跨多处理python共享熊猫数据帧字典

我找到的最好的解决方案(它只适用于某些类型的问题)是使用Python的BaseManager和SyncManager类的客户机/服务器设置。要做到这一点,首先要设置一个服务器,为数据提供一个代理类。

数据服务器。py公司

#!/usr/bin/python
from    multiprocessing.managers import SyncManager
import  numpy

# Global for storing the data to be served
gData = {}

# Proxy class to be shared with different processes
# Don't put big data in here since that will force it to be piped to the
# other process when instantiated there, instead just return a portion of
# the global data when requested.
class DataProxy(object):
    def __init__(self):
        pass

    def getData(self, key, default=None):
        global gData
        return gData.get(key, None)

if __name__ == '__main__':
    port  = 5000

    print 'Simulate loading some data'
    for i in xrange(1000):
        gData[i] = numpy.random.rand(1000)

    # Start the server on address(host,port)
    print 'Serving data. Press <ctrl>-c to stop.'
    class myManager(SyncManager): pass
    myManager.register('DataProxy', DataProxy)
    mgr = myManager(address=('', port), authkey='DataProxy01')
    server = mgr.get_server()
    server.serve_forever()

运行上述操作一次并保持其运行。下面是用于访问数据的客户端类。

DataClient。py公司

from   multiprocessing.managers import BaseManager
import psutil   #3rd party module for process info (not strictly required)

# Grab the shared proxy class.  All methods in that class will be availble here
class DataClient(object):
    def __init__(self, port):
        assert self._checkForProcess('DataServer.py'), 'Must have DataServer running'
        class myManager(BaseManager): pass
        myManager.register('DataProxy')
        self.mgr = myManager(address=('localhost', port), authkey='DataProxy01')
        self.mgr.connect()
        self.proxy = self.mgr.DataProxy()

    # Verify the server is running (not required)
    @staticmethod
    def _checkForProcess(name):
        for proc in psutil.process_iter():
            if proc.name() == name:
                return True
        return False

下面是使用多处理尝试此操作的测试代码。

测试MP。py公司

#!/usr/bin/python
import time
import multiprocessing as mp
import numpy
from   DataClient import *    

# Confusing, but the "proxy" will be global to each subprocess, 
# it's not shared across all processes.
gProxy = None
gMode  = None
gDummy = None
def init(port, mode):
    global gProxy, gMode, gDummy
    gProxy  = DataClient(port).proxy
    gMode  = mode
    gDummy = numpy.random.rand(1000)  # Same as the dummy in the server
    #print 'Init proxy ', id(gProxy), 'in ', mp.current_process()

def worker(key):
    global gProxy, gMode, gDummy
    if 0 == gMode:   # get from proxy
        array = gProxy.getData(key)
    elif 1 == gMode: # bypass retrieve to test difference
        array = gDummy
    else: assert 0, 'unknown mode: %s' % gMode
    for i in range(1000):
        x = sum(array)
    return x    

if __name__ == '__main__':
    port   = 5000
    maxkey = 1000
    numpts = 100

    for mode in [1, 0]:
        for nprocs in [16, 1]:
            if 0==mode: print 'Using client/server and %d processes' % nprocs
            if 1==mode: print 'Using local data and %d processes' % nprocs                
            keys = [numpy.random.randint(0,maxkey) for k in xrange(numpts)]
            pool = mp.Pool(nprocs, initializer=init, initargs=(port,mode))
            start = time.time()
            ret_data = pool.map(worker, keys, chunksize=1)
            print '   took %4.3f seconds' % (time.time()-start)
            pool.close()

当我在我的机器上运行时,我得到。。。

Using local data and 16 processes
   took 0.695 seconds
Using local data and 1 processes
   took 5.849 seconds
Using client/server and 16 processes
   took 0.811 seconds
Using client/server and 1 processes
   took 5.956 seconds

在多处理系统中,这是否适用取决于获取数据的频率。每次传输都有一小部分开销。如果在 x=sum(array) 环在某个时候,您将花费更多的时间来获取数据,而不是处理数据。

除了多处理之外,我也喜欢这种模式,因为我只需在服务器程序中加载一次大数组数据,它会一直加载到我杀死服务器为止。这意味着我可以针对数据运行一组单独的脚本,它们执行得很快;无需等待数据加载。

虽然这里的方法有点类似于使用数据库,但它的优点是可以处理任何类型的python对象,而不仅仅是简单的字符串和int的DB表等。我发现,对于那些简单的类型,使用DB要快一些,但对我来说,它往往需要更多的编程工作,而且我的数据并不总是容易移植到数据库。