众所周知,python本身是单线程的,python中的线程处理是由python解释器分配时间片的;
但在python 2.6中吸收了开源模块,开始支持系统原生的进程处理——multiprocessing.
注意:这个模块的某些函数需要操作系统的支持,
例如,multiprocessing.synchronize模块在某些平台上引入时会激发一个ImportError
1)Process
要创建一个Process是很简单的。
from multiprocessing import Process
def f(name):
print('hello', name)
if __name__ == '__main__':
p = Process(target=f, args=('bob',))
p.start()
p.join()
要获得一个Process的进程ID也是很简单的。
from multiprocessing import Process
import os
def info(title):
print title
print 'module name:', __name__
print 'parent process:', os.getppid()#这个测试不通过,3.0不支持
print 'process id:', os.getpid()
def f(name):
info('function f')
print 'hello', name
if __name__ == '__main__':
info('main line')
p = Process(target=f, args=('bob',))
p.start()
p.join()
创建进程:multiprocessing.Process([group[, target[, name[, args[, kargs]]]]])
参数:
group: None,它的存在仅仅是为了与threading.Thread兼容
target: 一般是函数
name: 进程名
args: 函数的参数
kargs: keywords参数
函数:
run() 默认的run()函数调用target的函数,你也可以在子类中覆盖该函数
start() 启动该进程
join([timeout]) 父进程被停止,直到子进程被执行完毕。
当timeout为None时没有超时,否则有超时。
进程可以被join很多次,但不能join自己
is_alive()
terminate() 结束进程。
在Unix上使用的是SIGTERM
在Windows平台上使用TerminateProcess
属性:
name 进程名
daemon 守护进程
pid 进程ID
exitcode 如果进程还没有结束,该值为None
authkey
2)Queue
Queue类似于queue.Queue,一般用来进程间交互信息
例子:
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()
注意:Queue是进程和线程安全的。
Queue实现了queue.Queue的大部分方法,但task_done()和join()没有实现。
创建Queue:multiprocessing.Queue([maxsize])
函数:
qsize() 返回Queue的大小
empty() 返回一个boolean值表示Queue是否为空
full() 返回一个boolean值表示Queue是否满
put(item[, block[, timeout]])
put_nowait(item)
get([block[, timeout]])
get_nowait()
get_no_wait()
close() 表示该Queue不在加入新的元素
join_thread()
cancel_join_thread()
3)JoinableQueue
创建:multiprocessing.JoinableQueue([maxsize])
task_done()
join()
4)Pipe
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()) # prints "[42, None, 'hello']"
p.join()
multiprocessing.Pipe([duplex]) 返回一个Connection对象
5)异步化synchronization
from multiprocessing import Process, Lock
def f(l, i):
l.acquire()
print('hello world', i)
l.release()
if __name__ == '__main__':
lock = Lock()
for num in range(10):
Process(target=f, args=(lock, num)).start()
6)Shared Memory
from multiprocessing import Process, Value, Array
def f(n, a):
n.value = 3.1415927
for i in range(len(a)):
a[i] = -a[i]
if __name__ == '__main__':
num = Value('d', 0.0)
arr = Array('i', range(10))
p = Process(target=f, args=(num, arr))
p.start()
p.join()
print(num.value)
print(arr[:])
1>Value
2>Array
7)Manager
from multiprocessing import Process, Manager
def f(d, l):
d[1] = '1'
d['2'] = 2
d[0.25] = None
l.reverse()
if __name__ == '__main__':
manager = Manager()
d = manager.dict()
l = manager.list(range(10))
p = Process(target=f, args=(d, l))
p.start()
p.join()
print(d)
print(l)
8)Pool
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
pool = Pool(processes=4) # start 4 worker processes
result = pool.apply_async(f, [10]) # evaluate "f(10)" asynchronously
print result.get(timeout=1) # prints "100" unless your computer is *very* slow
print pool.map(f, range(10)) # prints "[0, 1, 4,..., 81]"
multiprocessing.Pool([processes[, initializer[, initargs]]])
函数:
apply(func[, args[, kwds]])
apply_async(func[, args[, kwds[, callback]]])
map(func,iterable[, chunksize])
map_async(func,iterable[, chunksize[, callback]])
imap(func, iterable[, chunksize])
imap_unordered(func, iterable[, chunksize])
close()
terminate()
join()
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
pool = Pool(processes=4) # start 4 worker processes
result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously
print(result.get(timeout=1)) # prints "100" unless your computer is *very* slow
print(pool.map(f, range(10))) # prints "[0, 1, 4,..., 81]"
it = pool.imap(f, range(10))
print(next(it)) # prints "0"
print(next(it)) # prints "1"
print(it.next(timeout=1)) # prints "4" unless your computer is *very* slow
import time
result = pool.apply_async(time.sleep, (10,))
print(result.get(timeout=1)) # raises TimeoutError
9)杂项
multiprocessing.active_children() 返回所有活动子进程的列表
multiprocessing.cpu_count() 返回CPU数目
multiprocessing.current_process() 返回当前进程对应的Process对象
multiprocessing.freeze_support()
multiprocessing.set_executable()
10)Connection对象
send(obj)
recv()
fileno()
close()
poll([timeout])
send_bytes(buffer[, offset[, size]])
recv_bytes([maxlength])
recv_bytes_info(buffer[, offset])
>>> from multiprocessing import Pipe
>>> a, b = Pipe()
>>> a.send([1, 'hello', None])
>>> b.recv()
[1, 'hello', None]
>>> b.send_bytes('thank you')
>>> a.recv_bytes()
'thank you'
>>> import array
>>> arr1 = array.array('i', range(5))
>>> arr2 = array.array('i', [0] * 10)
>>> a.send_bytes(arr1)
>>> count = b.recv_bytes_into(arr2)
>>> assert count == len(arr1) * arr1.itemsize
>>> arr2
array('i', [0, 1, 2, 3, 4, 0, 0, 0, 0, 0])