I am opening a file which has 100,000 URL's. I need to send an HTTP request to each URL and print the status code. I am using Python 2.6, and so far looked at the many confusing ways Python implements threading/concurrency. I have even looked at the python concurrence library, but cannot figure out how to write this program correctly. Has anyone come across a similar problem? I guess generally I need to know how to perform thousands of tasks in Python as fast as possible - I suppose that means 'concurrently'.
当前回答
下面是一个“异步”解决方案,它不使用asyncio,而是使用asyncio使用的低级机制(在Linux上):select()。(或者asyncio可能使用poll或epoll,但这是类似的原理。)
它是对PyCurl示例的稍微修改版本。
(为了简单起见,它多次请求相同的URL,但您可以轻松地修改它以检索一系列不同的URL。)
(另一个轻微的修改可以使这个检索相同的URL作为一个无限循环。提示:将while url和句柄更改为while句柄,将while nprocessed<nurls更改为while 1。)
import pycurl,io,gzip,signal, time, random
signal.signal(signal.SIGPIPE, signal.SIG_IGN) # NOTE! We should ignore SIGPIPE when using pycurl.NOSIGNAL - see the libcurl tutorial for more info
NCONNS = 2 # Number of concurrent GET requests
url = 'example.com'
urls = [url for i in range(0x7*NCONNS)] # Copy the same URL over and over
# Check args
nurls = len(urls)
NCONNS = min(NCONNS, nurls)
print("\x1b[32m%s \x1b[0m(compiled against 0x%x)" % (pycurl.version, pycurl.COMPILE_LIBCURL_VERSION_NUM))
print(f'\x1b[37m{nurls} \x1b[91m@ \x1b[92m{NCONNS}\x1b[0m')
# Pre-allocate a list of curl objects
m = pycurl.CurlMulti()
m.handles = []
for i in range(NCONNS):
c = pycurl.Curl()
c.setopt(pycurl.FOLLOWLOCATION, 1)
c.setopt(pycurl.MAXREDIRS, 5)
c.setopt(pycurl.CONNECTTIMEOUT, 30)
c.setopt(pycurl.TIMEOUT, 300)
c.setopt(pycurl.NOSIGNAL, 1)
m.handles.append(c)
handles = m.handles # MUST make a copy?!
nprocessed = 0
while nprocessed<nurls:
while urls and handles: # If there is an url to process and a free curl object, add to multi stack
url = urls.pop(0)
c = handles.pop()
c.buf = io.BytesIO()
c.url = url # store some info
c.t0 = time.perf_counter()
c.setopt(pycurl.URL, c.url)
c.setopt(pycurl.WRITEDATA, c.buf)
c.setopt(pycurl.HTTPHEADER, [f'user-agent: {random.randint(0,(1<<256)-1):x}', 'accept-encoding: gzip, deflate', 'connection: keep-alive', 'keep-alive: timeout=10, max=1000'])
m.add_handle(c)
while 1: # Run the internal curl state machine for the multi stack
ret, num_handles = m.perform()
if ret!=pycurl.E_CALL_MULTI_PERFORM: break
while 1: # Check for curl objects which have terminated, and add them to the handles
nq, ok_list, ko_list = m.info_read()
for c in ok_list:
m.remove_handle(c)
t1 = time.perf_counter()
reply = gzip.decompress(c.buf.getvalue())
print(f'\x1b[33mGET \x1b[32m{t1-c.t0:.3f} \x1b[37m{len(reply):9,} \x1b[0m{reply[:32]}...') # \x1b[35m{psutil.Process(os.getpid()).memory_info().rss:,} \x1b[0mbytes')
handles.append(c)
for c, errno, errmsg in ko_list:
m.remove_handle(c)
print('\x1b[31mFAIL {c.url} {errno} {errmsg}')
handles.append(c)
nprocessed = nprocessed + len(ok_list) + len(ko_list)
if nq==0: break
m.select(1.0) # Currently no more I/O is pending, could do something in the meantime (display a progress bar, etc.). We just call select() to sleep until some more data is available.
for c in m.handles:
c.close()
m.close()
其他回答
考虑使用风车,虽然风车可能不能做那么多线程。
您可以在5台机器上使用手卷Python脚本,每台机器使用端口40000-60000连接出站,打开100,000个端口连接。
另外,使用一个线程良好的QA应用程序(如OpenSTA)做一个示例测试可能会有所帮助,以了解每个服务器可以处理多少。
另外,试着在LWP::ConnCache类中使用简单的Perl。这样您可能会获得更好的性能(更多的连接)。
(工具)
Apache Bench是您所需要的全部。—用于测量HTTP web服务器性能的命令行计算机程序
给你一篇不错的博客文章:https://www.petefreitag.com/item/689.cfm(来自Pete Freitag)
我发现使用tornado包是最快和最简单的方法来实现这一点:
from tornado import ioloop, httpclient, gen
def main(urls):
"""
Asynchronously download the HTML contents of a list of URLs.
:param urls: A list of URLs to download.
:return: List of response objects, one for each URL.
"""
@gen.coroutine
def fetch_and_handle():
httpclient.AsyncHTTPClient.configure(None, defaults=dict(user_agent='MyUserAgent'))
http_client = httpclient.AsyncHTTPClient()
waiter = gen.WaitIterator(*[http_client.fetch(url, raise_error=False, method='HEAD')
for url in urls])
results = []
# Wait for the jobs to complete
while not waiter.done():
try:
response = yield waiter.next()
except httpclient.HTTPError as e:
print(f'Non-200 HTTP response returned: {e}')
continue
except Exception as e:
print(f'An unexpected error occurred querying: {e}')
continue
else:
print(f'URL \'{response.request.url}\' has status code <{response.code}>')
results.append(response)
return results
loop = ioloop.IOLoop.current()
web_pages = loop.run_sync(fetch_and_handle)
return web_pages
my_urls = ['url1.com', 'url2.com', 'url100000.com']
responses = main(my_urls)
print(responses[0])
最简单的方法是使用Python的内置线程库。它们不是“真正的”/内核线程。它们有问题(比如序列化),但足够好了。你需要一个队列和线程池。这里有一个选项,但是编写自己的选项很简单。您无法并行处理所有100,000个调用,但可以同时发出100个(或左右)调用。
我知道这是一个老问题,但在Python 3.7中,您可以使用asyncio和aiohttp来做到这一点。
import asyncio
import aiohttp
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 pathlib
import sys
assert sys.version_info >= (3, 7), "Script requires Python 3.7+."
here = pathlib.Path(__file__).parent
with open(here.joinpath("urls.txt")) as infile:
urls = set(map(str.strip, infile))
asyncio.run(make_requests(urls=urls))
你可以阅读更多关于它的内容,并在这里看到一个例子。
推荐文章
- 证书验证失败:无法获得本地颁发者证书
- 当使用pip3安装包时,“Python中的ssl模块不可用”
- 无法切换Python与pyenv
- Python if not == vs if !=
- 如何从scikit-learn决策树中提取决策规则?
- 为什么在Mac OS X v10.9 (Mavericks)的终端中apt-get功能不起作用?
- 将旋转的xtick标签与各自的xtick对齐
- 为什么元组可以包含可变项?
- 如何合并字典的字典?
- 如何创建类属性?
- 不区分大小写的“in”
- 在Python中获取迭代器中的元素个数
- 解析日期字符串并更改格式
- 使用try和。Python中的if
- 如何在Python中获得所有直接子目录