# 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 is built on top of the Twisted asynchronous networking library, so you need to run it inside the Twisted reactor.

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)

See also

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 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, reactor settings should not have a different value per spider. Also, pre-crawler settings cannot be defined per spider.

See also

Run Scrapy from a script.

## 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

## 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. An open source alternative is scrapoxy, a super proxy that you can attach your own proxies to.

- 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.