Python, multiprocessing: 如何优化代码?让代码更快?

Python, multiprocessing: How to optimize the code? Make the code faster?

我用Python。我有 100 个 zip 文件。每个 zip 文件包含 100 多个 xml 文件。我使用 xmlfiles 创建 csvfiles。

from xml.etree.ElementTree import fromstring
import zipfile
from multiprocessing import Process

def parse_xml_for_csv1(data, writer1):
    root = fromstring(data)
    for node in root.iter('name'):
        writer1.writerow(node.get('value'))

def create_csv1():
    with open('output1.csv', 'w') as f1:
        writer1 = csv.writer(f1)

        for i in range(1, 100):
            z = zipfile.ZipFile('xml' + str(i) + '.zip')
            # z.namelist() contains more than 100 xml files
            for finfo in z.namelist():
                data = z.read(finfo) 
                parse_xml_for_csv1(data, writer1)


def create_csv2():
    with open('output2.csv', 'w') as f2:
        writer2 = csv.writer(f2)

        for i in range(1, 100): 
            ...


if __name__ == "__main__":
    p1 = Process(target=create_csv1)
    p2 = Process(target=create_csv2)
    p1.start()
    p2.start()
    p1.join()
    p2.join()

请告诉我,如何优化我的代码?让代码更快?

你只需要定义一个方法,带参数。 在给定数量的线程或进程中拆分 100 个 .zip 文件的处理。添加的进程越多,使用的 CPU 越多,也许您可​​以使用 2 个以上的进程,速度会更快(有时可能会因为磁盘 I/O 而出现瓶颈点)

在下面的代码中,我可以改成4个或10个进程,不需要copy/paste代码。它处理不同的 zip 文件。

您的代码并行处理相同的 100 个文件两次:比没有多处理时还要慢!

def create_csv(start_index,step):
    with open('output{0}.csv'.format(start_index//step), 'w') as f1:
        writer1 = csv.writer(f1)

        for i in range(start_index, start_index+step):
            z = zipfile.ZipFile('xml' + str(i) + '.zip')
            # z.namelist() contains more than 100 xml files
            for finfo in z.namelist():
                data = z.read(finfo)
                parse_xml_for_csv1(data, writer1)



if __name__ == "__main__":
    nb_files = 100
    nb_processes = 2   # raise to 4 or 8 depending on your machine

    step = nb_files//nb_processes
    lp = []
    for start_index in range(1,nb_files,step):
        p = Process(target=create_csv,args=[start_index,step])
        p.start()
        lp.append(p)
    for p in lp:
        p.join()