在多处理python中共享pandas数据帧的字典

Question

我有一个python pandas数据帧的字典.这本词典的总大小约为2GB.但是,当我在16个多处理中共享它时(在子进程中我只读取dict的数据而不修改它),它需要32GB ram.所以我想问一下,如果我可以在不进行复制的情况下跨多处理共享这个字典.我试图将它转换为manager.dict().但似乎需要太长时间.实现这一目标的最标准方法是什么？谢谢.

Answer 1

我发现的最佳解决方案（它仅适用于某些类型的问题）是使用 Python 的 BaseManager 和 SyncManager 类使用客户端/服务器设置。为此，您首先设置一个服务器，该服务器为数据提供代理类。

数据服务器.py

#!/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()

运行上面一次，让它继续运行。下面是您用来访问数据的客户端类。

数据客户端

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

以下是使用多处理尝试此操作的测试代码。

测试文件

#!/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它时，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

这在您的多处理系统中是否对您有用取决于抓取数据的频率。每次传输都会产生少量开销。如果您调低循环中的迭代次数，您可以看到这一点x=sum(array)。在某些时候，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，我也喜欢这种模式，因为我只需要在服务器程序中加载一次我的大数组数据，它会一直加载直到我杀死服务器。这意味着我可以针对数据运行一堆单独的脚本，并且它们可以快速执行；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，而不仅仅是字符串和整数的简单 DB 表等。我发现使用 DB 对那些简单的类型，但对我来说，它往往更多地以编程方式工作，而且我的数据并不总是很容易移植到数据库中。