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The Best Programming Languages for Web Scraping: An Ultimate List

We compare seven popular programming languages for web scraping.

the_best_programming_languages_for_web-scraping

There are many programming languages to choose from when it comes to web scraping. And it can be challenging to find the right fit – some languages are easy to learn but pretty slow, and others can handle only static websites.

This article goes through the best programming languages for web scraping tasks. We provide an overview of each language and highlight their strengths and weaknesses for an easier decision.

What to Consider When Choosing a Programming Language for Web Scraping

  • Size of the project. Some programming languages use a lot of computing power or take a lot of time to process large amounts of data. Others are fast and scale well, so they’re a good fit for large-sized projects.
  • Performance. It’s crucial that your scraper can work uninterrupted. Performance depends on factors like whether it’s strong or weak typing language, execution time, and more. For example, speed is crucial when you need to scrape multiple pages.
  • Available libraries. While building a web scraper entirely from scratch is technically possible, this approach is dreadful. Every programming language offers libraries with pre-built functions that will facilitate the scraping process and offload some of the work from you. So, look for libraries with robust capabilities.
  • Learning curve. It shouldn’t surprise you that some programming languages are easier to use and set up than others. The difficulty of a language corresponds to the time spent on building and maintaining your scraper.
  • Ability to scrape dynamic content. Today many websites like social media use JavaScript to load their content. As a consequence, you’ll need to use a headless library to render dynamic AJAX pages while mimicking a real browser to overcome browser fingerprint.
  • Documentation. Extensive documentation includes everything from user manuals to code comments. This is the best place to look for components required to create and maintain a web scraper. If the language you’re using lacks documentation, you’ll need to scavenge for information sources with a similar issue as yours.
  • Community support. As a rule, the more popular a programming language is, the better community support it’ll have. Why is this important? Well, you won’t have problems finding solutions on platforms like Stack Overflow or discussing specific issues related to your scraper.

Comparison Table of the Best Programming Languages for Web Scraping

Here’s a summary table that displays the main features of all seven programming languages – Python, Node.js, Ruby, Golang, PHP, C++, and Java – side by side:

 PythonNode.jsRubyGolangPHPC++Java
Year of release1991200919952009199519831995
PerformanceMediumMediumLowHighLowHighMedium
Learning curveEasyMediumMediumMediumMediumSteepSteep
Web scraping ecosystemRobustModerateLimitedLimitedLimitedRobustModerate
Recommended for dynamic contentYesYesNoNoNoNoNo
Best forAll types of projectsJavaScript-rendered websitesProject managementPersistent Go loversLarge amounts of data from static pagesSpeed-dependent tasksMulti-threading

What Is the Best Programming Language for Web Scraping in 2024

1. Python – Easiest to Use and Packed with Everything

Python programming language has one of the largest communities of developers and users. It’s the top choice for web scraping, and there are several reasons for that.

Python is known for its respectable performance. The language is dynamically typed, so you won’t have to declare variable type or manage memory when assigning a value to a variable. In simple words, this makes Python relatively fast and light on resources.

What’s more, The Python Software Foundation regularly releases new versions with additional features, bug fixes, and security measures.

One of the biggest benefits – Python’s easy to use and has a simple syntax. You can write a basic Python scraper in minutes and with a few lines of code. The language uses new lines for commands, while other languages often go with semicolons or parentheses. This makes Python a great choice for scrapers of all skill levels.

Python is also versatile in terms of web scraping libraries and frameworks. Requests is Python’s standard for sending HTTP requests, and you can customize it by adding headers, cookies and other parameters. Beautiful Soup is a powerful tool for structuring results that combines a package of in-built parsers. Scrapy handles crawling, whereas Selenium emulates browser interactions.

Overall, Python is one of a few languages that works well from small to large-sized projects and is used by both skilled users and beginners.

2. Node.js – Ideal for Scraping JavaScript-Based Websites

Node.js is a JavaScript runtime, and it’s a second really popular option for web scraping. The runtime’s primary focus is building web applications, but with the growing popularity of JavaScript-rendered websites, it has become irreplaceable for dynamic web scraping.

Node.js is a great performer – even the biggest web applications like Netflix, Paypal, and Uber, are built upon  it. The runtime uses a non-blocking I/O model, which allows you to handle multiple connections and requests simultaneously. This makes Node.js a good choice for scraping multiple pages.

If you’re familiar with JavaScript and CSS, Node.js is relatively easy to learn. It uses fewer lines of code than programming languages like Ruby. Additionally, it has an active, fast-growing community of developers and users, so you won’t lack support.

Node.js has great libraries and frameworks for scraping dynamic websites. You can go with Cheerio, great for parsing HTML documents, or Puppeteer, which allows you to control a headless Chrome browser. That means you can fully automate most browser interactions, like filling out forms, moving the mouse, and waiting for the page to load.

However, Node.js uses a lot of computing power, so it isn’t the best choice if you want to scrape a large amount of data from JavaScript-dependent pages.

3. Ruby – Versatile Language for Small Scraping Tasks

Ruby is primarily used for building web applications, but scrapers found its strengths in scraping HTML web pages with CSS selectors.

The programming language is very versatile. Ruby combines features from programming languages like Perl, Smalltalk, Eiffel, Ada, and Lisp. Ruby includes the package management system RubyGems which allows you to easily install, manage, and share libraries or packages (gems) in your Ruby project.

Performance-wise, Ruby has a slower runtime and takes longer to boot compared to Python and Node.js.  So, it’s best for downloading and parsing small amounts of data.

With Ruby you’ll need to write more code for the same web scraping task compared to Python, but it’s relatively simple to use and read. Also, the documentation isn’t extensive, so if your scraper breaks, fixing the error will take time. But since it’s an old programming language, you’ll find many discussions on forums to help you out.

Even though Ruby has fewer libraries and frameworks for web scraping than Python, it still has an impressive collection of tools like Nokogiri, Mechanize, and Watir. For example, Ruby’s Nokogiri library for parsing HTML elements is popular among the web scraping community for its ability to deal with broken or malformed HTML.

In brief, Ruby is a great choice for small-sized projects that you need to share with your team in a cloud environment.

4. Golang – When You Need a Fast Scraper

Golang, also known as Go, is one of the newest programming languages, released by Google. It’s often compared to Python, and when it comes to scraping, Golang has several advantages.

Golang’s biggest benefit is speed. The language is compiled from the binary file and doesn’t rely on a virtual machine to build a web scraper. In simple words, the code is already readable before it starts running. This makes it much faster than languages like Python or Java. Also, Go has built-in support for concurrency, so you can scrape multiple web pages simultaneously.

That said, Go is a difficult language to learn unless you’re familiar with C or Java programming languages. Even though it’s easy to read, usually you’ll have to write more code compared to Python because Go lacks basic features like overloading.

What’s more, Golang uses a different approach to error handling. It doesn’t support try/catch methods which are preferred in other programming languages. This makes Go a less attractive option in terms of maintaining your web scraper.

The programming language doesn’t have many web scraping libraries and frameworks. Some popular choices include Colly and Gocrawl for crawling web pages, and GoQuery for scraping pages using CSS selectors.

All in all, Golang is a good programming language if you need to scrape multiple pages at once and you need to do it fast.

5. PHP – Great for Beginners that Don’t Need to Scrape Dynamic Content

When it comes to web development, PHP is one of the popular languages – websites like WordPress and Slack are built with this language. In terms of web scraping, PHP is a server-side scripting language used for gathering data from static HTML pages.

PHP is a great performer because it has a relatively small memory footprint – a big advantage when scraping large amounts of data. The language uses less memory, and it is light on resources. PHP is an interpreted language – it has to be translated every time before it’s run, which adds extra processing time. This makes the language slower than C++ or Java.

PHP has a simple but versatile syntax, making it a great language for beginners. Like Python, you can build a scraper with just a few lines of code. It also has a large community of developers, a stack of videos, and extensive documentation.

PHP can be used to scrape JavaScript-rendered websites, but it will definitely be more challenging – it’s a server-side language, and dynamic content requires client-side scripting. In other words, the HTML code won’t be available until after the page has been fully rendered.

PHP has a limited ecosystem of libraries and frameworks used in web scraping. The most popular ones are Simple HTML DOM library for parsing HTML and XML documents, and Guzzle for rendering JavaScript.

Overall, you should choose PHP for scraping small to large amounts of data from static websites. Otherwise, you’ll need other programming languages to scrape JavaScript-rendered websites.

6. C++ – The Fastest Language with Robust Parsing Capabilities

C++ is  well known for its parsing capabilities – you can parallelize any parser and implement multi-threads. For example, you can read a large XML file and parse the contents into a data structure.

C++ is a compiled language, which means it’s faster than interpreted languages like Python and PHP. Additionally, C++ has features like templates and operator overload that can help to optimize performance. And because of that, C++ is the best performing language. However, C++ takes up a lot of memory, so it isn’t ideal for large-scale tasks.

The language has a steep learning curve. If you plan to use it for web scraping, you’ll need to understand programming concepts like pointers, memory management, and data structures. On the bright side, you’ll get lots of support from users and developers.

Since C++ is one of the oldest languages, you won’t lack libraries and frameworks to choose from. For example, you can use the libcurl library to make HTTP requests, the HTML Tidy library for parsing, and PhantomJS for headless scraping. In short, there is a tool for any web scraping task, and you’ll find both free and paid options (expensive, though).

C++ is a good choice for speed-dependent tasks that don’t require scraping large amounts of data.

7. Java – Compatible with Any Configuration Operating Systems

Java is an open-source programming language that works well with multithreading. It’s used to scrape both static and dynamic web pages.

Java is a compiled language, so you won’t have to deal with slow performance. It runs on a Java Virtual Machine (JVM), which is responsible for managing memory, garbage collection, and other details that make Java code run more efficiently than other languages.

Java is easier to learn than C++, but it still has a steep learning curve. It has complex syntax and is a strong typing language; if you’re a beginner, you won’t be able to write code fast.

Java comes with many libraries. The most popular ones are JSoup which is ideal for dealing with malformed HTML, while HtmlUnit is a headless browser that can emulate user behavior like clicking elements. However, similar to C++, Java uses a lot of computing power, so you shouldn’t go with the language for small scraping tasks.

Java is a great choice if you want to get similar features to C++, but don’t have enough skills to master the language. Or you need to scrape both dynamic and static pages.

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Isabel Rivera
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