5 Preferred Programming Languages for Web Scraping 5 Preferred Programming Languages for Web Scraping
Web scraping or web harvesting requires a good tool to be undertaken efficiently. It involves data crawling, content fetching, searching, parsing,... 5 Preferred Programming Languages for Web Scraping

Web scraping or web harvesting requires a good tool to be undertaken efficiently. It involves data crawling, content fetching, searching, parsing, as well as data reformatting to make the collected data ready for analysis and presentation. It is important to use the right software and languages for web scraping for the job.

Featured below are five of the best programming languages for web scraping. This list is based on a number of factors including intuitiveness, ease of coding, maintainability, flexibility, and, of course, effectiveness in web scraping. The popularity of the software also matters. A more popular tool tends to be better updated and backed by a large community of users who can help each other in addressing issues or learning new and more effective ways of web scraping.

Most popular: Web scraping with Python

Python is regarded as the most commonly used programming language for web scraping. Incidentally, it is also the top programming language for 2021 according to IEEE Spectrum. This object-oriented language comes with a massive group of libraries, including modules for machine learning.

What makes Python the top choice for web scraping is its ability to handle virtually all processes involved in data extraction. Aside from being easy to use (non-usage of semicolons and curly braces in particular), Python is notable for the direct use of variables wherever required. This makes the job considerably easier and faster. The programming language is also known for its “small code, big task” approach, wherein codes are generally small relative to those of other programs.

Also, Python syntax is very easy to understand. It is just like reading English phrases and statements. Newbie programmers and even those who know nothing about programming with Python will likely understand or have an idea of what the codes are meant to do.

It also helps that Python has a huge global community of users. There are many discussion boards and chat groups devoted to Python programming. Users can easily find help or advice on how to deal with a difficulty they encounter as they write their web harvesting programs.

Smooth and simple: Web scraping with Ruby

Ruby is another popular programming language for web scraping. It is known for its simplicity and easy-to-follow syntax, which is great for coders at any level. It is also notable for the productivity it affords its users.

This programming language excels at production deployments. String manipulation with Ruby is based on the Perl syntax, which not only makes it easy to do, but is also superb for web page analysis.

One feature that makes Ruby a preferred web scraping programming language is Nokogiri, which is often described as easier to use compared to Python. Nokogiri offers an easier way to deal with broken HTML / HTML fragments. Together with popular Ruby extensions such as Loofah and Sanitize, web scraping with Ruby, especially when addressing broken HTML, can indeed be a smooth and simple process.

Ruby has a significant advantage over Python in terms of cloud development and deployment. This is largely due to the Ruby Bundler system that works incredibly well in the management and deployment of packages from GitHub.

Moreover, Ruby has excellent testing frameworks that simplify and accelerate the building of unit tests that include advanced features like web crawling using WebKit / selenium, one of the most popular open-source tools for automating web applications.

The choice for dynamic pages: JavaScript web scraping

JavaScript, with the help of the Node.js runtime environment, is considered the preferred programming language for web harvesting on pages that were created using dynamic coding. It can produce non-blocking I/O apps that work well with several simultaneous events. It is the recommended option for API, streaming, and socket-based implementation.

One drawback of JavaScript, however, is that it is not that easy to understand for inexperienced coders. Also, it is not as robust as Python and Ruby. Most of its advantages are based on its association with Node.js.

One feature that stands out with Node.js is how it is being processed by computers. Every Node.js process is handled by one CPU core. As such, multiple instances of the same script can be run smoothly in most modern devices that usually already have multi-core CPUs.

JavaScript with Node.js enables the creation of a powerful web scraper backed by the following built-in libraries: ExpressJS, Request, Request-promise, and Cheerio. ExpressJS is a flexible web app framework that supports web and mobile apps. Request is intended for making HTTP calls while Request-promise enables fast and easy HTTP calls. Cheerio, on the other hand, is used in traversing the Document Object Model and extracting data based on the implementation of core jQuery for the server.

Node.js is suitable for basic web scraping. However, it is not the top choice when it comes to harvesting large amounts of data. It is also not advisable for long-term processes.

Old school web scraping with C++

C++ is often associated with general-purpose programming, but it can also be a good option among languages for web scraping. This object-oriented programming language is characterized by data abstraction, classes, and inheritance. These are qualities that make it easy to reuse and repurpose a written code for other needs. Also, the object-oriented nature of the language enables easy storage and parsing.

Additionally, C++ is notable for its high scalability. A code used for a small project can be reused to undertake bigger projects with some tweaks or modifications. The issue with C++, though, is that it is static, which means it will not work with use cases where dynamic languages are required.

Also, C++ is not good for creating web crawlers. This programming language is great for simple web scraping, but for projects that involve the generation of URL lists and other crawling activities, there are better options.

Still, C++ is a very popular programming language. It would not be difficult to find help from other C++ coders when encountering coding problems along the way. There are many developers willing to share their insights in various forums and groups.

Web scraping with Java

Java continues to be one of the most widely used programming languages in the world. It is the top programming language in the TIOBE index. It should not come as a surprise that it is a preferred programming language for web scraping.

Java has a variety of tools, libraries, and external APIs that can be used to create good web scrapers such as JSoup, HTMLUnit, and Jaunt. JSoup, a simple library, provides the functionality necessary for data extraction and manipulation through DOM traversal or CSS selection. HTMLUnit is a framework that enables the simulation of web page events such as clicks and form submissions. Meanwhile, Jaunt is a library devoted to web automation and scraping. It is useful in scraping data from HTML pages and JSON data payloads.

For advanced web scraping projects, Java may not be the best option. However, it does support the building of powerful web scrapers for various purposes. The program is also being used by an overwhelming majority of businesses worldwide, so it has a large community of users from whom new or inexperienced developers can ask for assistance or insights on addressing issues.

Complementing Web Scraping with Automated Tools 

The five programming languages above allow anyone to undertake web scraping projects. They are not suitable for everyone and for all kinds of projects, though. It is important to do some research on the right programming language to use, based on well-defined objectives and project parameters.

For companies looking to web scrape at scale, there are automated solutions that complement or supplement website data collection projects. Bright Data, for example, helps in scaling up web data collection projects, as it can run millions of web scrapers at the same time. It has a wide proxy infrastructure that helps address the challenges of website data harvesting.

About The Author –

Hazel Raoult is a freelance marketing writer and works with PRmention. She has 6+ years of experience in writing about business, entrepreneurship, marketing and all things SaaS. Hazel loves to split her time between writing, editing, and hanging out with her family.

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