Python 并发编程有很多方法，多线程的标准库 threading，concurrency，协程 asyncio，当然还有 grequests 这种异步库，每一个都可以实现上述需求，下面一一用代码实现一下，本文的代码可以直接运行，给你以后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，然后开启 200 个线程，每个线程都是不断的从队列中获取 url 并访问。
主线程读取文件中的 url 放入队列中，然后等待队列中所有的元素都被接收和处理完毕。代码如下：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
|
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)
|
运行结果如下：
有没有 get 到新技能？
线程池
如果你使用线程池，推荐使用更高级的 concurrent.futures 库：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
|
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
协程也是并发非常常用的工具了：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
|
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
这是个第三方库，目前有 3.8K 个星，就是 Requests + Gevent，让异步 http 请求变得更加简单。Gevent 的本质还是协程。
使用前：
|
1
|
pip install grequests
|
使用起来那是相当的简单：
|
1
2
3
4
5
6
7
8
|
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)
|
注意 grequests.map(rs) 是并发执行的。运行结果如下：
也可以加入异常处理：
|
1
2
3
4
5
6
7
8
9
10
|
>>> 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]>]
|
最后的话
今天分享了并发 http 请求的几种实现方式，有人说异步（协程）性能比多线程好，其实要分场景看的，没有一种方法适用所有的场景，笔者就曾做过一个实验，也是请求 url，当并发数量超过 500 时，协程明显变慢。
以上就是Python并发编程队列与多线程最快发送http请求方式的详细内容，更多关于Python并发编程队列与多线程的资料请关注服务器之家其它相关文章！
原文链接：https://blog.csdn.net/somenzz/article/details/120030634