python 多线程 多进程

node2:/root/python/20200525#cat t600.py 

import requests
import time

def download_one(url):
    resp = requests.get(url)
    print('Read {} from {}'.format(len(resp.content), url))
    
def download_all(sites):
    for site in sites:
        download_one(site)

def main():
    sites = [
    'http://192.168.137.3:9000/test111/',
    'http://192.168.137.3:9000/test222/',
    'http://192.168.137.3:9000/test333/'
    ]
    start_time = time.perf_counter()
    download_all(sites)
    end_time = time.perf_counter()
    print('Download {} sites in {} seconds'.format(len(sites), end_time - start_time))
    
if __name__ == '__main__':
    main()
	
	
node2:/root/python/20200525#time python3 t600.py 
Read 75453 from http://192.168.137.3:9000/test111/
Read 66983 from http://192.168.137.3:9000/test222/
Read 66496 from http://192.168.137.3:9000/test333/
Download 3 sites in 18.260805818950757 seconds

real	0m18.432s
user	0m0.161s
sys	0m0.023s
node2:/root/python/20200525#


接着我们再来看，多线程版本的代码实现：

node2:/root/python/20200525#cat t700.py 

import concurrent.futures
import requests
import threading
import time

def download_one(url):
    resp = requests.get(url)
    print('Read {} from {}'.format(len(resp.content), url))


def download_all(sites):
    with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
        executor.map(download_one, sites)

def main():
    sites = [
    'http://192.168.137.3:9000/test111/',
    'http://192.168.137.3:9000/test222/',
    'http://192.168.137.3:9000/test333/'
    ]
    start_time = time.perf_counter()
    download_all(sites)
    end_time = time.perf_counter()
    print('Download {} sites in {} seconds'.format(len(sites), end_time - start_time))

if __name__ == '__main__':
    main()

node2:/root/python/20200525#time python3 t700.py 
Read 75915 from http://192.168.137.3:9000/test111/
Read 66983 from http://192.168.137.3:9000/test222/
Read 66468 from http://192.168.137.3:9000/test333/
Download 3 sites in 7.2133595428895205 seconds

real	0m7.385s
user	0m0.151s
sys	0m0.146s

我们具体来看这段代码，它是多线程版本和单线程版的主要区别所在：


   with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
        executor.map(download_one, sites)
这里我们创建了一个线程池，总共有 5 个线程可以分配使用。executer.map() 与前面所讲的 Python 内置的 map() 函数类似，表示对 sites 中的每一个元素，

并发地调用函数 download_one()。



多进程:


with futures.ThreadPoolExecutor(workers) as executor
=>
with futures.ProcessPoolExecutor() as executor: 



node2:/root/python/20200525#cat t800.py 
import concurrent.futures
import requests
import threading
import time

def download_one(url):
    resp = requests.get(url)
    print('Read {} from {}'.format(len(resp.content), url))


def download_all(sites):
    with concurrent.futures.ProcessPoolExecutor() as executor:
        executor.map(download_one, sites)

def main():
    sites = [
    'http://192.168.137.3:9000/test111/',
    'http://192.168.137.3:9000/test222/',
    'http://192.168.137.3:9000/test333/'
    ]
    start_time = time.perf_counter()
    download_all(sites)
    end_time = time.perf_counter()
    print('Download {} sites in {} seconds'.format(len(sites), end_time - start_time))

if __name__ == '__main__':
    main()