The intersection of cloud computing, artificial intelligence (AI), and machine learning (ML) has opened up new opportunities for businesses to use advanced analytics to drive business outcomes. However, in order to leverage these technologies, you need to have vast amounts of data to feed predictive and analytical algorithms.
This is where web scraping comes into the picture. Web scraping is the process of collecting raw data from websites for analysis. This data can be used for making informed decisions and, with the help of programming languages, can be automated to save time and resources.
This roundup will compare the top five languages for web scraping: JavaScript, Python, Ruby, PHP, and C++. These languages were chosen due to their flexibility, performance, ease of configuration, and community support.
Jump right in by taking a look at JavaScript, the language that powers most modern web frameworks.
JavaScript
JavaScript is a versatile and widely used programming language that has earned its place as one of the best options for web scraping. This is primarily due to the staggering number of libraries and tools available in the JavaScript ecosystem as well as the support provided by its enthusiastic community.
Take a look at some of the reasons JavaScript is a popular choice for web scraping:
Flexibility
JavaScript’s seamless integration with HTML code makes it easy to use on the client side. In addition, thanks to Node.js, deploying a web scraper on the server side is equally simple. Its flexibility to work both on the client and server side allows developers to choose the most suitable path for their project, an obvious advantage.
Performance
In terms of performance, JavaScript does not disappoint. Over the years, significant improvements have been made to minimize resource usage on both the client and server sides. Open source engines like V8 are proof of that effort, making JavaScript a good choice when it comes to web scraping workloads. Furthermore, JavaScript’s ability to handle asynchronous operations makes it an ideal choice for large-scale web scraping applications, as it can process multiple requests simultaneously without compromising performance and efficiency.
Learning Curve
JavaScript has a relatively gentle learning curve, especially when compared to other programming languages. Its syntax is easy to understand, making it a popular choice for beginners and experienced developers alike. Furthermore, the language’s extensive documentation and a vast array of learning resources ensure that even those with minimal programming experience can quickly grasp its fundamentals.
Community Support
The JavaScript community is thriving and continually expanding, providing developers with invaluable support and collaboration opportunities. Thanks to the vast network of experienced JavaScript professionals, newcomers to the language can quickly find answers to their questions, troubleshoot issues, and seek guidance on best practices. This extensive community support not only fosters growth and development within the JavaScript ecosystem but also paves the way for innovative web scraping solutions.
Web Scraping Libraries
JavaScript offers an impressive selection of web scraping libraries, simplifying the web scraping process and enhancing efficiency. Some libraries include Axios, Cheerio, Puppeteer, and Playwright, each catering to different web scraping requirements and preferences. Developers can take advantage of various tools and features offered by these libraries, simplifying the web scraping process and facilitating the extraction and manipulation of data from multiple sources.
As an example, here’s a code snippet that demonstrates how to use Puppeteer to scrape the title of a web page:
const puppeteer = require('puppeteer');
(async () => {
const browser = await puppeteer.launch();
const page = await browser.newPage();
await page.goto('https://example.com');
const pageTitle = await page.evaluate(() => {
return document.title;
});
console.log(`Title of the webpage: ${pageTitle}`);
await browser.close();
})();
As you can see, Puppeteer launches a browser, navigates to example.com, extracts the page title, prints it to the console, and closes the browser.
JavaScript, along with HTML and CSS, are the three main technologies powering the modern web, so it’s no surprise that it’s one of the best options for web scraping. Its flexibility, gentle learning curve, and vast web scraping libraries are strengths that set it apart from other languages, such as C++ and PHP. In fact, it would be the undisputed roundup winner in those segments if Python weren’t on the list.
To learn more about web scraping with JavaScript, read this web scraping with JavaScript guide.
Python
Python is a general-purpose language that excels in multiple areas. Its frameworks are widely used for building websites, automating complex tasks, working on AI and ML projects, and as you might suspect, performing data analytics and web scraping. As you’ll see later, Python’s popularity lies in its simple syntax that makes it easy to learn and use, its thriving community, and its versatility.
Flexibility
Python is versatile by nature. It’s hard to think of a project or application where it can’t be used. Therefore, it’s typical to see Python in server-side frameworks, such as Django. In addition, thanks to Python-to-JavaScript compilers, such as Pyjamas, Python can also be easily used on the client side. It’s precisely this adaptability that makes Python a good option for web scraping projects. Some may even argue that it’s almost on par with JavaScript regarding overall flexibility.
Performance
Python’s multithreading and multiprocessing support allows it to process and manipulate large amounts of data, making it an ideal choice for web scraping.
In addition, as you’ll soon learn, Python has numerous libraries designed exclusively for web scraping. This ensures the high performance of data extraction and analysis applications.
Learning Curve
One of the most appealing aspects of Python is its beginner-friendly nature. The language’s simple and intuitive syntax makes it accessible to developers of all skill levels. This gentle learning curve is further bolstered by Python’s extensive documentation, which provides clear guidance and instructions to ensure that newcomers can quickly grasp the language’s fundamental concepts and start building their own web scraping projects.
Community Support
The Python community is renowned for its extensive support, offering developers a wealth of resources and knowledge to assist them in their projects. This supportive environment fosters collaboration, ensuring that Python developers continually have access to cutting-edge techniques and solutions. Moreover, the Python community’s commitment to promoting the language’s growth and development has contributed to its consistently high ranking among the top programming languages worldwide.
Web Scraping Libraries
Python offers a comprehensive selection of web scraping libraries, enabling developers to create custom solutions tailored to their specific needs. Some web scraping libraries include Beautiful Soup, lxml, Scrapy, Requests, and Selenium. These libraries provide a diverse range of functionalities, from simple HTML parsing to advanced web content extraction and manipulation. This further enhances Python’s appeal as a go-to language for web scraping projects.
Here’s an example of web scraping using Beautiful Soup and Requests libraries. Just as before, this code snippet fetches the title of a web page:
import requests
from bs4 import BeautifulSoup
url = "https://example.com"
response = requests.get(url)
soup = BeautifulSoup(response.content, "html.parser")
title = soup.title.string
print("Webpage title:", title)
In this case, the Requests library is used to fetch the web page content, and then Beautiful Soup parses it. Finally, Beautiful Soup extracts and prints the title of the web page.
As you can see, Python offers basically the same advantages as JavaScript. It is a versatile language, is easy to learn, has extensive community support, and has a comprehensive selection of web scraping libraries. Likewise, it provides a good level of performance for web scraping applications, arguably slightly better than JavaScript. However, despite Python’s ease of use, you may need a language that allows you to prototype a web scraping project in no time. If that’s the case, Ruby is what you’re looking for.
To learn more about web scraping with Python, read this web scraping with Python guide.
Ruby
The motto of Ruby, “a programmer’s best friend,” is well-earned. That’s because Ruby is a language focused on simplicity, which explains its concise syntax and ease of use. If you add its incredible community and excellent web scraping frameworks, the result is an ideal language for a variety of projects.
Delve into what makes Ruby one of the best languages for web scraping:
Flexibility
As previously mentioned, Ruby revolves around simplicity. This feature makes it simple to write code that is clean and easy to maintain. This, in turn, allows the code to be easily modified and adapted to the changing needs of any web scraping project. Moreover, Ruby’s ease of modifying classes and creating methods on the fly pushes its flexibility to levels that are hard to beat.
Performance
Thanks to its built-in garbage collection and advanced memory management, Ruby provides an adequate level of performance for web scraping applications. While such performance doesn’t quite match that of Python or JavaScript, Ruby makes up the difference with its versatility and flexibility. In other words, for applications where the data extraction speed is not critical, Ruby offers a better effort-benefit ratio, given its ease of maintenance.
Learning Curve
Ruby is well-known for its elegant and expressive syntax, which is easy to learn and understand. This makes it an excellent choice for beginners looking to start web scraping and experienced developers who want to prototype and implement their web scraping solutions quickly. Simply put, Ruby’s readability, simplicity, and amazing documentation allow developers to focus on the task at hand rather than get bogged down with complex syntax.
Community Support
The enthusiasm of the Ruby community is one of its greatest strengths. There is a multitude of user groups, mailings, conferences, blogs, and even an official Discord server focused on helping both experienced and novice programmers.
All in all, the collaborative nature of its community is a compelling reason to use Ruby in your next web scraping project.
Web Scraping Libraries
Ruby offers many web scraping libraries to choose from, enabling developers to use the one that best fits their needs. Some Ruby web scraping libraries include Nokogiri, Mechanize, httparty, selenium-webdriver, OpenURI, and Watir.
Here’s a web scraping example using the Nokogiri and OpenURI libraries. Once again, the goal of this code snippet is to fetch the title of the web page and print it to the console:
require 'nokogiri'
require 'open-uri'
url = 'https://example.com'
html_content = open(url)
parsed_content = Nokogiri::HTML(html_content)
title = parsed_content.css('title').text
puts "The title of the webpage is: #{title}"
Similar to the logic used in the Python example, the program initially calls a library—in this case, OpenURI—to fetch content from example.com and then uses the Nokogiri library to parse the title and print it to the console.
Overall, Ruby is an ideal language for newbies and experienced developers alike since it has a unique and supportive community, a gentle learning curve, a plethora of web scraping libraries, and enviable versatility. This incredible balance of features is matched only by one other language on this list, PHP.
To learn more about web scraping with Ruby, read this guide on web scraping with Ruby.
PHP
PHP is a versatile server-side scripting language that has been around since 1994. It’s largely responsible for the advent of Web 2.0, as PHP made Web 2.0 easier for developers to manage relational databases and, thus, create dynamic websites and content management platforms, such as WordPress. It’s precisely this flexibility, reliability, and data management capabilities that make it a good option for web scraping projects.
Flexibility
PHP is known for its flexibility and adaptability. It seamlessly integrates with databases and web servers commonly used by web developers, including MySQL, PostgreSQL, Apache, and Nginx. This flexibility allows developers to build custom web scraping solutions tailored to their specific requirements. Moreover, PHP’s compatibility with diverse platforms and operating systems, such as Windows, macOS, and Linux, further enhances its versatility.
Performance
While PHP may not be as fast as the rest of the programming languages in this list, it still offers a satisfactory performance for web scraping tasks. Moreover, since the release of PHP 7 in 2015 and, more recently, PHP 8 in 2020, the language has seen significant enhancements regarding its memory consumption and execution time. Simply put, PHP’s performance is more than adequate for most web scraping projects where speed or scaling is not critical.
Learning Curve
While it’s true that Ruby, JavaScript, and Python have a cleaner syntax than PHP, it’s also true that they are more powerful languages that seek to cover a wider range of use cases. In comparison, PHP is native to the web; its focus is narrower, which makes it one of the easiest programming languages to learn. Additionally, given its time on the market, PHP has extensive and detailed documentation that makes it easy for novice programmers to write web scraping apps quickly.
Community Support
PHP has an active community of developers. This community provides valuable support through forums, blogs, and social media platforms, ensuring that developers can find assistance with their PHP-related queries and challenges.
Web Scraping Libraries
There are a healthy number of PHP libraries focused on web scraping. Some include the PHP Simple HTML DOM Parser, Guzzle, Panther, Httpful, and cURL.
Here’s a sample code snippet using Symfony’s Panther PHP library to scrape the title of a website:
<?php
require 'vendor/autoload.php';
use Symfony\Component\Panther\Client;
function getTitle($url) {
$client = Client::createChromeClient();
$client->request('GET', $url);
$titleElement = $client->getCrawler()->filter('head > title');
$title = $titleElement->text();
$client->quit();
return $title;
}
$url = 'https://example.com';
$title = getTitle($url);
echo "The title of the website is: $title\n";
?>
This script initializes a Panther client, navigates to the specified URL, extracts the title, and then prints it out.
Overall, PHP stands out for its ease of use, gentle learning curve, and tight integration with the databases and web servers used by most web developers. This largely makes up for its relative weakness in speed. Now, if your project requires a high-performance web scraping language, you should consider C++.
To learn more about PHP web scraping, read this web scraping with PHP guide.
C++
Like the other contenders on this list, C++ is a high-level object-oriented language. However, one crucial difference is that C++ is possibly the closest thing to a native machine language that you can use for web scraping. This gives C++ a notable advantage in terms of flexibility and speed, albeit at the cost of a steeper learning curve.
Flexibility
When it comes to flexibility, C++ is second to none on this list. Its ability to access low-level system resources makes it ideal for any use case. Some apps and operating systems written in C++ include macOS X, Windows 10, Microsoft Office, Mozilla Firefox, Counter-Strike, and Doom; but the list is endless. This flexibility allows developers to build highly customizable web scraping solutions that are also lightning-fast. However, such flexibility comes at a cost. Prototyping in C++ is overwhelming since you have to compile the program every time you make a change to the code.
Performance
C++ is known for its exceptional speed, which is due to the fact that it’s a compiled language that is directly translated into assembly code. In contrast, interpreted languages, such as Python, JavaScript, PHP, or Ruby, require an interpreter (aka its name) to read and execute the code, which entails greater use of resources and lower performance than C++. For instance, under certain conditions, C++ can be up to ten times faster than Python, which is no easy feat. That means if your project requires fast execution times, C++ is your best option.
Learning Curve
C++ is considered one of the toughest programming languages to learn. This is because programming in C++ is close to writing a program in machine code. That is, it requires using complex constructs as well as a good understanding of how computers work. That being said, learning C++ is well worth the effort as it allows developers to create advanced web scraping applications that can run on basically any hardware.
Community Support
While C++ is not easy to learn, the amount of resources and support offered by the community is staggering. Both industry giants like Microsoft and associations like the C++ Alliance are committed to empowering the community with valuable resources that facilitate learning. All in all, finding up-to-date information and support for learning C++ is not a problem.
Web Scraping Libraries
C++ offers a range of web scraping libraries, simplifying the process of retrieving and parsing web data. Some of these libraries include libcurl, Boost.Asio, htmlcxx, and libtidy. It’s worth mentioning other libraries, such as Gumbo and cpprestsdk. However, the former is deprecated, and the latter is in maintenance mode.
That said, here is a sample code snippet for web scraping the title of a website using the libcurl and htmlcxx libraries:
#include <iostream>
#include <curl/curl.h>
#include <htmlcxx/html/ParserDom.h>
using namespace std;
using namespace htmlcxx;
size_t writeCallback(void* contents, size_t size, size_t nmemb, void* userp) {
((string*)userp)->append((char*)contents, size * nmemb);
return size * nmemb;
}
string getWebContent(const string& url) {
CURL* curl;
CURLcode res;
string readBuffer;
curl_global_init(CURL_GLOBAL_DEFAULT);
curl = curl_easy_init();
if (curl) {
curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, writeCallback);
curl_easy_setopt(curl, CURLOPT_WRITEDATA, &readBuffer);
res = curl_easy_perform(curl);
if (res != CURLE_OK) {
cerr << "curl_easy_perform() failed: " << curl_easy_strerror(res) << endl;
}
curl_easy_cleanup(curl);
}
curl_global_cleanup();
return readBuffer;
}
string parseTitle(const string& html) {
HTML::ParserDom parser;
tree<HTML::Node> dom = parser.parseTree(html);
tree<HTML::Node>::iterator it = dom.begin();
tree<HTML::Node>::iterator end = dom.end();
for (; it != end; ++it) {
if (it->tagName() == "title") {
return it->innerText();
}
}
return "";
}
int main() {
string url = "https://example.com";
string html = getWebContent(url);
string title = parseTitle(html);
cout << "Title: " << title << endl;
return 0;
}
The code uses libcurl to fetch the HTML content of example.com and htmlcxx to parse the HTML and extract the title tag’s text.
All in all, no one disputes that C++ offers unmatched flexibility, performance, and a supportive community. However, if you’re not familiar with it, it may be easier to choose a language like Python or PHP since they’re easier to learn and implement.
To learn more about web scraping wtih C++, read this web scraping with C++ guide.
Conclusion
Overall, each of the five languages analyzed in this roundup has unique pros and cons regarding flexibility, performance, ease of learning, community support, and web scraping libraries.
JavaScript and Python stand out for their flexibility and ease of learning, making them ideal for beginners and experienced developers alike. In addition, both languages boast extensive community support and numerous web scraping libraries. In comparison, Ruby and PHP offer a good balance between performance, flexibility, and a gentle learning curve, with the added benefit of solid community support for web scraping tasks.
However, while it requires a steeper learning curve, a good implementation of C++ outperforms any other language in terms of raw performance. This makes C++ ideal for large-scale web scraping projects.
Ultimately, the choice of language depends on your specific needs, goals, and prior experience.
Fortunately, regardless of your choice, you can use Bright Data to unlock the power of web data. Bright Data’s products offer all the support you need to scrape website data at ease. Whether it’s high quality proxies, a headless browser for scraping (Playwright/Puppeteer compatible), a fully hosted Web Scraper IDE, or a large dataset marketplace, Bright Data has all the solutions needed for web data gathering.
