Over the past decade, web scraping has become a common practice that allows businesses to deal with the vast amount of data produced on the internet. With quintillions of bytes of data being created each day, it’s no wonder that people have turned to automatic software which can move through the masses and find the required information.
While web scraping is undoubtedly a useful process, it’s fairly unknown that there are many languages that can be used when someone is creating a web scraping tool. Depending on which main coding language is used, the functions and capabilities of the platform will differ.
In this article, we’ll be exploring the main coding languages that are used within the world of web scraping, discussing the strengths of each language, and exploring what makes a coding language effective for web scraping.
Let’s get right into it.
What Makes a Coding Language Good for Web Scraping?
When creating a web scraping tool, you have a variety of different coding languages available to you, with each producing a different final product. Over time, three coding languages have distinguished themselves as the leading languages in web scraping, with Python, Node.js, and Ruby taking the cake.
The languages have found their way to the top due to four main reasons:
- Flexibility – Each of these languages offers a degree of flexibility, allowing a developer to change the data that they want to gather or adapt their searches to fit a more specific goal.
- Scalable – Some coding languages are much more frustrating to produce large programs in. These three languages are on the easier and more accessible side of the spectrum, often being fairly easy and painless to develop for long periods of time in.
- Maintainable – All three of these languages offer maintainable code, code that is easy to modify, build upon, adapt, and change over time. This is great for a system with ever-changing input, like a web scraper.
For these reasons, it’s clear why each of these coding languages has become so common for building web scrapers.
Web Scraping With Python
Python is by far the most commonly used language when it comes to web scraping. As a universal language that is used in a range of platforms, services, and by the majority of developers, this was always going to be a natural choice.
Python also allows developers to handle a range of different web scraping tasks (think: web crawling) at the same time without having to create elaborate code. With the addition of the Python frameworks of BeautifulSoup, Scrapy, and Requests, you’re also able to rapidly construct web scraping programs.
With a range of tools that help with the actual creation process, Python provides the major bulk of what’s needed to create an effective tool. Due to this, developers can create a comprehensive Python web scraper in a fraction of the time, launching their product with ease.
On systems that have the CPU power for this, this function of Node.js means that you can get through web scraping projects in a fraction of the time that it would take the same programs written in different languages.
The only downside to using Node.js for web scraping is that this process will consume your CPU, mainly for the aforementioned concurrent processing. If you don’t have a multicore CPU active during the process, then you won’t be able to do anything on your system until everything is complete.
Web Scraping With Ruby
Ruby is a very easy coding language to create web scraping platforms with, often providing a fast deployment without much hassle. If you’re looking for speed, then Ruby is definitely one of the best languages to go for. However, this coding language does have some rather large limitations when compared to Node.js and Python, making this the preferred style of developers that are looking for speed above all else.
One aspect of Nokogirl on Ruby that sets it apart and above the other languages is that it can effectively manage broken HTML fragments with ease. By coupling this with either Loofah or Sanitize, you’re able to clean up broken HTML, producing more information from a limited scope search that you would get with other languages.
Which Coding Language for Web Scraping Is Best for Me?
The best coding language you use to create a web scraping platform for you will change depending on what you’re looking for. Here are the best use cases of each of the languages that we’ve mentioned:
- Python Web Scraping – Fantastic for comprehensive searches, stable outputs, and slow but steady results.
- Node.js – Great for getting lots of information quickly, thanks to concurrent processing, but CPU intensive.
- Ruby – If you want to make and launch a web scraper in the next few hours, then use Ruby. It’ll allow you to get a basic quality web scraper that gets the job done and performs well for smaller data investigations.
Depending on what you’re looking for in a web scraper, the best coding language for you will change. That said, the best language is normally the one you’re most familiar with, as this will allow you to deploy the web scraper to its full capacity without any errors or frustrations on your part.
Web scraping is now a core part of data research, providing an easy and easy way to farm information from the internet. Of course, with any tool, there is a range of different coding languages that you could use to construct a web scraper. But web scraping manually does have its disadvantages, mainly that developers can only run one web scraper at a time.