Scrapy framework tips and tricks

Use Scrapy shell for interactive experimentation

Running scrapy shell gives you an interactive environment for experimenting with the site being scraped. For example, running fetch() with URL of page fetches the page and creates a response variable with scrapy.Response object for that page. view(response) opens the browser to let you see the HTML that the Scrapy spider would fetch. This bypasses some of the client-side rendering and also lets us detect if the site has some countermeasures against scraping.

Furthermore, calling css() or xpath() methods on response object is a convenient way to refine your CSS or XPath queries before putting them into actual Python code.

To learn more about Scrapy shell, see:

Use image/file pipeline for file downloading

Sometimes your web scraping project will involve downloading images or other kinds of files. Scrapy provides some official pipelines for this exact task.

To download any kind of files, we can integrate FilesPipeline by adding it into ITEM_PIPELINES map in file like this:

ITEM_PIPELINES = {'scrapy.pipelines.files.FilesPipeline': 1}

You would also need to set the directory path for files to be downloaded:

FILES_STORE = '/path/to/valid/dir'

Furthermore, your items will need to include file_urls field with a list of file URLs. When files pipeline processes the item, it will download each file into directory you have configured and will set a files property with list of dictionaries of original URLs and paths of downloaded files. To convert file URL into file name, SHA1 hash is computed on URL and prepended to the original file extension.

Integration of images pipeline is rather similar. You would edit to add scrapy.pipelines.files.FilesPipeline into ITEM_PIPELINES dictionary and set IMAGES_STORE to path of directory that will be used for storing files. However, images pipeline provides some image processing capabilities, thus requiring that Pillow module is installed. By default, images pipeline automatically converts all downloaded images to JPEG format.

Images pipeline expects image_urls item property to be filled with list of image URLs and will set images property in a a way equivalent to files property of files pipeline.

Both pipelines allow storing downloaded files on stores external to local file system:

  • AWS S3 buckets
  • Remote FTP servers
  • Google Cloud storage buckets

To learn more about this, see:

Use automatic throttling for scraping rate-limited sites

Some sites implement rate-limiting and will refuse to give you responses with proper pages if you are generating too many requests too quickly from single IP. A simple way to slow down your Scrapy project is to decrease the following values in


These variables control upper limit of how many concurrent requests spider is allowed to launch per domain/target IP address - only one of them should be set.

A more advanced way to slow down is to use AutoThrottle extension by uncommenting the following parts of file and experimenting with values to reach a point of no requests being dropped:

# Enable and configure the AutoThrottle extension (disabled by default)
# See
# The initial download delay
# The maximum download delay to be set in case of high latencies
# The average number of requests Scrapy should be sending in parallel to
# each remote server
# Enable showing throttling stats for every response received:

This will enable an adaptive throttling algorithm that will take into account a load of both your Scrapy instance and that of remote server(s) being scraped.

To learn more about automatic throttling, see:

Deploy Scrapy projects to the cloud

Once you have your Scrapy project running properly, you may want to avoid running it on your local machine to perform scraping. There are several ways to get it running in the cloud environment.

The simplest way is to get a cheap VPS (e.g. $5/month Digital Ocean droplet), install Scrapy there, upload your Scrapy project via SFTP and run it in tmux session.

Another way is to self-host a solution like Scrapydweb that will provide you with web interface to upload your Scrapy project and monitor scraping progress on the dashboard.

Yet another way is to sign up for Scrapy Cloud - the official service from creators of Scrapy. This service has its own CLI tool for uploading your Scrapy project and launching it in the cloud environment, which means you can integrate it into your CI/CD pipeline.

Use Item Loader to streamline item creation

Scrapy lets you create Item Loaders to simplify and streamline item creation based on CSS selectors and XPath queries. Furthermore, it allows you to include extra steps such as whitespace stripping and other parsing tasks.

Scrapy documentation provides the following example on how Item Loader could be used in the spider:

from scrapy.loader import ItemLoader
from myproject.items import Product

def parse(self, response):
    l = ItemLoader(item=Product(), response=response)
    l.add_xpath('name', '//div[@class="product_name"]')
    l.add_xpath('name', '//div[@class="product_title"]')
    l.add_xpath('price', '//p[@id="price"]')
    l.add_css('stock', 'p#stock]')
    l.add_value('last_updated', 'today') # you can also use literal values
    return l.load_item()

To learn more about Item Loaders, see:

Trickster Dev

Code level discussion of web scraping, gray hat automation, growth hacking and bounty hunting

By rl1987, 2022-02-20