The Best Programming Languages for Web Scraping

This article isn’t only for beginners; it’s also for those who already know how to code but are trying to decide on the best programming language for web scraping projects. Every programming language has its features, strengths, and weaknesses, and some are better suited for certain tasks.

When it comes to web scraping, things can get a bit more nuanced. That’s why in this article, I want to dive into the most suitable programming languages for web scraping and explain why they stand out.

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The Best Language for Web Scraping

Before diving into a detailed review of each programming language, let’s quickly go over the pros and cons of the top seven languages for web scraping. I’ll also share how many open-source scrapers you can find on GitHub for each language. To make it easier, I’ve compiled a small table summarizing the strengths and weaknesses of these languages:

You may have noticed that I didn’t call any programming language “the fastest.” That’s intentional. Having written scrapers in all these languages myself, I’d feel uncomfortable making claims without solid proof. So, as a not-too-serious experiment, I decided to see which language produces the fastest scraper in practice.

Here are the ground rules I followed for the test:

1. All tests were run at the same time of day.
2. I used the same machine and network for consistency.
3. The target website was a demo site specifically designed to be scraper-friendly.

The scraper algorithms followed a simple sequence:

1. Record the current time.
2. Send a request to the site and retrieve the HTML.
3. Parse the data to locate a specific element on the page, for example, the page title.
4. Record the time again and calculate the duration.

I deliberately kept things simple – no heavy libraries, web drivers, or complex setups. That said, I accounted for default features like connection reuse and caching, which some languages (like Node.js, Java, and Go) handle automatically unless explicitly disabled.

Of course, many factors can influence the results, so these tests aren’t perfect. Still, averaging over 10,000 measurements helps smooth out some inconsistencies. Here are the results:

Node.js emerged as the leading language, closely trailed by Python. Other languages – except for C, C++, and C# – lagged significantly behind. The slowest of all? Ruby.

I’ve already written detailed posts about scraping with all these languages, so feel free to explore those if you’re interested. And if Node.js (JavaScript) or Python caught your attention, our blog is packed with guides on using them for web scraping.

Now, let’s take a closer look at each of these programming languages and explore the unique advantages that make them popular choices for scraping.

Python

Python is undoubtedly one of the most popular programming languages when it comes to web scraping. Python is also one of the easiest languages to learn. There are an unimaginable number of libraries for almost anything you can think of, and programs written in Python are simple to run. But more on that later.

Now, let’s talk about community support because, honestly, what’s a programming language without a strong community backing it? If you’ve ever tried to prove the size or activity of a tech community, you’ll know it’s not exactly straightforward. But here’s a simple way: Stack Overflow. Every developer, no matter what language they use, eventually ends up on Stack Overflow (don’t deny it). A quick search for the tags “web scraping” and “Python” reveals over 30,000 questions, with only about 2,700 of them left unanswered. Therefore, the Python community is likely to assist you if you encounter any difficulties.

Another major win for Python is how beginner-friendly it is, even when it comes to setting up your first scraping project. You don’t even need to install Python or fiddle with your local development environment to get started. Instead, you can jump straight into the cloud with tools like Google Colab. Write your script, hit run, and you’re good to go – no setup nightmares involved.

Speaking of Google Colab, it’s also a useful source for finding prebuilt scrapers. Sure, GitHub is the obvious choice for open-source code, but don’t sleep on Colab – it’s packed with ready-to-run examples that you can learn from or adapt for your projects.

Now, let’s get to the best part: libraries. Python offers a lot of libraries for web scraping, but here are the most popular ones:

1. Beautiful Soup with Requests/Urllib
2. Lxml with Requests/Urllib
3. Scrapy
4. Selenium
5. Pyppeteer

If you’re curious about how these libraries stack up or want to see some hands-on examples, I’ve got you covered. Check out my other article where I break down the top 8 Python libraries for web scraping and share some code snippets to get you started.

NodeJS (JavaScript)

Another programming language that’s gained significant popularity for building scrapers is Node.js (JavaScript). When it comes to the “best programming language for web scraping,” Python and Node.js typically compete, with other options tending to lag behind and being more suited for specific, niche scenarios. One major point in favor of Node.js is its asynchronous nature, which sets it apart from Python.

The number of libraries, or npm packages, in Node.js is honestly staggering. There are so many that the joke, “Why write code when you can just install a package?” starts to feel more like reality. In fact, the Node.js ecosystem occasionally overuses packages. For example, instead of writing a few lines of code to check if a string is empty, you might stumble across a pre-built package for it.

When it comes to web scraping, Node.js doesn’t fall short in the package department compared to Python. Some of the standout tools include:

1. Axios and Cheerio
2. Puppeteer
3. Selenium
4. Playwright

By the way, if you’re interested in a deeper dive into the best scraping libraries for Node.js, we’ve got a dedicated blog post on that topic.

Now, here’s something unique JavaScript can offer for scraping: it works directly with Google Sheets using App Scripts. Thanks to Google Sheets’ support for scripting in Apps Script (a simplified version of JavaScript), knowing NodeJS allows you to write scripts that can run in the cloud and even export results directly into a Google Sheet.

Ruby

Ruby might not have as large a community or as many ready-to-go projects on GitHub as Python or Node.js, but compared to the other languages in our lineup, it holds its own surprisingly well. For starters, it has a relatively simple syntax that’s straightforward to pick up. Fun fact: like Python, Ruby is an interpreted language, which can make debugging and iteration a lot smoother.

Ruby also has plenty of libraries, or “gems,” as they’re called in the Ruby world, that cover pretty much any data scraping project you can think of. The standouts include:

1. Nokogiri. A powerful library for parsing HTML and XML.
2. Mechanize. Perfect for automating interaction with websites.
3. Selenium. When you need to handle dynamic content, it’s your go-to.

One undeniable advantage of Ruby is how seamlessly it integrates with various web services. If your web scraper needs to grow into a full-blown web application, you’re in excellent hands. Frameworks such as Rails or Sinatra facilitate this transition seamlessly. It’s like Ruby was built for this kind of flexibility.

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Java

Java stands out quite a bit in both its approach and syntax compared to the programming languages we discussed earlier. For beginners, it might seem overwhelming at first. It’s true — Java isn’t the easiest language to pick up if you’re just starting out. But let me tell you, Java has its loyal fans. Some are so passionate about it that they’ve even turned working Java code into rock songs, like NANOWAR OF STEEL’s HelloWorld.java.

One of Java’s defining features is that it’s statically typed. What does this mean? In simple terms, it catches errors during compilation instead of waiting until runtime to throw surprises at you. This can save you time, especially if you’re working on a big project. It’s like having a safety net to help ensure your code is reliable and secure.

Another area where Java shines is performance. Thanks to the Java Virtual Machine (JVM) and a host of optimizations, Java can handle large datasets and multitasking with impressive efficiency. If your project involves processing tons of data or running multiple tasks simultaneously, Java might just be the best choice.

When it comes to web scraping, Java offers a few libraries:

1. 1. JSoup. Extract data from HTML.
2. 2. HTMLUnit. Great for simulating a browser and handling JavaScript-heavy pages.
3. 3. Selenium. More complex solution that mimics user interactions.

If you’re worried about how to use these libraries or want to build your first scraper in Java, we’ve got a dedicated article for that. From my own experience, Java is a fantastic choice for web scraping if your project demands scalability, complexity, and a high level of reliability. Sure, it might require steeper learning, but for large-scale, ambitious projects, it’s definitely worth considering.

C/C++/C#

Talking about building scrapers with C, C++, or C# can be a bit tricky. While they share some foundational similarities, they’re actually quite different in practice. For example, in the performance tests we ran earlier, C++ came better in terms of speed, while C# lagged behind. That said, C# has a big advantage when it comes to libraries and ready-made tools for web scraping – it has way more options compared to C++.

Another thing worth noting is that C# is much easier to learn than C++. If you’re new to programming or want to set up a scraper quickly, C# is likely the best option. Because these languages share some common ground, we grouped them together for the purpose of this discussion.

One thing that really stands out about these languages is the incredible IDE they all have access to: Visual Studio. If you haven’t tried it, you’re missing out. It makes development so much easier and faster, which is especially handy for something as detail-oriented as web scraping.

When it comes to libraries for scraping, here are a few popular ones you’ll want to check out:

1. HtmlAgilityPack
2. ScrapySharp
3. Selenium

If you’re thinking about using one of these three languages for scraping, my advice is to go with C#. It’s more beginner-friendly, has better library support, and will save you a lot of time in the long run.

Go

Go is the youngest programming language on our list. It’s simple, functional, and incredibly efficient. In recent years, its popularity has been on the rise.

When it comes to web scraping, Go has a couple of libraries to offer:

1. Colly
2. GoQuery

One of Go’s most powerful and unique features is its built-in support for concurrency using goroutines. Goroutines let you run thousands of tasks simultaneously, making Go particularly effective for scraping large numbers of pages at once.

What’s impressive about goroutines is how little memory they require. Spinning up a new goroutine is simple and lightweight, allowing you to scale your scraper effortlessly. This means you can process hundreds or even thousands of pages at the same time without a problem.

PHP

Despite being at the bottom of our list, PHP retains a unique position in the realm of web scraping languages. One of PHP’s biggest advantages is its near-universal support for hosting services and VPS platforms. If you’re renting a server to keep your scraper running around the clock, PHP might be the easiest and most practical choice.

Using PHP saves you the trouble of configuring the system or environment for a different language – most servers support PHP scripts right out of the box. Plus, major cloud platforms like Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS) offer support for PHP.

As for libraries, there are two options worth mentioning:

1. DOMDocument
2. simple_html_dom

That said, don’t think you’re limited to running PHP scripts only on a server. I’ve previously shared a guide on setting up a local environment for PHP and demonstrated some simple scraper examples in another article.

Conclusion

If none of the languages we’ve discussed feel like the right fit, check out our other articles on web scraping with R or Rust. They’re less common choices but still worth exploring if you’re curious or if the languages we covered in this article don’t fully meet your needs. Honestly, you can use almost any programming language for web scraping as long as you’re comfortable with it. Each language has its own features, strengths, and limitations. Choosing the appropriate language depends most on your personal preferences and the specific requirements of your project.