The 5 Best Programming Languages for Web Scraping

Collecting large amounts of data for further analysis, forecasting, monitoring, and a host of other tasks has become a mainstay of many industries. Using web scraping (or parsing) with programming languages can save time and resources. 

The effectiveness of web scraping depends on its proper utilization. Among the many programming languages, only a few can be noted as the best ones for this purpose. In this publication, you will learn about which languages are best suited for information gathering, their advantages, and an effective method of automatically solving captcha during the data extraction process.

Python is currently considered one of the most popular languages for web scraping. This is due to several very good reasons, which makes it top of our list.

Adaptability, flexibility, simplicity and convenience

Python has a clear and simple syntax, it also integrates easily with other tools and technologies. Due to its versatility, its use can be envisioned in almost any project or application. Therefore, it is not surprising that even novice programmers can quickly create scripts to collect data from websites. 

Performance 

Python is capable of supporting parallelism and multiprocessing, which allows it to efficiently process and manipulate large amounts of data. It can also perform asynchronous operations, and this increases performance. All this makes it an ideal choice for parsing.

Large number of libraries and extensive community support

Python has many specialized libraries for web scraping, such as BeautifulSoup, Requests, and Scrapy. These tools make it easy to work with HTML, XML, and other data formats and the data collection process itself. Python also has a large community of developers who actively create and maintain libraries and tools for web scraping. This fosters collaboration and ensures continued access to best practices and solutions. Thanks to the community's commitment to language development, Python remains one of the leaders among the top programming languages around the world.

Python parsing example (using Requests and BeautifulSoup libraries)

import requests
from bs4 import BeautifulSoup

url = "https://example.com"

response = requests.get(url)

soup = BeautifulSoup(response.content, "html.parser")

title = soup.find("title").text

print("Title:", title)

This code sends a request to the specified URL, then uses BeautifulSoup to parse the HTML code of the page. It then finds the <title> tag and outputs its text, which is the page title.

JavaScript integrates seamlessly with HTML, making it easy to use on the client side. Node.js also makes deploying the parser on the server simple. This flexibility allows developers to choose the most appropriate path for their projects.

Without Node.js, JavaScript would have been limited for web scraping because it was originally designed for basic scripting in browsers. Node.js moved JavaScript to the server, and it made it easy to open network connections and store data in databases. These features made JavaScript one of the best languages for web scraping. 

Performance 

JavaScript performs well in terms of performance due to improvements in client-side and server-side resource utilization. JavaScript's ability to handle asynchronous operations makes it ideal for large projects, allowing multiple requests to be processed simultaneously without performance loss

Community and library support

The JavaScript community is actively growing, providing developers with support and opportunities for collaboration. This fosters innovation in parsing. JavaScript offers a wide range of libraries for web parsing, such as Axios, Cheerio, Puppeteer, and Playwright, each catering to different requirements. 

While the advantage of one process per CPU core limits Node.js for heavy data collection tasks, for simple web scraping tasks, Node.js with its lightweight and flexible features remains an excellent choice.

JavaScript (Node.js) parsing example:

const axios = require('axios');

const cheerio = require('cheerio');

async function getPageHTML(url) {
    const response = await axios.get(url);
    return response.data;
}

function parseTitle(html) {
    const $ = cheerio.load(html);
    return $('title').text();
}

const url = 'http://example.com';

getPageHTML(url)
    .then(html => {
        const title = parseTitle(html);
        console.log('Page title:', title);
    });

This code sends a GET request to a web page at the specified URL (http://example.com), loads the resulting HTML code of the page, and then parses the page title from the HTML using the cheerio library and outputs it to the console.

Perhaps the main advantage of Ruby is its ease of use, making it one of the most sought-after open source programming languages. It is important to note that the benefits of using Ruby are not limited to its simple syntax and other available features.

Interestingly, Ruby also outperforms Python in cloud development and deployment. This can be attributed to the Ruby Bundler system as it efficiently manages and deploys packages from GitHub, which makes Ruby a great choice if your requirements come down to simple and smooth web scraping.

Great frameworks make Ruby an ideal choice for web scraping. Here are all the reasons why Ruby is so good for parsing:

Flexibility

Ruby's simplicity makes it easy to create clean and easily modifiable code.

Performance

Ruby provides ample performance for web scraping with built-in garbage collection and memory management.

Elegant syntax makes Ruby appealing to beginners and experienced developers.

Community Support

Ruby's active community provides extensive support and resources for all skill levels.

Web Scraping Libraries

Many Ruby libraries, such as Nokogiri and Mechanize, simplify the process of writing code and parsing itself.

Ruby parsing example:

require 'nokogiri'
require 'open-uri'

url = 'https://www.example.com'

html = open(url)

doc = Nokogiri::HTML(html)

title = doc.at_css('title').text

puts "Page title: #{title}"

The purpose of this parser is similar to the previous Python and JavaScript examples - to find and display the title of a web page in the console. This code sends a request to a specified URL, loads the HTML content of the page, then uses the Nokogiri library to parse and find the title tag (<title>) of the page. The title is then displayed on the screen.

Although C++ may require a deeper learning curve and more effort to write and maintain than some simpler programming languages, its performance and flexibility are superior to any other language on this list. Suppose easy-to-understand syntax and simplified structure are not your priority. In that case, if you have enough experience with this language and you care about high speed of processing large amounts of data, C++ will be the best choice. Let's consider all the main advantages that deserve C++'s inclusion in our rating:

Flexibility

C++ is highly flexible due to its access to low-level system resources, making it ideal for various use cases. 

Performance 

It is a compiled language, unlike interpreted Python or JavaScript, which require an interpreter to execute. This affects the speed at which tasks are completed. C++ is considered difficult to learn because of its proximity to machine code, requiring an understanding of computers and the use of complex constructs. However, learning C++ is worth the effort as it allows you to create advanced applications that run on various hardware. 

Community Support

C++ has extensive community support and resources provided by companies and associations.

Web Scraping Libraries 

There are also a number of web scraping libraries available for this language to simplify the process of data extraction and parsing, such as libcurl, Boost.Asio, htmlcxx, and libtidy.

C++ parsing example:

#include <iostream>
#include <string>
#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") {
            ++it;
            if (it != end) {
                return it->text();
            }
        }
    }

    return "";
}

int main() {
    string url = "https://example.com";
    string html = getWebContent(url);
    string title = parseTitle(html);

    cout << "Page title: " << title << endl;

    return 0;
}

This example gives a general idea of how you can parse page headers in C++ using the libcurl and htmlcxx libraries. 

PHP is a powerful server-side programming language developed in 1994 and has since become one of the most popular web development languages. PHP was originally designed to create dynamic web pages, and its syntax and structure make it particularly suitable for web scraping. Its features include built-in functions for handling HTTP requests and processing HTML content.

Performance 

PHP is an interpreted programming language, which can reduce its execution speed compared to, for example, the compiled C++ language. However, modern versions of PHP, from version 7 and up, include optimizations that greatly improve its performance, and this is more than enough for many web scraping tasks, especially for medium to small projects. Plus, PHP has the ability to run asynchronous queries, which also improves performance.

Flexibility and versatility 

PHP integrates seamlessly with various platforms and operating systems, and supports a wide range of databases, web servers, and protocols - allowing developers to create flexible and scalable web scraping applications. 

Widespread adoption, community support, sustainability and reliability 

PHP is one of the most popular programming languages for building web applications. Its support is available with most hosting providers, making it a convenient choice for web scraping. PHP is known for its stability and reliability, which is why it is considered the preferred programming language for web scraping tasks. An active developer community provides support and assistance in case of questions or problems.

Web Scraping Libraries 

Thanks to a large community of developers, there are many libraries and tools that facilitate the web scraping process. The most popular ones are: PHP Simple HTML DOM Parser, Panther, Guzzle, cURL. 

PHP parsing example

<?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 "Page title: $title\n";
?>

This code uses the Panther library to extract the page header.

Each of the programming languages on this list has its own advantages for web scraping. With proper understanding of their peculiarities and competent use, they all cope with this task. We have compiled a list of the most optimal languages for data mining, but you can consider other languages like Go, Rust, Java and C# in addition to them.  They can also easily cope with extracting information from websites, although in general they are still a bit inferior to the main languages from our rating (but for you and your tasks one of them may be the ideal choice). 

Let's briefly describe the pros and cons for working with data of each of them: 

Go

Pros for web scraping: 

• High speed and efficiency
• Built-in goroutines (lightweight threads in Go that allow efficient execution of concurrent tasks within a single process) for simultaneous query processing
• Lightweight and easy to understand syntax
• Availability of basic libraries for HTTP requests and HTML parsing

Cons for web scraping: 

• Less flexibility in handling dynamic data
• Lack of high-level libraries (compared to Python)
• More complex HTML parsing
• Fewer resources and examples (compared to Python)

Rust 

Pros for web scraping: 

• Rust's security system avoids many typical errors, such as accessing invalid memory, this makes scraping more reliable.
• Rust compiles to machine code, ensuring high performance and efficient resource utilization.
• The language has powerful tools for secure parallel code execution, which is useful when processing large amounts of data.
• Rust has a rich ecosystem of libraries that can be useful for web scraping, such as reqwest for HTTP requests and scraper for HTML parsing.

Cons for web scraping: 

• Rust can be difficult to learn and use because of its security system and strict typing.
• Compared to other languages such as Python, the libraries for web scraping in Rust are less developed, which may require more development time.
• Working with dynamically changing data structures, such as HTML documents, can be more challenging.

Java 

Pros for web scraping: 

• Java code can be executed on various operating systems without modification.
• It has an extensive ecosystem of libraries for networking and HTML parsing, such as Jsoup.
• Java has good performance and scalability, which is important for processing large amounts of data.

Cons for web scraping: 

• Java can be too cumbersome and complex for some web scraping tasks because of its strict typing and voluminous code.
• Compared to some other languages, development in Java can take longer due to the need to write more verbose code.
• Java has less flexibility when dealing with dynamic data structures such as HTML, which can make parsing web pages difficult.

C# 

Pros for web scraping: 

• C# has rich HTML parsing and web scraping capabilities.
• The extensive .NET ecosystem and the availability of libraries such as HtmlAgilityPack make it easy to develop web scrapers.
• C# provides high performance. 

Cons for web scraping: 

• Despite the ability to use .NET on a variety of platforms, C# still has a closer association with Windows, and this can be a limiting factor.
• Some developers find C# more cumbersome and less simple compared to some other web scraping languages.
• Compared to the same Python, the ecosystem for web scraping in C# may be less developed.

Some websites may have restrictions in the form of captchas that must be solved to access page content. Capmonster Cloud service allows you to automatically solve such captchas and continue parsing without interruption. 

To integrate Capmonster Cloud with your code, you will need to follow these steps: 

1. Get Capmonster Cloud API key: register on the Capmonster Cloud website and get an API key. 
2. Install the official CapMonster library for your programming language (Python, JavaScript, C#, Go, PHP).
3. Integrate into your code: use CapMonster Cloud's API key and methods (you can find instructions in documentation) to send a captcha to the solution and get the result.
4. Send the captcha to the solution. After receiving the captcha on the page, send it to the Capmonster Cloud server for the solution.
5. Wait until the Capmonster Cloud server receives the Captcha solution.
6. After receiving the captcha solution, use it to continue parsing the web page.

Sample code for web scraping and captcha traversal using CapMonster Cloud in Python: 

import requests
import time
from bs4 import BeautifulSoup

def solve_recaptcha_v2(api_key, page_url, site_key):
    solve_url = 'https://api.capmonster.cloud/createTask'
    
    task_data = {
        "clientKey": api_key,
        "task": {
            "type": "RecaptchaV2TaskProxyless",
            "websiteURL": page_url,
            "websiteKey": site_key,
            "userAgent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36"
        }
    }
    
    response = requests.post(solve_url, json=task_data)
    
    response_data = response.json()
    task_id = response_data.get('taskId')
    return task_id

def get_recaptcha_solution(api_key, task_id):
    result_url = 'https://api.capmonster.cloud/getTaskResult'
    
    result_data = {
        "clientKey": api_key,
        "taskId": task_id
    }
    
    attempts = 0
    max_attempts = 15
    
    while attempts < max_attempts:
        response = requests.post(result_url, json=result_data)
        response_data = response.json()
        
        if response_data['status'] == 'ready':
            return response_data['solution']['gRecaptchaResponse']
        
        time.sleep(1)
        attempts += 1
    
    print("The number of attempts to obtain a result has been exceeded")
    return None

def parse_site_title(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    title = soup.title.string
    return title

def main():
    api_key = 'YOUR_CAPMONSTER_API'
    page_url = 'https://lessons.zennolab.com/captchas/recaptcha/v2_simple.php?level=low'
    site_key = '6Lcf7CMUAAAAAKzapHq7Hu32FmtLHipEUWDFAQPY'

    task_id = solve_recaptcha_v2(api_key, page_url, site_key)
    print("Task ID:", task_id)

    if task_id:
        captcha_response = get_recaptcha_solution(api_key, task_id)
        print("captcha solution:", captcha_response)

    # Parsing site title 
    zennolab_url = 'https://lessons.zennolab.com/captchas/recaptcha/v2_simple.php?level=low'
    site_title = parse_site_title(zennolab_url)
    print("Site title:", site_title)

if __name__ == "__main__":
    main()

Thus, Capmonster Cloud can be a useful complement to library-based parsers, helping to ensure a smooth and efficient process of collecting data from websites.

Web scraping is a powerful tool for collecting data from the Internet, and choosing the right programming language plays a key role in the effectiveness of this process. After studying various programming languages, we have identified a few optimal choices for scraping. Python stands out as the primary language for web scraping due to its simplicity, library wealth, and wide developer community. Libraries like BeautifulSoup and Scrapy make the scraping process intuitive and efficient. However, depending on the specific requirements of the project, other languages may also be suitable options.

In addition, the article mentions Capmonster Cloud's efficient method of automatic captcha solving, which makes the scraping process much easier by freeing developers from the need to manually enter captchas. Using such tools improves scraping performance, allowing you to focus on the main tasks of the project.

The decision to choose a programming language for web scraping is determined by individual preferences, level of experience, and project specifics. The use of advanced tools also helps to simplify and increase the efficiency of the process.

Note: We'd like to remind you that the product is used for automating testing on your own websites and on websites to which you have legal access.