Contact Information

Affiliate Engine SRL
Have any Questions? +40 768 002 995
Mail us today [email protected]

Python Frameworks for Web Crawling

There is basically no such thing as too much data, no matter what your project is. Out of the types of tools available for getting that data, the most robust ones for DIY work are Python frameworks for web crawling.

If you’re building your first web scraper and have limited programming experience, you may wish to consider using Python libraries for web scraping instead. Tools such as BeautifulSoup are easier to use for people just starting out. Once you have more experience, you can move on to more complex frameworks.

Let’s quickly go over some of those reasons to use Python. Then, let’s go over the two leading frameworks available for web data acquisition in Python.

Why Use Python?

There are a lot of solutions across several programming languages besides Python. However, Python is largely accepted as one of the best choices for web scrapers and crawlers for several reasons. If you are familiar with other viable programming languages already, it can still be worth considering using Python anyway.

Reasons to use Python include:

  • Low barrier to entry. Even with little prior experience with programming, you can fairly quickly learn how to use Python.
  • Elegant code. Due to its syntax and structure, Python code ends up with much higher readability than many other languages. Just how often do people give genuinely informative comments? 
  • Plentiful libraries, modules, add-ons, and frameworks are already available. These tools are often multi-purpose, so there’s no need to master more than just a few of them.
  • Easy script creation, usage, and distribution. Hence why all of that code is available. Also, even if the overall project isn’t entirely in Python, its code is easily convertible into other languages. It can communicate with other programs rather readily and can output to databases in numerous formats.
  • Lambda functions. When creating short-lived functions or variables, you can create and call the lambda function right when you need it, instead of having to pre-declare everything. Once the function is over and its output is made, it goes into garbage collection instead of holding those resources hostage.
  • Flexible and readily multitasks. While you’re scraping, with proper multithreading implementation it can also simultaneously download files, cycle proxies alongside your requests, handle strings as well as regular expressions, and export all of the results to your chosen database.

If you’re still inclined to stick to your language of choice, there are still a few available tool options. For example, JavaScript has Cheerio and Puppeteer, Ruby has Kimura, and PHP has Goutte.

Python Web Crawling Frameworks

You may already be familiar with web scraping libraries, which are smaller packages with only a few functions. In contrast, web crawling frameworks are more elaborate with many more features. But, of course, the more features, the more challenge in properly utilizing them all.

Of all the DIY tools available, one of the ones you’ll invariably see mentioned is Scrapy.


Scrapy is a powerful open-source framework with numerous functions for building both scrapers and spiders. It’s inherently multi-threaded and runs asynchronously as it is built on top of the event-driven framework for network programming, Twisted. 

Being event-driven means that it has a main function constantly running, looking for specific inputs. Once a function-triggering input is detected, it runs a subcommand on a separate thread accordingly. It then sends the results back to the main thread when it’s finished. 

Scrapy Pros

Scrapy is optimized for speed, and it shows. Code using Scrapy has been benchmarked at running an impressive 20 times faster than equivalent scripts in other languages. 

Not only is it fast, but it’s also memory-efficient, too. 

Scrapy has an exhaustive number of functions for any web scraping or crawling needs, some of which are:

  • Extracting and storing info from the called URLs, whether HTML or XML.
  • Generating feed exports in XML, JSON, or CSV and storing them backend via FTP, S3, or local filesystems.
  • Auto-detection and support for foreign, broken, and non-standard encoding declarations.
  • Strong extensibility support, open for plugins for any other features you need to have included.
  • A Telnet console for debugging.

It also smoothly handles:

  • Cookies and sessions.
  • HTTP features like authentication, compression, and caching.
  • Spoofing user-agents.
  • Honoring the robots.txt of the target sites.
  • Crawl depth restrictions.
  • Proxy rotation for each request.

And much more. 

While the number of features it has may seem daunting, there is an equally exhaustive quantity of documentation for Scrapy covering everything.

Scrapy Cons

It lacks the ability to render JavaScript. You could implement a bit of Selenium for any anticipated JavaScript, or Splash. Should you decide to go with the lightweight headless browser Splash, there is already a plugin for incorporating it into Scrapy, aptly named scrapy-splash.

Also keep in mind that Scrapy has a rather steep learning curve, even with the help of all of its documentation and community. Hence the earlier suggestion of starting with BeautifulSoup for your initial projects, or for when you’re doing smaller tasks in general. 

Scrapy isn’t your only option though. As long as you’re doing crawling rather than just scraping, there is also Pyspider.


Pyspider is another open-source framework available for programming in Python. While it isn’t as fast as Scrapy and is just for crawling, it has its perks. 

Pyspider Pros

Unlike Scrapy, Pyspider has a Graphical User Interface via a dashboard. It allows you to edit, monitor, manage, and view the results of your active spiders. The dashboard is much more user-friendly than the raw command-line coding for Scrapy. 

Also unlike Scrapy, Pyspider has JavaScript support already included via its built-in integration with Puppeteer

Pyspider has support for a large number of backend databases available for its results; Redis, MongoDB, Elasticsearch, MySQL, SQLite, PostgreSQL, and SQLAlchemy.

Pyspider Cons

It requires being hosted on a server, rather than something you could run locally. However, when running a perpetually crawling spider you built with Scrapy, you’d still want to use a server, anyway. 

As aforementioned, its run speed doesn’t compare to Scrapy

Pyspider has fewer anti-bot measures built in compared to Scrapy. It still supports rotating residential proxies, which handle most of the protections needed. However, If a priority of yours is ensuring that your bot doesn’t get blocked from any of your target sites, you should stick to Scrapy.


There are plenty of good reasons to use either Scrapy or Pyspider. Scrapy can handle more complex projects and be used for both scraping and crawling at high speeds. Pyspider is easier to use with no additional setup needed for JavaScript rendering.

Either of these Python frameworks for web crawling will help you build the perfect spider for your project. Running that spider with the protection of a Google proxy will give you the security you need to ensure you get accurate results with no holes in your data. There are reliable proxy providers for every budget, so don’t take unnecessary risks with free proxies.