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 job. It is, however, really slow (it takes approx. 28 hours to crawl the 23770 pages).

I have looked on the scrapy webpage and the mailing lists and stackoverflow, but I can't seem to find generic recommendations for writing fast crawlers understandable for beginners. Maybe my problem is not the spider itself, but the way i run it. All suggestions welcome!

I have listed my code below, if it's needed.

from scrapy.spider import BaseSpider
from scrapy.selector import HtmlXPathSelector
from scrapy.item import Item, Field
import re

class Sale(Item):
    Adresse = Field()
    Pris = Field()
    Salgsdato = Field()
    SalgsType = Field()
    KvmPris = Field()
    Rum = Field()
    Postnummer = Field()
    Boligtype = Field()
    Kvm = Field()
    Bygget = Field()

class HouseSpider(BaseSpider):
    name = 'House'
    allowed_domains = ["http://boliga.dk/"]
    start_urls = ['http://www.boliga.dk/salg/resultater?so=1&type=Villa&type=Ejerlejlighed&type=R%%C3%%A6kkehus&kom=&amt=&fraPostnr=&tilPostnr=&iPostnr=&gade=&min=&max=&byggetMin=&byggetMax=&minRooms=&maxRooms=&minSize=&maxSize=&minsaledate=1992&maxsaledate=today&kode=&p=%d' %n for n in xrange(1, 23770, 1)]

    def parse(self, response):
        hxs = HtmlXPathSelector(response)
        sites = hxs.select("id('searchresult')/tr")
        items = []      
        for site in sites:
            item = Sale()
            item['Adresse'] = site.select("td[1]/a[1]/text()").extract()
            item['Pris'] = site.select("td[2]/text()").extract()
            item['Salgsdato'] = site.select("td[3]/text()").extract()
            Temp = site.select("td[4]/text()").extract()
            Temp = Temp[0]
            m = re.search('\r\n\t\t\t\t\t(.+?)\r\n\t\t\t\t', Temp)
            if m:
                found = m.group(1)
                item['SalgsType'] = found
            else:
                item['SalgsType'] = Temp
            item['KvmPris'] = site.select("td[5]/text()").extract()
            item['Rum'] = site.select("td[6]/text()").extract()
            item['Postnummer'] = site.select("td[7]/text()").extract()
            item['Boligtype'] = site.select("td[8]/text()").extract()
            item['Kvm'] = site.select("td[9]/text()").extract()
            item['Bygget'] = site.select("td[10]/text()").extract()
            items.append(item)
        return items

Thanks!

Answer 1

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 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

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 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"].