Speed up web scraper

Question

I am scraping 23770 webpages with a pretty simple web scraper using scrapy. I am quite new to scrapy and even python, but managed to write a spider that does the jo

Answer 1

I work also on web scrapping, using optimized C#, and it ends up CPU bound, so I am switching to C.

Parsing HTML blows the CPU data cache, and pretty sure your CPU is not using SSE 4.2 at all, as you can only access this feature using C/C++.

If you do the math, you are quickly compute bound but not memory bound.

Answer 2

Looking at your code, I'd say most of that time is spent in network requests rather than processing the responses. All of the tips @alecxe provides in his answer apply, but I'd suggest the HTTPCACHE_ENABLED setting, since it caches the requests and avoids doing it a second time. It would help on following crawls and even offline development. See more info in the docs: http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.contrib.downloadermiddleware.httpcache

Answer 3

Here's a collection of things to try:

• use latest scrapy version (if not using already)
• check if non-standard middlewares are used
• try to increase CONCURRENT_REQUESTS_PER_DOMAIN, CONCURRENT_REQUESTS settings (docs)
• turn off logging LOG_ENABLED = False (docs)
• try yielding an item in a loop instead of collecting items into the items list and returning them
• use local cache DNS (see this thread)
• check if this site is using download threshold and limits your download speed (see this thread)
• log cpu and memory usage during the spider run - see if there are any problems there
• try run the same spider under scrapyd service
• see if grequests + lxml will perform better (ask if you need any help with implementing this solution)
• try running Scrapy on pypy, see Running Scrapy on PyPy

Hope that helps.

Answer 4

One workaround to speed up your scrapy is to config your start_urls appropriately.

For example, If our target data is in http://apps.webofknowledge.com/doc=1 where the doc number range from 1 to 1000, you can config your start_urls in followings:

 start_urls = [
    "http://apps.webofknowledge.com/doc=250",
    "http://apps.webofknowledge.com/doc=750",
]

In this way, requests will start from 250 to 251,249 and from 750 to 751,749 simultaneously, so you will get 4 times faster compared to start_urls = ["http://apps.webofknowledge.com/doc=1"].