Hey everyone, I’m working on a project where I need to extract text from paragraphs on about 2500 different web pages. I’ve got all the URLs in a list, but I’m not sure what’s the most efficient way to handle this many links.
Here’s what I’m thinking of doing:
url_list = ['http://example.com/page1', 'http://example.com/page2', ...]
for url in url_list:
page = requests.get(url).text
parsed = BeautifulSoup(page, 'html.parser')
# extract data here
Is this a good way to go about it? Or are there better methods for handling such a large number of pages? I’m worried about performance and don’t want to overload the target website.
Any tips or suggestions would be really helpful. Thanks!
yo jack, have u tried using a proxy rotation? it can help avoid ip bans. also, consider using multiprocessing to speed things up. just remember to be cool and respect the site’s robots.txt. good luck with ur project dude!
Hey Jack27! Your project sounds really interesting. Have you thought about using a distributed system for this? I’ve heard that tools like Scrapy can handle large-scale scraping pretty well.
What kind of data are you looking to extract from these pages? I’m curious about the end goal of your project. Maybe there’s a way to optimize the scraping based on the specific info you need?
Also, have you checked if the site has an API? Sometimes that can be a faster and more reliable way to get data, if it’s available.
One thing to keep in mind - websites can change their structure over time. How are you planning to handle that? It might be worth considering some error handling or logging to catch any pages that don’t match your expected format.
Good luck with your project! Let us know how it goes - I’d love to hear about what you learn along the way.
Your approach is a solid starting point but there are several improvements worth considering. In my experience, implementing asynchronous requests using libraries like aiohttp can significantly reduce the waiting time when processing thousands of pages. It is also important to incorporate rate limiting to prevent overwhelming the server. Using a session object helps maintain persistent connections and can improve performance. Additionally, handling errors and implementing retries for failed requests is essential for robust scraping. If the pages load content dynamically, a headless browser might be necessary. Always verify that your scraping practices abide by the target website’s policies.
Have you thought about using a distributed system for this? I’ve heard that tools like Scrapy can handle large-scale scraping pretty well.