mnmkng
Joined 6 years ago
Joined 6 years ago
Data pollution is real. Also location specific results, personalized results, A/B testing, and my favorite, badly implemented websites are real as well.
When you encounter this, you can try scraping the data from different locations, with various tokens, cookies, referrers etc. and often you can find a pattern to make the data consistent. Websites hate scraping, but they hate showing wrong data to human users even more. So if you resemble a legit user, you’ll most likely get correct data. But of course, there are exceptions.
But I personally think it does some things a little easier, a little faster and little more conveniently than the other libraries and tools out there.
Although there’s one thing that the JS version of Crawlee has which unfortunately isn’t in Python yet, but it will be there soon. AFAIK it’s unique among all libraries. It’s automatically detecting whether a headless browser is needed or if HTTP will suffice and using the most performant option.
(Not my opinion. I visited a major global law firm’s seminar on this topic a month ago and this is what they said.)
We wanted to have as many features in the initial release as possible, because we have a local Python community conference coming up tomorrow and we wanted to have the library ready for that.
More docs will come soon. I promise. And thanks for the shout.
Headless browsers are useful when the servers are protected by anti-scraping software and you can't reverse engineer it, when the data you need is generated dynamically - not downloaded, but computed, or simply when you don't have the time to bother with understanding the website on a deeper level.
Usually it's a tradeoff between development costs and runtime costs. In our case, we always try plain HTTP first. If we can't find an obvious way to do it, we go with browsers and then get back to optimizing the scraper later, using plain HTTP or a combination of plain HTTP and browsers for some requests like logins, tokens or cookies.