Python vs. C++ for Web Scraping

Learn about the differences between Python and C++ for web scraping
11 min read
C++ vs Python for scraping

If you’re looking to efficiently gather information from the internet, then web scraping is for you. As you begin looking into the various programming languages that promise to get the job done, you’ll find that Python and C++ are popular options, each with their own unique strengths.

Whether you’re just starting out or looking to refine your scraping skills, this article will help you compare Python and C++, focusing on their use in web scraping. By the end, you’ll have all the knowledge you need to select the right language for your web scraping projects.

Python vs. C++

Python is a high-level interpreted language praised for its simplicity and readability. Its clear syntax and dynamic typing make it accessible for beginners and versatile for a wide range of applications, including web scraping.

In comparison, C++ is a middle-level language that gives you both high-level and low-level language features. It excels in scenarios where execution speed and efficient resource management are important. This makes it a go-to choice for tasks like game development and real-time systems. Read our Web scraping with C++ for more info.

Now, let’s dive in and compare the two languages based on a few key features:


For web scraping, Python is well-equipped with libraries such as Beautiful Soup, Scrapy, and Requests. These libraries streamline the process of sending HTTP requests, parsing HTML content, and extracting necessary data. You can find more libraries developed with Python for web scraping on the Python package index website.

In contrast, C++ gives you access to libraries like libxml2 and lexbor, each serving as an important tool for scraping HTML and XML content. These libraries complement curl, which handles network operations in C++, and streamlines tasks such as making HTTP requests and handling data transfer across various protocols. These libraries are particularly beneficial for scenarios requiring detailed control over network interactions.

Both languages have their strengths, and the choice largely depends on the project’s complexity and performance requirements. Python has a simpler syntax and extensive library support, which is ideal for quick development and ease of use. Meanwhile, C++ offers more control and efficient processing, making it suitable for more complex and performance-intensive scraping tasks.

Ease of Learning

As previously stated, Python’s syntax is straightforward and logical, making it easier for beginners to understand and use. Its commands and structure follow a clear and consistent pattern, which resembles everyday writing, simplifying the initial learning process for new programmers.

Consider a basic web scraping task that involves extracting and printing the headlines from a website. The following code snippet demonstrates how you can use Python to handle a basic web scraping task:

import requests
from bs4 import BeautifulSoup

# Request the content of the web page
response = requests.get('')

# Parse the HTML
soup = BeautifulSoup(response.text, 'html.parser')

# Extract and print the headlines
for headline in soup.find_all('h1'):

The code retrieves the HTML content, parses it, and iterates over the h1 tags, printing out the stripped text of each headline.

While Python’s syntax is user-friendly for beginners, C++ has a more complex syntax. This contrast is crucial when it comes to the rapid development and iterative nature of web scraping.

In C++, you’re responsible for managing memory manually, which can be particularly challenging if you’re just starting out. The language’s syntax requires a meticulous approach to programming, with careful attention to pointers, memory allocation, and deallocation to prevent leaks and security vulnerabilities. This complexity often translates into a steeper learning curve and demands a higher level of vigilance in debugging and maintaining your web scraping code.

Here’s how you can start with C++ code to complete a basic web scraping task that involves extracting and printing the headlines from a website:

#include <iostream>
#include <curl/curl.h>
#include <libxml/HTMLparser.h>

// Callback function for handling the data received by libcurl
static size_t WriteCallback(void *contents, size_t size, size_t nmemb, void *userp) {
    // Append the data to the user-provided string
    ((std::string*)userp)->append((char*)contents, size * nmemb);
    return size * nmemb;

int main() {
    CURL *curl;
    CURLcode res;
    std::string readBuffer;

    curl = curl_easy_init();
    if(curl) {
        curl_easy_setopt(curl, CURLOPT_URL, "");
        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &readBuffer);
        res = curl_easy_perform(curl);

        // Parse the HTML and extract headlines...
        // This part of the code would be more complex in C++ and would require
        // using an HTML parsing library like libxml2 to extract the headlines.

    return 0;

This code shows the complexity of C++, highlighting the deeper understanding of the libraries and language constructs required to perform web scraping. The level of memory management is a source of complexity and potential errors, particularly for beginners.

In summary, with Python’s intuitive code and extensive scraping libraries, you can quickly write scripts to scrape websites. C++ requires a deeper understanding of system-level programming, which may slow initial progress but is invaluable for projects demanding meticulous control over processing and memory management.


Python’s versatility shines when it comes to web scraping tasks, where you might encounter a variety of data formats. Its ability to integrate with different databases and tools simplifies the extraction and management of data. Whether you’re working with structured data in relational databases like MySQL or PostgreSQL, leveraging Python’s libraries such as mysql-connector-python or psycopg2 makes these interactions straightforward.

For unstructured data, Python connects to NoSQL databases like MongoDB with pymongo303, handling flexible schemas with ease. Even when you are dealing with in-memory data stores, time series databases, or cloud-based services, Python’s extensive library ecosystem provides the means to interact with these systems efficiently, ensuring you can adapt to any web scraping challenge with the right tools at your disposal.

Python easily integrates with other systems and services, which is convenient for web scraping tasks that require working with web APIs or databases.

Integrating C++ with SQL and NoSQL databases poses unique challenges due to the lack of high-level abstractions present in languages like Python. While C++ provides performance advantages in data processing, it requires direct interaction with databases using specific drivers such as libpqxx for PostgreSQL or MySQL Connector/C++. This integration demands a comprehensive understanding of both C++ and database query languages without the simplifications offered by object-relational mapping (ORM) tools common in other languages.


Python’s vibrant community is an invaluable asset, providing you extensive support through detailed documentationforums, and Q&A sites like Stack Overflow. Whether you’re troubleshooting, seeking advice, or exploring best practices, the likelihood is high that you’ll find existing discussions or documentation to guide you.

Beyond troubleshooting, the Python community actively engages in the creation and maintenance of a vast array of development tools and open source projects. Events such as PyCon, along with numerous local meetups and special interest groups, foster continuous learning and networking opportunities. This ensures that Python developers have access to the latest programming trends and a platform for growth, innovation, and collaboration.

In comparison, the C++ community is helpful when it comes to things like system-level programming, game development, and performance-critical applications. However, when it comes to web scraping, the community is not as focused or extensive as Python’s.

In C++, the available guidance and shared knowledge tend to be more general, covering broader topics in network programming and data parsing without the specific nuances of web scraping. Moreover, the C++ forums and discussion platforms might not have as many dedicated discussions or examples of web scraping projects, making it more challenging for developers to find community support for web scraping tasks.

As a result, developers working with C++ on web scraping projects might find themselves leaning more on individual exploration and less on community-driven insights and solutions.


Python can be slower than other languages because it’s interpreted at runtime rather than compiled. This means Python’s code is executed line-by-line at runtime, which often results in slower performance compared to compiled languages. This may be particularly noticeable when scraping large websites.

In comparison, C++ excels when it comes to performance because of its compiled nature. It runs closer to the hardware, offering faster execution of scraping tasks. In high-volume or complex web scraping operations, the performance of C++ can be a game changer, minimizing execution time and maximizing efficiency. This makes it particularly suited for scenarios where speed is crucial and any delay can impact the overall workflow or data analysis.

Overall, C++ often outpaces Python in raw performance metrics, a factor that can be decisive for scraping in time-sensitive contexts, such as financial data analysis, where real-time scraping is critical. Python, while typically slower, still performs adequately for a broad spectrum of scraping tasks and is favored for its quick script execution and ease of testing. For heavy-duty scraping tasks, particularly where the processing of massive datasets is required, the speed and efficiency of C++ can provide a significant advantage, potentially reducing operation times from hours to minutes.

Memory Consumption

While Python’s user-friendly design streamlines development, it can lead to greater memory usage, which is a critical factor in resource-limited situations. Its dynamic nature—which includes automatic memory management and the use of high-level data types—often results in a larger memory footprint compared to languages that allow for more manual control over memory allocation.

In scenarios where memory efficiency is important, such as in web scraping tasks running on servers with limited memory or in conjunction with other memory-intensive applications, Python’s memory consumption can cause problems. This is particularly relevant when scraping and processing large volumes of data simultaneously as the overhead for managing all the objects and data structures in memory can accumulate quickly.

In contrast, C++ provides direct low-level access to system resources, which allows for granular optimization of performance. This control over hardware interaction is particularly beneficial in web scraping when you need to fine-tune your program for speed and efficiency or when you need to handle tasks that are sensitive to system architecture.

This level of control enables you to tailor web scraping scripts closely to the operating environment, potentially leading to more efficient memory and processor usage. For data-heavy scraping tasks, this can mean the difference between a program that runs smoothly and one that strains system resources.

Real-World Applications

In the world of Python web scraping, the language’s simplicity and extensive library support make it a popular choice for a range of industries. Start-ups and data analysts frequently use Python to gather market intelligence or conduct competitive analysis. It’s also a go-to for digital marketers and social media managers who automate the collection of posts for sentiment analysis. Moreover, Python excels in e-commerce data extraction, where businesses regularly pull product details to monitor pricing strategies.

C++, with its high execution speed, is reserved for more performance-intensive web scraping tasks. It’s particularly valuable in the financial sector, where real-time data scraping can influence trading decisions and even a few milliseconds of delay can be costly. C++ is also preferred for scraping vast catalogs of products from e-commerce giants, managing the heavy data processing load with efficiency. Additionally, in scenarios where resources are constrained, such as embedded systems, C++’s ability to finely control resource usage makes it the language of choice.


Both Python and C++ have their strengths and weaknesses in the context of web scraping. Python is widely regarded as the easier option to learn and use, especially for web scraping tasks, thanks to its specialized libraries and supportive community. C++ offers superior performance, which can be helpful for intensive web scraping needs, but it comes at a cost: it’s harder to learn.

No matter which language you choose, Bright Data provides powerful proxy management tools that enhance the web scraping capabilities of both. With the addition of the Bright Data Serverless Functions, the process becomes even more accessible, offering a graphical interface that makes it easy for newcomers and seasoned developers looking to streamline their scraping projects. Whether you’re after business insights, brand reputation monitoring, or comparative price analysis, leveraging the Bright Data tools can refine your web scraping projects.

Talk to one of our data experts about our different proxy and scraping solutions.

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