【转】【Python】Python多进程与多线程
1.1 multiprocessing
multiprocessing是多进程模块,多进程提供了任务并发性,能充分利用多核处理器。避免了GIL(全局解释锁)对资源的影响。
有以下常用类:
类 |
描述 |
Process(group=None, target=None, name=None, args=(), kwargs={}) | 派生一个进程对象,然后调用start()方法启动 |
Pool(processes=None, initializer=None, initargs=()) |
返回一个进程池对象,processes进程池进程数量 |
Pipe(duplex=True) | 返回两个连接对象由管道连接 |
Queue(maxsize=0) | 返回队列对象,操作方法跟Queue.Queue一样 |
multiprocessing.dummy | 这个库是用于实现多线程 |
Process()类有以下些方法:
run() | |
start() | 启动进程对象 |
join([timeout]) | 等待子进程终止,才返回结果。可选超时。 |
name | 进程名字 |
is_alive() | 返回进程是否存活 |
daemon | 进程的守护标记,一个布尔值 |
pid | 返回进程ID |
exitcode | 子进程退出状态码 |
terminate() | 终止进程。在unix上使用SIGTERM信号,在windows上使用TerminateProcess()。 |
Pool()类有以下些方法:
apply(func, args=(), kwds={}) | 等效内建函数apply() |
apply_async(func, args=(), kwds={}, callback=None) | 异步,等效内建函数apply() |
map(func, iterable, chunksize=None) | 等效内建函数map() |
map_async(func, iterable, chunksize=None, callback=None) | 异步,等效内建函数map() |
imap(func, iterable, chunksize=1) | 等效内建函数itertools.imap() |
imap_unordered(func, iterable, chunksize=1) | 像imap()方法,但结果顺序是任意的 |
close() | 关闭进程池 |
terminate() | 终止工作进程,垃圾收集连接池对象 |
join() | 等待工作进程退出。必须先调用close()或terminate() |
Pool.apply_async()和Pool.map_aysnc()又提供了以下几个方法:
get([timeout]) | 获取结果对象里的结果。如果超时没有,则抛出TimeoutError异常 |
wait([timeout]) | 等待可用的结果或超时 |
ready() | 返回调用是否已经完成 |
successful() |
举例:
1)简单的例子,用子进程处理函数
from multiprocessing import Process import os def worker(name): print name print 'parent process id:', os.getppid() print 'process id:', os.getpid() if __name__ == '__main__': p = Process(target=worker, args=('function worker.',)) p.start() p.join() print p.name # python test.py function worker. parent process id: 9079 process id: 9080 Process-1
Process实例传入worker函数作为派生进程执行的任务,用start()方法启动这个实例。
2)加以说明join()方法
from multiprocessing import Process import os def worker(n): print 'hello world', n if __name__ == '__main__': print 'parent process id:', os.getppid() for n in range(5): p = Process(target=worker, args=(n,)) p.start() p.join() print 'child process id:', p.pid print 'child process name:', p.name # python test.py parent process id: 9041 hello world 0 child process id: 9132 child process name: Process-1 hello world 1 child process id: 9133 child process name: Process-2 hello world 2 child process id: 9134 child process name: Process-3 hello world 3 child process id: 9135 child process name: Process-4 hello world 4 child process id: 9136 child process name: Process-5 # 把p.join()注释掉再执行 # python test.py parent process id: 9041 child process id: 9125 child process name: Process-1 child process id: 9126 child process name: Process-2 child process id: 9127 child process name: Process-3 child process id: 9128 child process name: Process-4 hello world 0 hello world 1 hello world 3 hello world 2 child process id: 9129 child process name: Process-5 hello world 4
可以看出,在使用join()方法时,输出的结果都是顺序排列的。相反是乱序的。因此join()方法是堵塞父进程,要等待当前子进程执行完后才会继续执行下一个子进程。否则会一直生成子进程去执行任务。
在要求输出的情况下使用join()可保证每个结果是完整的。
3)给子进程命名,方便管理
from multiprocessing import Process import os, time def worker1(n): print 'hello world', n def worker2(): print 'worker2...' if __name__ == '__main__': print 'parent process id:', os.getppid() for n in range(3): p1 = Process(name='worker1', target=worker1, args=(n,)) p1.start() p1.join() print 'child process id:', p1.pid print 'child process name:', p1.name p2 = Process(name='worker2', target=worker2) p2.start() p2.join() print 'child process id:', p2.pid print 'child process name:', p2.name # python test.py parent process id: 9041 hello world 0 child process id: 9248 child process name: worker1 hello world 1 child process id: 9249 child process name: worker1 hello world 2 child process id: 9250 child process name: worker1 worker2... child process id: 9251 child process name: worker2
4)设置守护进程,父进程退出也不影响子进程运行
from multiprocessing import Process def worker1(n): print 'hello world', n def worker2(): print 'worker2...' if __name__ == '__main__': for n in range(3): p1 = Process(name='worker1', target=worker1, args=(n,)) p1.daemon = True p1.start() p1.join() p2 = Process(target=worker2) p2.daemon = False p2.start() p2.join()
5)使用进程池
#!/usr/bin/python # -*- coding: utf-8 -*- from multiprocessing import Pool, current_process import os, time, sys def worker(n): print 'hello world', n print 'process name:', current_process().name # 获取当前进程名字 time.sleep(1) # 休眠用于执行时有时间查看当前执行的进程 if __name__ == '__main__': p = Pool(processes=3) for i in range(8): r = p.apply_async(worker, args=(i,)) r.get(timeout=5) # 获取结果中的数据 p.close() # python test.py hello world 0 process name: PoolWorker-1 hello world 1 process name: PoolWorker-2 hello world 2 process name: PoolWorker-3 hello world 3 process name: PoolWorker-1 hello world 4 process name: PoolWorker-2 hello world 5 process name: PoolWorker-3 hello world 6 process name: PoolWorker-1 hello world 7 process name: PoolWorker-2
进程池生成了3个子进程,通过循环执行8次worker函数,进程池会从子进程1开始去处理任务,当到达最大进程时,会继续从子进程1开始。
在运行此程序同时,再打开一个终端窗口会看到生成的子进程:
# ps -ef |grep python root 40244 9041 4 16:43 pts/3 00:00:00 python test.py root 40245 40244 0 16:43 pts/3 00:00:00 python test.py root 40246 40244 0 16:43 pts/3 00:00:00 python test.py root 40247 40244 0 16:43 pts/3 00:00:00 python test.py
6)进程池map()方法
map()方法是将序列中的元素通过函数处理返回新列表。
from multiprocessing import Pool def worker(url): return 'http://%s' % url urls = ['www.baidu.com', 'www.jd.com'] p = Pool(processes=2) r = p.map(worker, urls) p.close() print r # python test.py ['http://www.baidu.com', 'http://www.jd.com']
7)Queue进程间通信
multiprocessing支持两种类型进程间通信:Queue和Pipe。
Queue库已经封装到multiprocessing库中,在第十章 Python常用标准库已经讲解到Queue库使用,有需要请查看以前博文。
例如:一个子进程向队列写数据,一个子进程读取队列数据
#!/usr/bin/python # -*- coding: utf-8 -*- from multiprocessing import Process, Queue # 写数据到队列 def write(q): for n in range(5): q.put(n) print 'Put %s to queue.' % n # 从队列读数据 def read(q): while True: if not q.empty(): value = q.get() print 'Get %s from queue.' % value else: break if __name__ == '__main__': q = Queue() pw = Process(target=write, args=(q,)) pr = Process(target=read, args=(q,)) pw.start() pw.join() pr.start() pr.join() # python test.py Put 0 to queue. Put 1 to queue. Put 2 to queue. Put 3 to queue. Put 4 to queue. Get 0 from queue. Get 1 from queue. Get 2 from queue. Get 3 from queue. Get 4 from queue.
8)Pipe进程间通信
from multiprocessing import Process, Pipe def f(conn): conn.send([42, None, 'hello']) conn.close() if __name__ == '__main__': parent_conn, child_conn = Pipe() p = Process(target=f, args=(child_conn,)) p.start() print parent_conn.recv() p.join() # python test.py [42, None, 'hello']
Pipe()创建两个连接对象,每个链接对象都有send()和recv()方法,
9)进程间对象共享
Manager类返回一个管理对象,它控制服务端进程。提供一些共享方式:Value()、Array()、list()、dict()、Event()等
创建Manger对象存放资源,其他进程通过访问Manager获取。
from multiprocessing import Process, Manager def f(v, a, l, d): v.value = 100 a[0] = 123 l.append('Hello') d['a'] = 1 mgr = Manager() v = mgr.Value('v', 0) a = mgr.Array('d', range(5)) l = mgr.list() d = mgr.dict() p = Process(target=f, args=(v, a, l, d)) p.start() p.join() print(v) print(a) print(l) print(d) # python test.py Value('v', 100) array('d', [123.0, 1.0, 2.0, 3.0, 4.0]) ['Hello'] {'a': 1}
10)写一个多进程的例子
比如:多进程监控URL是否正常
from multiprocessing import Pool, current_process import urllib2 urls = [ 'http://www.baidu.com', 'http://www.jd.com', 'http://www.sina.com', 'http://www.163.com', ] def status_code(url): print 'process name:', current_process().name try: req = urllib2.urlopen(url, timeout=5) return req.getcode() except urllib2.URLError: return p = Pool(processes=4) for url in urls: r = p.apply_async(status_code, args=(url,)) if r.get(timeout=5) == 200: print "%s OK" %url else: print "%s NO" %url # python test.py process name: PoolWorker-1 http://www.baidu.com OK process name: PoolWorker-2 http://www.jd.com OK process name: PoolWorker-3 http://www.sina.com OK process name: PoolWorker-4 http://www.163.com OK
1.2 threading
threading模块类似于multiprocessing多进程模块,使用方法也基本一样。threading库是对thread库进行二次封装,我们主要用到Thread类,用Thread类派生线程对象。
1)使用Thread类实现多线程
from threading import Thread, current_thread def worker(n): print 'thread name:', current_thread().name print 'hello world', n for n in range(5): t = Thread(target=worker, args=(n, )) t.start() t.join() # 等待主进程结束 # python test.py thread name: Thread-1 hello world 0 thread name: Thread-2 hello world 1 thread name: Thread-3 hello world 2 thread name: Thread-4 hello world 3 thread name: Thread-5 hello world 4
2)还有一种方式继承Thread类实现多线程,子类可以重写__init__和run()方法实现功能逻辑。
#!/usr/bin/python # -*- coding: utf-8 -*- from threading import Thread, current_thread class Test(Thread): # 重写父类构造函数,那么父类构造函数将不会执行 def __init__(self, n): Thread.__init__(self) self.n = n def run(self): print 'thread name:', current_thread().name print 'hello world', self.n if __name__ == '__main__': for n in range(5): t = Test(n) t.start() t.join() # python test.py thread name: Thread-1 hello world 0 thread name: Thread-2 hello world 1 thread name: Thread-3 hello world 2 thread name: Thread-4 hello world 3 thread name: Thread-5 hello world 4
3)Lock
from threading import Thread, Lock, current_thread lock = Lock() class Test(Thread): # 重写父类构造函数,那么父类构造函数将不会执行 def __init__(self, n): Thread.__init__(self) self.n = n def run(self): lock.acquire() # 获取锁 print 'thread name:', current_thread().name print 'hello world', self.n lock.release() # 释放锁 if __name__ == '__main__': for n in range(5): t = Test(n) t.start() t.join()
众所周知,Python多线程有GIL全局锁,意思是把每个线程执行代码时都上了锁,执行完成后会自动释放GIL锁,意味着同一时间只有一个线程在运行代码。由于所有线程共享父进程内存、变量、资源,很容易多个线程对其操作,导致内容混乱。
当你在写多线程程序的时候如果输出结果是混乱的,这时你应该考虑到在不使用锁的情况下,多个线程运行时可能会修改原有的变量,导致输出不一样。
由此看来Python多线程是不能利用多核CPU提高处理性能,但在IO密集情况下,还是能提高一定的并发性能。也不必担心,多核CPU情况可以使用多进程实现多核任务。Python多进程是复制父进程资源,互不影响,有各自独立的GIL锁,保证数据不会混乱。能用多进程就用吧!