I'd like to do perform data mining on a large scale. For this, I need a fast crawler. All I need is something to download a web page, extract links and follow them recursively, but without visiting the same url twice. Basically, I want to avoid looping.
I already wrote a crawler in python, but it's too slow. I'm not able to saturate a 100Mbit line with it. Top speed is ~40 urls/sec. and for some reason it's hard to get better results. It seems like a problem with python's multithreading/sockets. I also ran into problems with python's gargabe collector, but that was solvable. CPU isn't the bottleneck btw.
So, what should I use to write a crawler that is as fast as possible, and what's the best solution to avoid looping while crawling?
EDIT:
The solution was to combine multiprocessing
and threading
modules. Spawn multiple processes with multiple threads per process for best effect. Spawning multiple threads in a single process is not effective and multiple processes with just one thread consume too much memory.
Why not use something already tested for crawling, like Scrapy? I managed to reach almost 100 pages per second on a low-end VPS that has limited RAM memory (about 400Mb), while network speed was around 6-7 Mb/s (i.e. below 100Mbps).
Another improvement you can do is use urllib3
(especially when crawling many pages from a single domain). Here's a brief comparison I did some time ago:
Scrapy now uses the Requests library, which in turn uses urllib3. That makes Scrapy the absolute go-to tool when it comes to scraping. Recent versions also support deploying projects, so scraping from a VPS is easier than ever.