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
自从2010年这篇文章发布以来,事情发生了很大的变化,我还没有尝试过所有其他的答案,但我尝试了一些,我发现使用python3.6对我来说这是最好的。
在AWS上运行时,我每秒可以获取大约150个独特的域名。
import concurrent.futures
import requests
import time
out = []
CONNECTIONS = 100
TIMEOUT = 5
tlds = open('../data/sample_1k.txt').read().splitlines()
urls = ['http://{}'.format(x) for x in tlds[1:]]
def load_url(url, timeout):
ans = requests.head(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)
time1 = time.time()
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(str(len(out)),end="\r")
time2 = time.time()
print(f'Took {time2-time1:.2f} s')
其他回答
自从2010年这篇文章发布以来,事情发生了很大的变化,我还没有尝试过所有其他的答案,但我尝试了一些,我发现使用python3.6对我来说这是最好的。
在AWS上运行时,我每秒可以获取大约150个独特的域名。
import concurrent.futures
import requests
import time
out = []
CONNECTIONS = 100
TIMEOUT = 5
tlds = open('../data/sample_1k.txt').read().splitlines()
urls = ['http://{}'.format(x) for x in tlds[1:]]
def load_url(url, timeout):
ans = requests.head(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)
time1 = time.time()
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(str(len(out)),end="\r")
time2 = time.time()
print(f'Took {time2-time1:.2f} s')
如果您希望获得尽可能好的性能,您可能会考虑使用异步I/O而不是线程。与成千上万个操作系统线程相关的开销是不小的,Python解释器内的上下文切换甚至增加了更多的开销。线程当然可以完成工作,但我怀疑异步路由将提供更好的整体性能。
具体来说,我建议使用Twisted库中的异步web客户端(http://www.twistedmatrix.com)。它有一个公认的陡峭的学习曲线,但一旦你很好地掌握了Twisted的异步编程风格,它就很容易使用。
Twisted的异步web客户端API的HowTo可以在以下地址找到:
http://twistedmatrix.com/documents/current/web/howto/client.html
Twistedless解决方案:
from urlparse import urlparse
from threading import Thread
import httplib, sys
from Queue import Queue
concurrent = 200
def doWork():
while True:
url = q.get()
status, url = getStatus(url)
doSomethingWithResult(status, url)
q.task_done()
def getStatus(ourl):
try:
url = urlparse(ourl)
conn = httplib.HTTPConnection(url.netloc)
conn.request("HEAD", url.path)
res = conn.getresponse()
return res.status, 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)
这个方案比twisted方案稍微快一点,并且使用更少的CPU。
(下一个项目的自我提示)
Python 3解决方案只使用请求。它是最简单且快速的,不需要多处理或复杂的异步库。
最重要的方面是重用连接,特别是对于HTTPS (TLS需要额外的往返才能打开)。注意,连接是特定于子域的。如果在多个域上抓取多个页面,则可以对url列表进行排序,以最大化连接重用(它有效地按域进行排序)。
当给定足够的线程时,它将与任何异步代码一样快。(请求在等待响应时释放python GIL)。
[带有日志记录和错误处理的生产等级代码]
import logging
import requests
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
# source: https://stackoverflow.com/a/68583332/5994461
THREAD_POOL = 16
# This is how to create a reusable connection pool with python requests.
session = requests.Session()
session.mount(
'https://',
requests.adapters.HTTPAdapter(pool_maxsize=THREAD_POOL,
max_retries=3,
pool_block=True)
)
def get(url):
response = session.get(url)
logging.info("request was completed in %s seconds [%s]", response.elapsed.total_seconds(), response.url)
if response.status_code != 200:
logging.error("request failed, error code %s [%s]", response.status_code, response.url)
if 500 <= response.status_code < 600:
# server is overloaded? give it a break
time.sleep(5)
return response
def download(urls):
with ThreadPoolExecutor(max_workers=THREAD_POOL) as executor:
# wrap in a list() to wait for all requests to complete
for response in list(executor.map(get, urls)):
if response.status_code == 200:
print(response.content)
def main():
logging.basicConfig(
format='%(asctime)s.%(msecs)03d %(levelname)-8s %(message)s',
level=logging.INFO,
datefmt='%Y-%m-%d %H:%M:%S'
)
urls = [
"https://httpstat.us/200",
"https://httpstat.us/200",
"https://httpstat.us/200",
"https://httpstat.us/404",
"https://httpstat.us/503"
]
download(urls)
if __name__ == "__main__":
main()
(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)
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