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！