Python 2.6: 使用多进程时的进程本地存储

Question

我试图构建一个python脚本，它有一个工作进程池（使用多处理。池)在一大组数据中。在

我希望每个进程都有一个唯一的对象，该对象可以在该进程的多次执行中使用。在

Psudo代码：

def work(data):
    #connection should be unique per process
    connection.put(data)
    print 'work done with connection:', connection

if __name__ == '__main__':
    pPool = Pool() # pool of 4 processes
    datas = [1..1000]
    for process in pPool:
        #this is the part i'm asking about // how do I really do this?
        process.connection = Connection(conargs)
    for data in datas:
       pPool.apply_async(work, (data))

Answer 1

进程本地存储作为一个映射容器很容易实现，对于其他从Google这里寻找类似内容的人来说（注意这是Py3，但是很容易转换为2的语法（只需从object继承）：

class ProcessLocal:
    """
    Provides a basic per-process mapping container that wipes itself if the current PID changed since the last get/set.
    Aka `threading.local()`, but for processes instead of threads.
    """

    __pid__ = -1

    def __init__(self, mapping_factory=dict):
        self.__mapping_factory = mapping_factory

    def __handle_pid(self):
        new_pid = os.getpid()
        if self.__pid__ != new_pid:
            self.__pid__, self.__store = new_pid, self.__mapping_factory()

    def __delitem__(self, key):
        self.__handle_pid()
        return self.__store.__delitem__(key)

    def __getitem__(self, key):
        self.__handle_pid()
        return self.__store.__getitem__(key)

    def __setitem__(self, key, val):
        self.__handle_pid()
        return self.__store.__setitem__(key)

查看更多@https://github.com/akatrevorjay/pytutils/blob/develop/pytutils/mappings.py

Answer 2

直接创建mp.Processes可能是最简单的方法（没有mp.Pool）：

import multiprocessing as mp
import time

class Connection(object):
    def __init__(self,name):
        self.name=name
    def __str__(self):
        return self.name

def work(inqueue,conn):
    name=mp.current_process().name
    while 1:
        data=inqueue.get()
        time.sleep(.5)
        print('{n}: work done with connection {c} on data {d}'.format(
            n=name,c=conn,d=data))
        inqueue.task_done()

if __name__ == '__main__':
    N=4
    procs=[]
    inqueue=mp.JoinableQueue()
    for i in range(N):
        conn=Connection(name='Conn-'+str(i))
        proc=mp.Process(target=work,name='Proc-'+str(i),args=(inqueue,conn))
        proc.daemon=True
        proc.start()

    datas = range(1,11)
    for data in datas:
        inqueue.put(data)
    inqueue.join()

收益率

^{pr2}$

请注意，Proc号每次都对应相同的Conn号。在

Answer 3

我觉得这样的东西应该行得通（没有经过测试）

def init(*args):
    global connection
    connection = Connection(*args)
pPool = Pool(initializer=init, initargs=conargs)