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 API to run Scrapy from a script, instead of the typical way of running Scrapy via scrapy crawl.

Remember that Scrapy requires a Twisted reactor or (with TWISTED_REACTOR_ENABLED set to False) an asyncio event loop, so you need to run one of those in your script for it to work (helpers described below can do it for you).

The first utility you can use to run your spiders is scrapy.crawler.AsyncCrawlerProcess or scrapy.crawler.CrawlerProcess. These classes will start a Twisted reactor for you, configuring the logging and setting shutdown handlers. These classes are the ones used by all Scrapy commands. They have similar functionality, differing in their asynchronous API style: AsyncCrawlerProcess returns coroutines from its asynchronous methods while CrawlerProcess returns Deferred objects.

Here’s an example showing how to run a single spider with it.

import scrapy
from scrapy.crawler import AsyncCrawlerProcess


class MySpider(scrapy.Spider):
    # Your spider definition
    ...


process = AsyncCrawlerProcess(
    settings={
        "FEEDS": {
            "items.json": {"format": "json"},
        },
    }
)

process.crawl(MySpider)
process.start()  # the script will block here until the crawling is finished

You can define settings within the dictionary passed to AsyncCrawlerProcess. Make sure to check the AsyncCrawlerProcess documentation to get acquainted with its usage details.

If you are inside a Scrapy project there are some additional helpers you can use to import those components within the project. You can automatically import your spiders passing their name to AsyncCrawlerProcess, and use scrapy.utils.project.get_project_settings() to get a Settings instance with your project settings.

What follows is a working example of how to do that, using the testspiders project as example.

from scrapy.crawler import AsyncCrawlerProcess
from scrapy.utils.project import get_project_settings

process = AsyncCrawlerProcess(get_project_settings())

# 'followall' is the name of one of the spiders of the project.
process.crawl("followall", domain="scrapy.org")
process.start()  # the script will block here until the crawling is finished

There’s another Scrapy utility that provides more control over the crawling process: scrapy.crawler.AsyncCrawlerRunner or scrapy.crawler.CrawlerRunner. These classes are thin wrappers that encapsulate some simple helpers to run multiple crawlers, but they won’t start or interfere with existing reactors in any way. Just like scrapy.crawler.AsyncCrawlerProcess and scrapy.crawler.CrawlerProcess they differ in their asynchronous API style.

When using these classes the reactor should be explicitly run after scheduling your spiders. It’s recommended that you use AsyncCrawlerRunner or CrawlerRunner instead of AsyncCrawlerProcess or CrawlerProcess if your application is already using Twisted and you want to run Scrapy in the same reactor.

If you want to stop the reactor or run any other code right after the spider finishes you can do that after the task returned from AsyncCrawlerRunner.crawl() completes (or the Deferred returned from CrawlerRunner.crawl() fires). In the simplest case you can also use twisted.internet.task.react() to start and stop the reactor, though it may be easier to just use AsyncCrawlerProcess or CrawlerProcess instead.

Here’s an example of using AsyncCrawlerRunner together with simple reactor management code:

import scrapy
from scrapy.crawler import AsyncCrawlerRunner
from scrapy.utils.defer import deferred_f_from_coro_f
from scrapy.utils.log import configure_logging
from scrapy.utils.reactor import install_reactor
from twisted.internet.task import react


class MySpider(scrapy.Spider):
    # Your spider definition
    ...


async def crawl(_):
    configure_logging({"LOG_FORMAT": "%(levelname)s: %(message)s"})
    runner = AsyncCrawlerRunner()
    await runner.crawl(MySpider)  # completes when the spider finishes


install_reactor("twisted.internet.asyncioreactor.AsyncioSelectorReactor")
react(deferred_f_from_coro_f(crawl))

Same example but using CrawlerRunner and a different reactor (AsyncCrawlerRunner only works with AsyncioSelectorReactor):

import scrapy
from scrapy.crawler import CrawlerRunner
from scrapy.utils.log import configure_logging
from scrapy.utils.reactor import install_reactor
from twisted.internet.task import react


class MySpider(scrapy.Spider):
    custom_settings = {
        "TWISTED_REACTOR": "twisted.internet.epollreactor.EPollReactor",
    }
    # Your spider definition
    ...


def crawl(_):
    configure_logging({"LOG_FORMAT": "%(levelname)s: %(message)s"})
    runner = CrawlerRunner()
    d = runner.crawl(MySpider)
    return d  # this Deferred fires when the spider finishes


install_reactor("twisted.internet.epollreactor.EPollReactor")
react(crawl)

Running spiders inside existing applications

You may want to run Scrapy spiders inside an existing application. In simple cases (e.g. task queues that spawn a process for every task, or applications that can execute tasks synchronously in the same process) you can use the same approach as for standalone scripts (see Run Scrapy from a script). More complex cases, e.g. asynchronous web applications, have additional caveats and limitations.

If the application runs its own Twisted reactor, you can use AsyncCrawlerRunner or CrawlerRunner to run spiders using this reactor, see Run Scrapy from a script for examples.

If the application doesn’t run a Twisted reactor or an asyncio event loop (for example, a Django web app deployed with a WSGI server such as uWSGI), you can use AsyncCrawlerProcess with TWISTED_REACTOR_ENABLED set to False, so that Scrapy starts and stops an asyncio event loop for every spider run:

import scrapy
from django.http import HttpResponse
from scrapy.crawler import AsyncCrawlerProcess


class MySpider(scrapy.Spider):
    # Your spider definition
    ...


def crawl_view(request):
    process = AsyncCrawlerProcess(settings={"TWISTED_REACTOR_ENABLED": False})
    process.crawl(MySpider)
    process.start()  # returns when the spider finishes
    return HttpResponse("Crawling finished")

If the application runs its own asyncio event loop (for example, a Django web app deployed with an ASGI server such as uvicorn), you can use AsyncCrawlerRunner with TWISTED_REACTOR_ENABLED set to False, so that Scrapy uses the existing event loop:

import scrapy
from django.http import HttpResponse
from scrapy.crawler import AsyncCrawlerRunner


class MySpider(scrapy.Spider):
    # Your spider definition
    ...


async def crawl_view(request):
    runner = AsyncCrawlerRunner(settings={"TWISTED_REACTOR_ENABLED": False})
    await runner.crawl(MySpider)  # completes when the spider finishes
    return HttpResponse("Crawling finished")

Note

Running Scrapy without a Twisted reactor is experimental and has some limitations, described in Using Scrapy without a Twisted reactor.

Running spiders in Jupyter notebooks

You can run Scrapy spiders in Jupyter notebooks. You need to use AsyncCrawlerRunner with TWISTED_REACTOR_ENABLED set to False for this, so that Scrapy uses the event loop provided by the notebook kernel. As AsyncCrawlerRunner doesn’t configure logging, and you most likely want to see the spider log in the notebook, you should call scrapy.utils.log.configure_logging(). Here is a full example, which supports rerunning both as a single cell and as separate cells:

from scrapy import Spider
from scrapy.crawler import AsyncCrawlerRunner
from scrapy.utils.log import configure_logging

configure_logging()


class BooksSpider(Spider):
    name = "books"
    start_urls = ["https://books.toscrape.com"]

    def parse(self, response):
        for book in response.css("h3"):
            yield {"title": book.css("a::attr(title)").get()}


runner = AsyncCrawlerRunner({"TWISTED_REACTOR_ENABLED": False})
await runner.crawl(BooksSpider)

Note

Running Scrapy without a Twisted reactor is experimental and has some limitations, described in Using Scrapy without a Twisted reactor.

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 internal API.

Here is an example that runs multiple spiders simultaneously:

import scrapy
from scrapy.crawler import AsyncCrawlerProcess
from scrapy.utils.project import get_project_settings


class MySpider1(scrapy.Spider):
    # Your first spider definition
    ...


class MySpider2(scrapy.Spider):
    # Your second spider definition
    ...


settings = get_project_settings()
process = AsyncCrawlerProcess(settings)
process.crawl(MySpider1)
process.crawl(MySpider2)
process.start()  # the script will block here until all crawling jobs are finished

Same example using AsyncCrawlerRunner:

import scrapy
from scrapy.crawler import AsyncCrawlerRunner
from scrapy.utils.defer import deferred_f_from_coro_f
from scrapy.utils.log import configure_logging
from scrapy.utils.reactor import install_reactor
from twisted.internet.task import react


class MySpider1(scrapy.Spider):
    # Your first spider definition
    ...


class MySpider2(scrapy.Spider):
    # Your second spider definition
    ...


async def crawl(_):
    configure_logging({"LOG_FORMAT": "%(levelname)s: %(message)s"})
    runner = AsyncCrawlerRunner()
    runner.crawl(MySpider1)
    runner.crawl(MySpider2)
    await runner.join()  # completes when both spiders finish


install_reactor("twisted.internet.asyncioreactor.AsyncioSelectorReactor")
react(deferred_f_from_coro_f(crawl))

Same example but running the spiders sequentially by awaiting until each one finishes before starting the next one:

import scrapy
from scrapy.crawler import AsyncCrawlerRunner
from scrapy.utils.defer import deferred_f_from_coro_f
from scrapy.utils.log import configure_logging
from scrapy.utils.reactor import install_reactor
from twisted.internet.task import react


class MySpider1(scrapy.Spider):
    # Your first spider definition
    ...


class MySpider2(scrapy.Spider):
    # Your second spider definition
    ...


async def crawl(_):
    configure_logging({"LOG_FORMAT": "%(levelname)s: %(message)s"})
    runner = AsyncCrawlerRunner()
    await runner.crawl(MySpider1)
    await runner.crawl(MySpider2)


install_reactor("twisted.internet.asyncioreactor.AsyncioSelectorReactor")
react(deferred_f_from_coro_f(crawl))

Note

When running multiple spiders in the same process, logging settings and reactor settings should not have a different value per spider, and pre-crawler settings cannot be defined per spider.

Distributed crawls

Scrapy doesn’t provide any built-in facility for running crawls in a distributed (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

Reducing startup time in large projects

When running a spider with scrapy crawl, Scrapy loads all modules listed in SPIDER_MODULES to find the target spider. In large projects with many spiders, this can noticeably increase startup time and memory usage.

To avoid loading every spider module, override SPIDER_MODULES on the command line to point only to the module that contains the spider you want to run:

scrapy crawl myspider -s SPIDER_MODULES=myproject.spiders.myspider

Because SPIDER_MODULES is a list setting, you can include multiple modules by separating them with commas.

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 kinds 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 COOKIES_ENABLED) as some sites may use cookies to spot bot behaviour
use download delays (2 or higher). See DOWNLOAD_DELAY setting.
if possible, use Common Crawl 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.
for HTTPS websites, if blocking appears related to TLS behavior, consider adjusting the DOWNLOAD_TLS_MIN_VERSION and DOWNLOAD_TLS_MAX_VERSION settings, since some websites may respond differently depending on the TLS method used by the client.
use a ban avoidance service, such as Zyte API, which provides a Scrapy plugin and additional features, like AI web scraping

If you are still unable to prevent your bot getting banned, consider contacting commercial support.

Static analysis

Consider using scrapy-lint, a linter for Scrapy projects that detects common mistakes and anti-patterns.