Scrapy Vs. Beautiful Soup: Which is the best choice for your business?

‘Beautiful Soup’ can help extract specific elements from a target web page, while ‘Scrapy’ can manage asynchronous data retrieval, increasing efficiency. Not sure which option is best suited to your business’s needs? This guide can help.
Scrapy Vs. Beautiful Soup: Which is the best choice for your business?
louisr
Louis Ruggeri | Support Manager
02-Aug-2022

In this article, we will cover:

What is Beautiful Soup?

Beautiful Soup is a widely used Python-based library used for data collection. It operates using a ‘branch-like’ structure that is useful when looking to parse target data in either XML or HTML format. 

Beautiful Soup web scraping is fairly easy, especially for those familiar with Python coding conventions. Outgoing information is automatically formatted as UTF-8, while incoming datasets are transitioned into Unicode. 

It is important to understand that Beautiful Soup can help extract specific elements from a target web page but is not in and of itself, an independent scraping tool.

You can start using Beautiful Soup with this command:

pip install BeautifulSoup4

Key Beautiful Soup features 

The major feature that data collection professionals appreciate is the fact that Beautiful Soup enables a lot of parsing strategy agility. This is due to the fact that its library is based on parsers typically used with Python such as:

  • Html5lib
  • lxml 

What is Scrapy?

While some professionals may prefer web scraping with Python, others prefer using Scrapy.

But what is Scrapy?

Scrapy is an open-source tool that enables people to perform data collection, web crawling, data mining, performing testing automation, as well as other web-based tasks. Scrapy is Application Programming Interface (API)-based and can be used as a framework for building tailored web spiders. 

Scrapy is supported by, and can be used in tandem with:

  • PyPy 5.9
  • Python 3.5+ 
  • PyPy
  • CPython

The nice thing about Scrapy is that it can be used independently in order to crawl, retrieve, and parse data but also supports/integrates with a wide variety of extensions, and software. Scrapy can also manage asynchronous requests meaning that many target pages can be accessed simultaneously for data retrieval.

You can start using Scrapy with this command: 

 pip install scrapy

Key Scrapy features 

Some of the Scrapy features that data experts love include the fact that Scrapy is compatible with XPath, and CSS enabled by its interactive shell console. Scrapy can also be used to retrieve data from sources that use both XML, and HTML. And lastly, Scrapy allows professionals to easily export datasets into a variety of formats such as JSON, XML, and CSV. 

How does Scrapy measure up against Beautiful Soup?

In order to simplify the comparison process we have put together a comparison chart for your convenience:

ScrapyBeautiful Soup 
What type of tool is it?Is a ‘Spider’, and is more of a complete scraping tool. Is a ‘Parser’ and is more of a ‘library’. 
What kind of support system does it have?Has a large following and online community complete with forums. Has much less of a following, leading to a lesser ‘support system’. 
What level of technical knowledge is needed? Is geared towards a more tech-savvy crowd, including data experts, as well as IT/DevOps personnel.Is a much more ‘novice-friendly’ framework and can be more easily worked by individuals without a coding background.
How does this tool perform in the context of ‘scalability’?Is the better choice for companies that are growing and need a tool that can more easily handle growing data collection needs. It is capable of handling large/complex projects. Is more suitable for smaller/pinpointed projects such as extracting specific elements from a target web page. 
What are the performance speeds like?Is considered to be a very well-integrated tool with quick data-retrieval speeds. Is considered relatively slow, though the other side of that coin is its ability to be laser-focused

When comparing these two scraping options, Beautiful Soups’ clear advantages include the fact that it is much more beginner friendly with good supporting documentation.

Scrapy, on the other hand, is easily scalable, is very quick, and is very memory/CPU-efficient. It also has a strong community as well as built-in support. On the other hand – its documentation is quite sparse, especially for newbies. 

The bottom line 

Scrapy is a great choice for larger companies with more complex and ever-growing/changing data collection needs. Beautiful Soup, on the other hand, is better for smaller businesses run by individuals with very specific needs and limited technical capabilities. 

But people looking to collect data in order to grow their business should know that there is a ‘third way’. Automated data collection tools have taken front and center stage in recent years. What companies appreciate about this option is that:

  • It is fully automated
  • Professionals do not need any coding background to benefit
  • Retry logic ensures effortlessly circumventing target site blockades
  • No in-house personnel, software, or hardware needs to be maintained
  • And lastly, data collection operations can be scaled up or down at any point in time
louisr
Louis Ruggeri | Support Manager

Lou is a Support Manager at Bright Data with a voracious appetite for knowledge, especially when it comes to data. In his spare time, he enjoys a good book, the occasional taco, and blogging about data collection.