Best Programming Languages for Web Scraping: A Comprehensive Guide

Web scraping has helped many businesses and enterprises collect large amounts of raw data from the internet. This data can be used to make informed business decisions, and with the help of programming languages, this process can be automated to save time and resources.

But the question remains: “Which programming language is the best for web scraping?” Selecting the best language for web scraping can be challenging, given the various options, each with unique features and limitations.

By the end of this guide, you’ll have a clear understanding of the best languages for web scraping. More importantly, you’ll be able to determine which one suits your needs based on key capabilities, essential libraries, and potential limitations.

Let’s get started!

Top 8 Programming Languages for Web Scraping

1. Python

​​Python is the go-to choice for many programmers looking to build a web scraping tool. It is currently the most popular programming language in 2025, largely due to its simplicity and ability to handle almost every process involved in data extraction.

The language boasts one of the largest developer communities in the world, meaning there are plenty of tutorials, forums, and resources to help novice and experienced developers.

Note: Check out our beginner-friendly but detailed step-by-step tutorial on building a scraper in Python.

Another major win for Python is how beginner-friendly it is. Python is also dynamically typed, making it relatively fast for development and light on resources. 

Unlike other languages that rely on semicolons or parentheses, Python uses new lines to separate commands, promoting better readability and reducing software maintenance overhead.

Moreover, The Python Software Foundation regularly releases new versions with additional features, bug fixes, and security measures. 

Key Features

• Simple, readable syntax that reduces development time
• Extensive standard library with built-in tools and extensive documentation
• Dynamic typing for flexible data handling
• Excellent text processing capabilities
• Active support and large community
• Strong support for data analysis and manipulation

Popular Libraries/Frameworks

Python offers extensive support for scraping frameworks and libraries. Its libraries are capable of handling almost every aspect of data extraction. Some of the most popular options include:

• Beautiful Soup: Great for parsing and navigating HTML and XML documents.
• Requests: Requests is Python’s standard library for making HTTP requests, and you can further customize it by adding headers, cookies, and other parameters.
• Scrapy: Scrapy is a full-fledged scraping framework designed for large-scale scraping projects, supporting concurrency, data pipelines, and more.
• Selenium: A tool for automating browsers that helps scrape scraping JavaScript-heavy websites.

Limitations

• Slower performance compared to compiled languages like C++ or Go.
• Not ideal for scraping massive datasets where performance is critical.

2.  JavaScript (Node.js)

JavaScript (on its own) is very limited for serious web scraping. Thankfully, Node.js (a JavaScript runtime) expands its capabilities to the server side, making it a popular choice for building web scraping tools.  
Although Node.js primarily focuses on creating web applications, its asynchronous nature and event-driven architecture are well-suited for fetching and processing data from multiple web pages.

Node.js is the most popular choice for indexing various web pages since it enables simultaneous distributed crawling and data scraping. However, Node.js is generally best for small to mid-sized web scraping projects as it may struggle with large-scale scraping tasks without considerable optimization.

JavaScript has a relatively gentle learning curve compared to other programming languages. Its easy-to-understand syntax, extensive documentation, and many learning resources ensure that even those with minimal programming experience can quickly grasp its fundamentals. 

Additionally, JavaScript has an active, fast-growing community of developers who can offer support and advice when you encounter challenges. To learn more about web scraping with JavaScript, check out this web scraping with JavaScript guide.

Key Features

• Native understanding of web technologies and DOM
• Asynchronous processing capabilities
• Excellent for handling dynamic content
• Asynchronous I/O (For fetching data from multiple sources at once)

Popular Libraries/Frameworks

Developers can take advantage of the impressive selection of web scraping libraries available for Node.js. Some of these libraries include:

• Axios: A promise-based HTTP client for making requests and handling responses.
• Cheerio: This library implements core jQuery-like features for parsing and manipulating HTML on the server side.
• Puppeteer: This library allows you to control a headless browser (like Chrome) programmatically, simulating user interactions to extract data from dynamic element

Limitations

• High computing power requirements for large-scale scraping tasks
• More complex than Python, particularly for beginners.
• The Node.js ecosystem can sometimes overuse packages
• Risk of memory leaks in long-running scripts

3. Java

Java is one of the oldest and most established programming languages. It is highly versatile, object-oriented, and widely used in large-scale enterprise environments. Java also has remarkable portability since the Java Virtual Machine (JVM) can run on almost any platform.

Because it is a compiled language, you generally avoid the slower performance associated with some interpreted languages like Python. 

Java runs on the JVM, which manages critical operations like multithreading, memory management, and garbage collection, making Java code run more efficiently than many interpreted languages. 

While easier to learn than C++, Java has a steeper learning curve than scripting languages like Python or JavaScript.

Key Features

• Strong typing and compilation for robust performance
• Platform independence through JVM (Write once, run anywhere)
• Excellent multithreading capabilities
• Robust memory management
• Long-Standing Community:

Popular Libraries/Frameworks

Java offers support for various scraping frameworks:

• JSoup: A powerful library for parsing and manipulating HTML, well-suited for general web scraping tasks.
• HtmlUnit: A headless browser that simulates user interactions, useful for scraping JavaScript-heavy websites.

Limitations

• Steeper learning curve for beginners compared to Python
• Setting up large-scale Java projects can be complex and time-consuming
• Heavier resource consumption

4. Ruby

Ruby stands out as a programming language with an elegant, easy-to-follow syntax that makes it relatively simple to pick up and use. This allows the code to be easily modified and adapted to any changing needs of your web scraping project. 
Though Ruby excels at smaller scraping tasks, it may not be optimal for handling large datasets or complex scraping operations.

Performance-wise, Ruby has a slower runtime and longer startup time than Python or Node.js. As a result, it is better suited for downloading and parsing smaller amounts of data. 

Note: Check our beginner’s guide to Ruby scraping to learn more.

Although Ruby typically requires more lines of code than Python for similar scraping tasks, the code remains readable and maintainable.

Ruby has fewer web scraping tools than Python. However, it still offers valuable resources like Nokogiri and Sanitize, which excel at parsing HTML elements and can handle broken or malformed HTML. 

In addition, Ruby’s cloud development and deployment strengths make it a great choice for small-scale projects you might share with a team in a cloud environment.

Key Features

• Elegant and readable syntax
• Strong text processing capabilities
• Built-in regular expression support
• Active community and gem ecosystem
• Strong support for cloud deployment
• ‌Easy HTML CSS selector search

Popular Libraries/Frameworks

• Nokogiri: Gracefully handles HTML and XML parsing, even with malformed HTML.
• Watir: Automates browser actions, allowing for dynamic page scraping if needed.

Limitations

• Not ideal for large-scale scraping due to its slower runtime
• Less extensive documentation compared to Python or Node.js

Limited library support for scraping dynamic or JavaScript-heavy content.

5. Go

Go, often called GoLang, is the youngest programming language on our list. Google released it in 2009 with a focus on high performance and scalability. Go is a statically typed, compiled language well-suited for concurrent tasks. 
This makes it an excellent choice for web scraping projects that must handle multiple requests simultaneously without sacrificing speed or performance.

One of Go’s standout features is its built-in support for concurrency through goroutines. These are lightweight threads managed by the Go runtime, allowing developers to run thousands of tasks simultaneously, making Go highly effective for large-scale web scraping operations.

Note: Learn how to use Go for web scraping with our easy-to-follow guide.

What is particularly impressive about goroutines is their minimal memory usage. Creating a new goroutine is simple and requires very little overhead.

However, Go’s biggest advantage is its speed, as it is a compiled language. This makes it much faster than languages like Python or Java. 

Go’s syntax is very similar to C, and its commands are short and straightforward, allowing developers to create code quickly with fewer bugs. That said, Go is difficult to learn unless you’re familiar with C or Java programming languages. 

Key Features

• Strong support for concurrency and parallelism
• High-performance compiled code
• Strong static typing for better reliability
• Excellent memory management
• Fast compilation times

Popular Libraries/Frameworks

Go provides a couple of web scraping libraries. Some of them include:

• GO-Colly: A high-level, fast, and easy-to-use scraping framework.
• GoQuery: Offers a jQuery-like API for HTML document traversal and manipulation.

Limitations

• Smaller community compared to Python and JavaScript
• Steeper learning curve for developers new to static typing
• Fewer web scraping-specific libraries
• Limited libraries and tools for data processing

6. C++

C++ is a high-level, object-oriented language that operates closer to machine language than most other languages on this list. This proximity to machine language offers C++ a notable advantage in flexibility and speed, though it has a steeper learning curve.

While C++ is generally associated with systems and general-purpose programming, it can also be used to build simple web scrapers. However, it is not typically the most convenient choice for developing complex web crawlers.

With C++, developers can easily parallelize parsers and implement multi-threading; this makes it efficient for processing large XML files or HTML content. 

Key Features

• Can be used to write an HTML parsing library and fetch URLs
• ‌Massive user base
• ‌Easy to parallelize scrapers‌
• Superior performance and speed
• Excellent memory management control
• Strong parsing abilities

Popular Libraries/Frameworks

C++ offers a range of web scraping libraries. Some of these libraries include:

• libcurl: Handles HTTP requests and responses.
• libtidy: Used to clean and parse HTML effectively.

Limitations

• C++ is considered one of the most challenging programming languages to learn
• There’s a longer development time compared to scripting languages

7. R

R is an advanced programming language for statistical computing and data science. It includes a variety of data-oriented libraries that can also support web scraping.

While R is not as commonly used for web scraping as Python or JavaScript, it provides unique advantages, especially for data scientists who need to combine web scraping with statistical analysis.

Note: Learn to scrape web pages using R with our guide.

Another advantage of R is its built-in support for parallelization, making it possible to scrape multiple pages simultaneously with excellent efficiency. This capability, combined with its strong data manipulation features, makes it extremely useful for research and academic projects requiring data collection and analysis.

Key Features

• Great for statistical analysis and machine learning
• Excellent data visualization tools
• It supports multithreading
• Strong parallel processing support
• Active academic and research community

Popular Libraries/Frameworks

• rvest: Inspired by Beautiful Soup and RoboBrowser (two popular Python libraries for web scraping), rvest uses a similar syntax, making it an excellent choice for those transitioning from Python. With rvest, you can download an HTML document, parse it, select specific HTML elements, and extract data from them.

• RSelenium: Selenium is a popular browser-based rendering solution that can be used with R. While rvest works for most static websites, RSelenium is ideal for handling dynamic or JavaScript-rendered pages.

Limitations

• Higher learning curve for non-statisticians
• It has a smaller web scraping community compared to Python
• There are fewer web scraping tutorials and resources
• R’s performance can be slower compared to compiled languages

8. PHP

PHP is widely recognized as a server-side scripting language commonly used in web development. It also provides tools for web scraping and works well when integrated with server-side applications. 
One of PHP’s biggest advantages is its broad support for hosting services and VPS platforms. If you need a scraper running around the clock on a rented server, PHP may be the easiest and most practical choice since most servers support PHP scripts right out of the box.

PHP is also a great performer because it has a relatively small memory footprint, which is a big advantage when scraping large amounts of data. The language is lightweight on resources and uses less memory than some other popular options.

Note: Learn how to collect data from the web using PHP with our in-depth guide.

However, PHP’s multi-threading and dynamic content handling limitations can pose workflow scheduling and queue management challenges. Because of this, PHP is less suitable for large-scale scraping projects that require parallel processing. Still, as a relatively old language, PHP has extensive documentation that helps new programmers build web scraping applications quickly.

Key Features

• Simple syntax
• Robust ecosystem and dev resources
• Allows pattern-base data extraction

Popular Libraries/Frameworks

• Guzzle: It is a PHP HTTP client that makes it easy to send HTTP requests and trivial to integrate with web services.
• cURL (built-in): A built-in PHP tool for sending HTTP requests and fetching web content

Limitations

• Not suitable for large-scale data extraction
• Weak multithreading support
• Performance limitations compared to faster, more modern languages like Go or Java
• Limited parallel programming capabilities compared to Java and Go.

What is the Best Language for Web Scraping?

You can build a quality and reliable data scraper with almost any programming language. However, creating a web scraping tool isn’t simple and requires a lot of time, knowledge, and skill. 

Choosing the best language for web scraping depends on your specific objectives, resources, and expertise. Below are some categories to help guide your decision:

Most Popular 

Python has one of the largest communities of developers and users. Its popularity in web scraping is primarily driven by its extensive support for specialized scraping libraries such as BeautifulSoup, Scrapy, and Selenium.

The strength of Python’s community support is evident in its online presence. A quick search for the tags “web scraping” and “Python” on stack overflow reveals over 33,000 questions, with only about 2,700 of them left unanswered. This level of community engagement shows that you’re likely to find help quickly if you encounter any difficulties.

Fastest

Go (Golang) is the best option when performance is on the line due to its high execution speed and efficient memory management. Also, Go’s built-in support for concurrent processing allows it to execute multiple scraping requests simultaneously, making it an ideal choice for large projects and web applications. 

Easiest to Learn

For most beginners, Python is a simple-to-learn programming language with easy syntax and several libraries for web scraping. This abundance of resources, tutorials, and community support makes it easy for less experienced developers and newbies to get started with web scraping quickly.

Best for Dynamic Sites

 JavaScript/Node.js is the best option for scraping dynamic websites that rely on JavaScript for rendering content. Developers can use tools like Puppeteer and Cheerio to interact with the browser’s DOM, enabling them to extract content that traditional scraping techniques would otherwise miss.

Also, the ability to execute code in a browser context, handle asynchronous content loading, and manage single-page applications makes JavaScript uniquely suited for dealing with complex web applications and dynamic content updates. 

Note: Python also comes loaded with pretty good headless browser libraries. Check this article to see how to scrape dynamic websites with Python.

Here is a quick comparison table summarizing the key points we’ve covered:

Language	Learning Curve	Performance	Library Support	Best Use Cases	Limitations	Scrapers on Github
Python	Beginner-Friendly	Moderate	Excellent (Scrapy, BS4, Selenium)	General-purpose scraping, prototypes	Slower than compiled languages	76.1 k
JavaScript	Intermediate	High with Node.js	Strong (Puppeteer, Playwright)	Dynamic sites, large-scale async scraping	Resource intensiveMemory overhead	27k
Java	Intermediate	Moderate	Good (JSoup, HtmlUnit)	Enterprise-level, stable, cross-platform	Steeper learning curveHeavier resource consumption	3.7k
Ruby	Beginner-Friendly	Moderate	Decent (Nokogiri, Watir)	Quick scripts, prototyping, smaller projects	Less Extensive Documentation Limited library support	4.3k
Go (Golang)	Steep	Very High	Growing (Colly, Goquery)	Large-scale scraping, concurrency	Fewer librariesStricter syntax	3.3k
C++	Steep	Very High	Limited (libcurl)	Performance-critical, large data scraping	Steep learning curve Fewer scraping frameworks	3.4k
R	Intermediate	Moderate	Decent for data analysis (rvest)	Research, academic, quick data analysis	Not designed for large-scale scrapingSlower overall	2.3k
PHP	Intermediate	Moderate	Decent (Guzzle)	Simple scraping on existing PHP servers	Less optimal for concurrency	2.7k

Factors to Consider When Choosing a Language for Web Scraping

Size of the project 

The size of your project is a factor to consider when selecting the best language for web scraping. If your project is relatively small or you’re just experimenting, a language with a gentle learning curve and quick setup (such as Python or JavaScript) is ideal. For high-volume tasks, consider performance and concurrency features in languages like Go, Java, or C++ for enterprise-level or high-volume tasks.

Speed and Performance

Performance becomes crucial when scraping many websites or dealing with large-scale projects. Some languages are better at handling large datasets and can scrape data faster while optimizing computing power. Generally, If you need to scrape data at high speeds or in real-time, compiled languages like C++ or Go can handle high throughput efficiently.

Available libraries

Every programming language offers libraries with pre-built functions to handle different aspects of the scraping process, such as making HTTP requests, parsing HTML, handling JavaScript-rendered content, and managing concurrency for large-scale scraping. So, always keep the availability and quality of libraries and frameworks in mind when considering a language for your project.

Learning curve

The ease of use is one of the most important factors to consider when choosing a language for web scraping, especially for developers who are just starting or need to write scripts quickly. The difficulty of a language corresponds to the time spent on building and maintaining your scraper.

Documentation

Popular and well-established languages tend to have better documentation. This includes everything from tutorials to code comments. If the language you’re using lacks proper documentation, you’ll need to search extensively for information from others who have faced similar issues.

Community support

If you’re new to web scraping, a large community can help you troubleshoot issues quickly. Python and JavaScript excel in this area, as you won’t have problems finding solutions on platforms like Stack Overflow or Github when you encounter any issues.

Ultimately, the choice of language depends on your specific needs, goals, and prior experience. However, extracting data from the web is challenging because more and more websites have adopted rigid anti-scraping measures and bot prevention technologies.

Fortunately, regardless of your choice of language, you can use ScraperAPI to bypass these otherwise tricky measures. ScraperAPI also supports over five different languages, making the API easy to integrate with your project regardless of scale. 

Start your free trial today!

FAQs about Programming Languages for Web Scraping