一个程序,该程序创建在可连接队列上工作的多个进程Q
,并且最终可能会操纵全局字典D
来存储结果。(因此每个子进程都可以D
用来存储其结果,并查看其他子进程正在产生什么结果)
如果我在子进程中打印字典D,我会看到对它进行的修改(即在D上)。但是在主流程加入Q之后,如果我打印D,那就是空洞的字典!
我了解这是同步/锁定问题。有人可以告诉我这里发生了什么,如何同步对D的访问?
一个程序,该程序创建在可连接队列上工作的多个进程Q
,并且最终可能会操纵全局字典D
来存储结果。(因此每个子进程都可以D
用来存储其结果,并查看其他子进程正在产生什么结果)
如果我在子进程中打印字典D,我会看到对它进行的修改(即在D上)。但是在主流程加入Q之后,如果我打印D,那就是空洞的字典!
我了解这是同步/锁定问题。有人可以告诉我这里发生了什么,如何同步对D的访问?
Answers:
一般的答案涉及使用Manager
对象。改编自文档:
from multiprocessing import Process, Manager
def f(d):
d[1] += '1'
d['2'] += 2
if __name__ == '__main__':
manager = Manager()
d = manager.dict()
d[1] = '1'
d['2'] = 2
p1 = Process(target=f, args=(d,))
p2 = Process(target=f, args=(d,))
p1.start()
p2.start()
p1.join()
p2.join()
print d
输出:
$ python mul.py
{1: '111', '2': 6}
多处理不像线程。每个子进程将获得主进程内存的副本。通常,状态是通过通信(管道/套接字),信号或共享内存共享的。
多重处理可为您的用例提供一些抽象-共享状态通过使用代理或共享内存被视为本地状态:http : //docs.python.org/library/multiprocessing.html#sharing-state-between-processes
相关章节:
我想分享自己的工作,该工作比Manager的指令快,并且比使用大量内存并且不适用于Mac OS的pyshmht库更简单,更稳定。虽然我的字典仅适用于纯字符串,并且目前不可变。我使用线性探测实现,并将键和值对存储在表后的单独内存块中。
from mmap import mmap
import struct
from timeit import default_timer
from multiprocessing import Manager
from pyshmht import HashTable
class shared_immutable_dict:
def __init__(self, a):
self.hs = 1 << (len(a) * 3).bit_length()
kvp = self.hs * 4
ht = [0xffffffff] * self.hs
kvl = []
for k, v in a.iteritems():
h = self.hash(k)
while ht[h] != 0xffffffff:
h = (h + 1) & (self.hs - 1)
ht[h] = kvp
kvp += self.kvlen(k) + self.kvlen(v)
kvl.append(k)
kvl.append(v)
self.m = mmap(-1, kvp)
for p in ht:
self.m.write(uint_format.pack(p))
for x in kvl:
if len(x) <= 0x7f:
self.m.write_byte(chr(len(x)))
else:
self.m.write(uint_format.pack(0x80000000 + len(x)))
self.m.write(x)
def hash(self, k):
h = hash(k)
h = (h + (h >> 3) + (h >> 13) + (h >> 23)) * 1749375391 & (self.hs - 1)
return h
def get(self, k, d=None):
h = self.hash(k)
while True:
x = uint_format.unpack(self.m[h * 4:h * 4 + 4])[0]
if x == 0xffffffff:
return d
self.m.seek(x)
if k == self.read_kv():
return self.read_kv()
h = (h + 1) & (self.hs - 1)
def read_kv(self):
sz = ord(self.m.read_byte())
if sz & 0x80:
sz = uint_format.unpack(chr(sz) + self.m.read(3))[0] - 0x80000000
return self.m.read(sz)
def kvlen(self, k):
return len(k) + (1 if len(k) <= 0x7f else 4)
def __contains__(self, k):
return self.get(k, None) is not None
def close(self):
self.m.close()
uint_format = struct.Struct('>I')
def uget(a, k, d=None):
return to_unicode(a.get(to_str(k), d))
def uin(a, k):
return to_str(k) in a
def to_unicode(s):
return s.decode('utf-8') if isinstance(s, str) else s
def to_str(s):
return s.encode('utf-8') if isinstance(s, unicode) else s
def mmap_test():
n = 1000000
d = shared_immutable_dict({str(i * 2): '1' for i in xrange(n)})
start_time = default_timer()
for i in xrange(n):
if bool(d.get(str(i))) != (i % 2 == 0):
raise Exception(i)
print 'mmap speed: %d gets per sec' % (n / (default_timer() - start_time))
def manager_test():
n = 100000
d = Manager().dict({str(i * 2): '1' for i in xrange(n)})
start_time = default_timer()
for i in xrange(n):
if bool(d.get(str(i))) != (i % 2 == 0):
raise Exception(i)
print 'manager speed: %d gets per sec' % (n / (default_timer() - start_time))
def shm_test():
n = 1000000
d = HashTable('tmp', n)
d.update({str(i * 2): '1' for i in xrange(n)})
start_time = default_timer()
for i in xrange(n):
if bool(d.get(str(i))) != (i % 2 == 0):
raise Exception(i)
print 'shm speed: %d gets per sec' % (n / (default_timer() - start_time))
if __name__ == '__main__':
mmap_test()
manager_test()
shm_test()
在我的笔记本电脑上,性能结果是:
mmap speed: 247288 gets per sec
manager speed: 33792 gets per sec
shm speed: 691332 gets per sec
简单用法示例:
ht = shared_immutable_dict({'a': '1', 'b': '2'})
print ht.get('a')
除了这里的@senderle之外,有些人可能还想知道如何使用的功能multiprocessing.Pool
。
令人高兴的是,实例中有一个.Pool()
方法可以manager
模拟所有熟悉的顶层API multiprocessing
。
from itertools import repeat
import multiprocessing as mp
import os
import pprint
def f(d: dict) -> None:
pid = os.getpid()
d[pid] = "Hi, I was written by process %d" % pid
if __name__ == '__main__':
with mp.Manager() as manager:
d = manager.dict()
with manager.Pool() as pool:
pool.map(f, repeat(d, 10))
# `d` is a DictProxy object that can be converted to dict
pprint.pprint(dict(d))
输出:
$ python3 mul.py
{22562: 'Hi, I was written by process 22562',
22563: 'Hi, I was written by process 22563',
22564: 'Hi, I was written by process 22564',
22565: 'Hi, I was written by process 22565',
22566: 'Hi, I was written by process 22566',
22567: 'Hi, I was written by process 22567',
22568: 'Hi, I was written by process 22568',
22569: 'Hi, I was written by process 22569',
22570: 'Hi, I was written by process 22570',
22571: 'Hi, I was written by process 22571'}
这是一个稍有不同的示例,其中每个进程仅将其进程ID记录到全局DictProxy
对象中d
。