我正在使用多线程和随机代理抓取网页。我的家用 PC 可以很好地处理这个问题,无论需要多少进程(在当前代码中,我已将其设置为 100)。RAM使用量似乎达到了2.5GB左右。然而,当我在我的 CentOS VPS 上运行它时,我收到一个通用的"已杀"消息,程序终止。运行 100 个进程时,我非常非常快地得到 Kill 错误。我将其减少到更合理的 8,但仍然得到相同的错误,但经过更长的时间。根据一些研究,我假设"已杀死"错误与内存使用有关。如果没有多线程,则不会发生错误。
那么,我该怎么做才能优化我的代码,使其仍然快速运行,但不占用太多内存呢?我最好的选择是进一步减少进程数量吗?我可以在程序运行时从 Python 中监控我的内存使用情况吗?
编辑:我刚刚意识到我的VPS有256mb的RAM,而我的桌面上有24gb,这是我最初编写代码时没有考虑的事情。
#Request soup of url, using random proxy / user agent - try different combinations until valid results are returned
def getsoup(url):
attempts = 0
while True:
try:
proxy = random.choice(working_proxies)
headers = {'user-agent': random.choice(user_agents)}
proxy_dict = {'http': 'http://' + proxy}
r = requests.get(url, headers, proxies=proxy_dict, timeout=5)
soup = BeautifulSoup(r.text, "html5lib") #"html.parser"
totalpages = int(soup.find("div", class_="pagination").text.split(' of ',1)[1].split('n', 1)[0]) #Looks for totalpages to verify proper page load
currentpage = int(soup.find("div", class_="pagination").text.split('Page ',1)[1].split(' of', 1)[0])
if totalpages < 5000: #One particular proxy wasn't returning pagelimit=60 or offset requests properly ..
break
except Exception as e:
# print 'Error! Proxy: {}, Error msg: {}'.format(proxy,e)
attempts = attempts + 1
if attempts > 30:
print 'Too many attempts .. something is wrong!'
sys.exit()
return (soup, totalpages, currentpage)
#Return soup of page of ads, connecting via random proxy/user agent
def scrape_url(url):
soup, totalpages, currentpage = getsoup(url)
#Extract ads from page soup
###[A bunch of code to extract individual ads from the page..]
# print 'Success! Scraped page #{} of {} pages.'.format(currentpage, totalpages)
sys.stdout.flush()
return ads
def scrapeall():
global currentpage, totalpages, offset
url = "url"
_, totalpages, _ = getsoup(url + "0")
url_list = [url + str(60*i) for i in range(totalpages)]
# Make the pool of workers
pool = ThreadPool(100)
# Open the urls in their own threads and return the results
results = pool.map(scrape_url, url_list)
# Close the pool and wait for the work to finish
pool.close()
pool.join()
flatten_results = [item for sublist in results for item in sublist] #Flattens the list of lists returned by multithreading
return flatten_results
adscrape = scrapeall()
BeautifulSoup是纯Python库,在中端网站上它会消耗大量内存。如果这是一个选项,请尝试将其替换为 lxml,它更快且用 C 编写。但是,如果您的页面很大,它可能仍会耗尽内存。
正如评论中已经建议的那样,您可以使用队列。排队以存储响应。更好的版本是检索对磁盘的响应,将文件名存储在队列中,并在单独的进程中解析它们。为此,您可以使用多处理库。如果解析耗尽内存并被终止,则继续获取。这种模式称为fork and die,是使用过多内存的Python的常见解决方法。
然后,您还需要一种方法来查看哪些响应解析失败。