Hello, 大家好，我是程序员小二！
如果有一个文件，外面有 10 万个 url，须要对每个 url 发送 http 申请，并打印申请后果的状态码，如何编写代码尽可能快的实现这些工作呢？
Python 并发编程有很多办法，多线程的规范库 threading，concurrency，协程 asyncio，当然还有 grequests 这种异步库，每一个都能够实现上述需要，上面一一用代码实现一下，本文的代码能够间接运行，给你当前的并发编程作为参考：
队列 + 多线程
定义一个大小为 400 的队列，而后开启 200 个线程，每个线程都是一直的从队列中获取 url 并拜访。
主线程读取文件中的 url 放入队列中，而后期待队列中所有的元素都被接管和处理完毕。代码如下：
from threading import Thread
import sys
from queue import Queue
import requests
concurrent = 200
def doWork():
while True:
url = q.get()
status, url = getStatus(url)
doSomethingWithResult(status, url)
q.task_done()
def getStatus(ourl):
try:
res = requests.get(ourl)
return res.status_code, ourl
except:
return "error", ourl
def doSomethingWithResult(status, url):
print(status, url)
q = Queue(concurrent * 2)
for i in range(concurrent):
t = Thread(target=doWork)
t.daemon = True
t.start()
try:
for url in open("urllist.txt"):
q.put(url.strip())
q.join()
except KeyboardInterrupt:
sys.exit(1)
运行后果如下：
有没有 get 到新技能？
线程池
如果你应用线程池，举荐应用更高级的 concurrent.futures 库：
import concurrent.futures
import requests
out = []
CONNECTIONS = 100
TIMEOUT = 5
urls = []
with open("urllist.txt") as reader:
for url in reader:
urls.append(url.strip())
def load_url(url, timeout):
ans = requests.get(url, timeout=timeout)
return ans.status_code
with concurrent.futures.ThreadPoolExecutor(max_workers=CONNECTIONS) as executor:
future_to_url = (executor.submit(load_url, url, TIMEOUT) for url in urls)
for future in concurrent.futures.as_completed(future_to_url):
try:
data = future.result()
except Exception as exc:
data = str(type(exc))
finally:
out.append(data)
print(data)
协程 + aiohttp
协程也是并发十分罕用的工具了，
import asyncio
from aiohttp import ClientSession, ClientConnectorError
async def fetch_html(url: str, session: ClientSession, **kwargs) -> tuple:
try:
resp = await session.request(method="GET", url=url, **kwargs)
except ClientConnectorError:
return (url, 404)
return (url, resp.status)
async def make_requests(urls: set, **kwargs) -> None:
async with ClientSession() as session:
tasks = []
for url in urls:
tasks.append(fetch_html(url=url, session=session, **kwargs)
)
results = await asyncio.gather(*tasks)
for result in results:
print(f'{result[1]} - {str(result[0])}')
if __name__ == "__main__":
import sys
assert sys.version_info >= (3, 7), "Script requires Python 3.7+."
with open("urllist.txt") as infile:
urls = set(map(str.strip, infile))
asyncio.run(make_requests(urls=urls))
grequests[1]
这是个第三方库，目前有 3.8K 个星，就是 Requests + Gevent[2]，让异步 http 申请变得更加简略。Gevent 的实质还是协程。
应用前：
pip install grequests
应用起来那是相当的简略：
import grequests
urls = []
with open("urllist.txt") as reader:
for url in reader:
urls.append(url.strip())
rs = (grequests.get(u) for u in urls)
for result in grequests.map(rs):
print(result.status_code, result.url)留神 grequests.map(rs) 是并发执行的。运行后果如下：
也能够退出异样解决：
>>> def exception_handler(request, exception):
... print("Request failed")
>>> reqs = [... grequests.get('http://httpbin.org/delay/1', timeout=0.001),
... grequests.get('http://fakedomain/'),
... grequests.get('http://httpbin.org/status/500')]
>>> grequests.map(reqs, exception_handler=exception_handler)
Request failed
Request failed
[None, None, <Response [500]>]最初的话
明天分享了并发 http 申请的几种实现形式，有人说异步（协程）性能比多线程好，其实要分场景看的，没有一种办法实用所有的场景，笔者就曾做过一个试验，也是申请 url，当并发数量超过 500 时，协程显著变慢。所以，不能说哪个肯定比哪个好，须要划分状况。