6.6 KiB
Common Practices
This section documents common practices when using Scrapy. These are things that cover many topics and don't often fall into any other specific section.
Run Scrapy from a script
You can use the :ref:`API <topics-api>` to run Scrapy from a script, instead of the typical way of running Scrapy via scrapy crawl.
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Unknown interpreted text role "ref".Remember that Scrapy is built on top of the Twisted asynchronous networking library, so you need to run it inside the Twisted reactor.
Note that you will also have to shutdown the Twisted reactor yourself after the spider is finished. This can be achieved by adding callbacks to the deferred returned by the :meth:`CrawlerRunner.crawl <scrapy.crawler.CrawlerRunner.crawl>` method.
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Unknown interpreted text role "meth".What follows is a working example of how to do that, using the testspiders project as example.
from twisted.internet import reactor
from scrapy.crawler import CrawlerRunner
from scrapy.utils.project import get_project_settings
# If you aren't inside a Scrapy project, you could use an instance of the
# Settings class in scrapy.settings instead of the configuration returned
# by get_project_settings
runner = CrawlerRunner(get_project_settings())
# 'followall' is the name of one of the spiders of the project. If you
# aren't working in a Scrapy project, use the spider class as first
# argument instead of its name (or set the SPIDER_MODULES setting so Scrapy
# knows where to look at)
d = runner.crawl('followall', domain='scrapinghub.com')
d.addBoth(lambda _: reactor.stop())
reactor.run() # the script will block here until the crawling is finished
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.. seealso:: `Twisted Reactor Overview`_.
Running multiple spiders in the same process
By default, Scrapy runs a single spider per process when you run scrapy crawl. However, Scrapy supports running multiple spiders per process using the :ref:`internal API <topics-api>`.
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Unknown interpreted text role "ref".Here is an example that runs multiple spiders simultaneously, using the testspiders project:
from twisted.internet import reactor, defer
from scrapy.crawler import CrawlerRunner
from scrapy.utils.project import get_project_settings
runner = CrawlerRunner(get_project_settings())
dfs = set()
for domain in ['scrapinghub.com', 'insophia.com']:
d = runner.crawl('followall', domain=domain)
dfs.add(d)
defer.DeferredList(dfs).addBoth(lambda _: reactor.stop())
reactor.run() # the script will block here until all crawling jobs are finished
Same example but running the spiders sequentially by chaining the deferreds:
from twisted.internet import reactor, defer
from scrapy.crawler import CrawlerRunner
from scrapy.utils.project import get_project_settings
runner = CrawlerRunner(get_project_settings())
@defer.inlineCallbacks
def crawl():
for domain in ['scrapinghub.com', 'insophia.com']:
yield runner.crawl('followall', domain=domain)
reactor.stop()
crawl()
reactor.run() # the script will block here until the last crawl call is finished
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.. seealso:: :ref:`run-from-script`.
Distributed crawls
Scrapy doesn't provide any built-in facility for running crawls in a distribute (multi-server) manner. However, there are some ways to distribute crawls, which vary depending on how you plan to distribute them.
If you have many spiders, the obvious way to distribute the load is to setup many Scrapyd instances and distribute spider runs among those.
If you instead want to run a single (big) spider through many machines, what you usually do is partition the urls to crawl and send them to each separate spider. Here is a concrete example:
First, you prepare the list of urls to crawl and put them into separate files/urls:
http://somedomain.com/urls-to-crawl/spider1/part1.list http://somedomain.com/urls-to-crawl/spider1/part2.list http://somedomain.com/urls-to-crawl/spider1/part3.list
Then you fire a spider run on 3 different Scrapyd servers. The spider would receive a (spider) argument part with the number of the partition to crawl:
curl http://scrapy1.mycompany.com:6800/schedule.json -d project=myproject -d spider=spider1 -d part=1 curl http://scrapy2.mycompany.com:6800/schedule.json -d project=myproject -d spider=spider1 -d part=2 curl http://scrapy3.mycompany.com:6800/schedule.json -d project=myproject -d spider=spider1 -d part=3
Avoiding getting banned
Some websites implement certain measures to prevent bots from crawling them, with varying degrees of sophistication. Getting around those measures can be difficult and tricky, and may sometimes require special infrastructure. Please consider contacting commercial support if in doubt.
Here are some tips to keep in mind when dealing with these kind of sites:
rotate your user agent from a pool of well-known ones from browsers (google around to get a list of them)
disable cookies (see :setting:`COOKIES_ENABLED`) as some sites may use cookies to spot bot behaviour
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use download delays (2 or higher). See :setting:`DOWNLOAD_DELAY` setting.
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if possible, use Google cache to fetch pages, instead of hitting the sites directly
use a pool of rotating IPs. For example, the free Tor project or paid services like ProxyMesh
use a highly distributed downloader that circumvents bans internally, so you can just focus on parsing clean pages. One example of such downloaders is Crawlera
If you are still unable to prevent your bot getting banned, consider contacting commercial support.
Dynamic Creation of Item Classes
For applications in which the structure of item class is to be determined by user input, or other changing conditions, you can dynamically create item classes instead of manually coding them.
from scrapy.item import DictItem, Field
def create_item_class(class_name, field_list):
field_dict = {}
for field_name in field_list:
field_dict[field_name] = Field()
return type(class_name, (DictItem,), field_dict)