以用来避免访问的冲突

lock1 = multiprocessing.Lock()
lock2 = multiprocessing.Lock()
lock3 = multiprocessing.Lock()
lock4 = multiprocessing.Lock()
lock5 = multiprocessing.Lock()
lock6 = multiprocessing.Lock()
lock7 = multiprocessing.Lock()
lock8 = multiprocessing.Lock()