Web scraping has become an essential technique for collecting data from websites at scale. While Python often dominates this space due to its simplicity and ecosystem, C++ offers something different: raw performance, memory efficiency, and full system-level control. When scraping workloads become large, speed-critical, or resource-intensive, C++ becomes a powerful alternative.
In this guide, we will explore how to build a C++ web scraper from scratch and understand the libraries involved in the development of the scraper.
Why Use C++ for Web Scraping?
C++ is not the most common choice for web scraping, but it has unique strengths that make it valuable in specific scenarios.
First, C++ is extremely fast. It runs close to the hardware, which makes it ideal for processing large volumes of pages quickly. When scraping thousands or even millions of URLs, performance differences become noticeable compared to interpreted languages.
Second, it provides fine-grained control over memory and threading. This is useful when building high-performance scraping systems that need to run efficiently under heavy loads.
However, this power comes at a cost. C++ is more complex, requires manual setup of libraries, and lacks a unified scraping framework. Unlike Python, there is no “one-stop solution.”
As noted in industry discussions and technical guides, C++ is best used when performance matters more than development speed.
How Web Scraping Works in C++
At a high level, web scraping in C++ follows three steps:
- Send an HTTP request to a webpage.
- Receive and store the TML response.
- Parse the HTML and extract structured data.
Unlike higher-level languages, C++ requires separate tools for each step. Typically, you combine multiple libraries to build a full pipeline.
Essential C++ Web Scraping Libraries
To build a functional scraper in C++, you will need at least two categories of libraries: HTTP clients and an HTML parser.
libcurl (HTTP Requests)
libcurl is the most widely used library for sending HTTP requests in C++. It supports:
- GET and POST requests
- HTTPS connections
- Cookies and sessions
- Proxy support
- Custom headers
It is stable, fast, and used in production systems worldwide.
CRR (C++ Requests Wrapper)
CRR is a modern wrapper over libcurl. It simplifies syntax significantly, making C++ HTTP requests more readable. Instead of verbose libcurl code, CRP lets you write clean request logic similar to Python’s requests library.
libxml2 (HTML Parsing)
Once you fetch HTML, you need to extract data. libxml2 is a powerful parser used for parsing HTML into a DOM tree, running XPath queries, and navigating nodes efficiently. It is extremely fast and suitable for large-scale scraping.
pugixml
pugixml is a lightweight alternative to libxml2, as it is easier to use and often preferred for small to medium scraping projects.
Setting Up a C++ Scraping Environment
Before you start developing the scraper, you need to install the main libraries. These setup steps depend on your operating system.
Windows
The easiest way to manage C++ scraper libraries on Windows is to use vcpkg, which is Microsoft’s open-source package manager.
Installing vcpkg:
git clone https://github.com/microsoft/vcpkg
cd vcpkg
bootstrap-vcpkg.batInstalling the libraries:
vcpkg install curl libxml2 cpr pugixml
vcpkg integrate installmacOS
On macOS, you can use Homebrew to install the required libraries:
brew install curl libxml2 cpr pugixml cmakeIf you’re using CMake, add the Homebrew prefix to your build config:
export PKG_CONFIG_PATH="/opt/homebrew/lib/pkgconfig"Linux
On Linux, use apt to install dependencies:
sudo apt update
sudo apt install libcurl4-openssl-dev libxml2-dev libpugixml-dev cmakeFor CPR, build from source using CMake:
git clone https://github.com/libcpr/cpr.git
cd cpr && mkdir build && cd build
cmake .. && make && sudo make installCmake Configuration
No matter what OS you use, add the following lines to your CMakeLists.txt file:
cmake_minimum_required(VERSION 3.10)
project(CppWebScraper)
set(CMAKE_CXX_STANDARD 17)
find_package(CURL REQUIRED)
find_package(libxml2 REQUIRED)
add_executable(scraper main.cpp)
target_link_libraries(scraper PRIVATE CURL::libcurl xml2)If your project also uses libcurl directly (rather than through CPR), add find_package(CURL REQUIRED) and link CURL::libcurl as well.
How to Build a C++ Web Scraper
This section explains every part of the scraper so you understand not just what to write, but why it works.
1. CMake Project Setup
cmake_minimum_required(VERSION 3.10)
project(CppWebScraper)
set(CMAKE_CXX_STANDARD 17)
find_package(CURL REQUIRED)
find_package(libxml2 REQUIRED)
add_executable(scraper main.cpp)
target_link_libraries(scraper PRIVATE CURL::libcurl xml2)Explanation
These files configure your C++ project build system.
- cmake_minimum_required → sets minimum CMake version required
- project() → names your project
- set(CMAKE_CXX_STANDARD 17) → enables modern C++ features
- find_package(CURL REQUIRED) → finds libcurl for HTTP requests
- find_package(libxml2 REQUIRED) → loads HTML parsing library
- add_executable() → defines your output program
- target_link_libraries() → connects required libraries.
Without this, the scraper can’t compile or link external libraries.
Step 2: Include Libraries
#include <iostream>
#include <fstream>
#include <vector>
#include <string>
#include <thread>
#include <chrono>
#include <cpr/cpr.h>
#include <libxml/HTMLparser.h>
#include <libxml/xpath.h>
#include <libxml/xpathInternals.h>Explanation
This section imports everything your scraper needs:
- iostream: prints output to the terminal
- fstream: writes data to files (CSV)
- vector: stores lists of scraped data
- thread + chrono: adds delays and multitasking
- cpr.h: handles HTTP requests (like browser calls)
- libxml headers: parse HTML and extract data using XPath
Think of this as “toolkit setup” before scraping begins.
Step 3: Fetch HTML from Website
std::string fetchHTML(const std::string& url) {
cpr::Response r = cpr::Get(
cpr::Url{url},
cpr::Header{{"User-Agent", "Mozilla/5.0"}}
);
if (r.status_code != 200) {
std::cerr << "Request failed: " << r.status_code << std::endl;
return "";
}
return r.text;
}Explanation
This function downloads the webpage content.
- Sends a GET request to the target URL
- Adds a User-Agent header to act like a real browser
- Checks if request was successful (status_code == 200)
- Returns raw HTML as a string.
This is the data collection phase of scraping. Without this step, you can’t access any webpage content.
Step 4: Parse HTML into DOM Structure
htmlDocPtr parseHTML(const std::string& html) {
htmlDocPtr doc = htmlReadMemory(
html.c_str(),
html.size(),
nullptr,
nullptr,
HTML_PARSE_NOERROR | HTML_PARSE_NOWARNING
);
return doc;
}xplanation
Web pages are just messy text. This step converts them into a structured format.
- htmlReadMemory(): converts HTML string into a DOM tree
- DOM (Document Object Model): structured representation of a webpage
- Flags ignore minor HTML errors or warnings
- Returns a pointer to the parsed document
Now the scraper can “navigate” the webpage like a tree instead of raw text.
Step 5: Create XPath Context
xmlXPathContextPtr createXPathContext(htmlDocPtr doc) {
return xmlXPathNewContext(doc);
}Explanation
XPath is used to locate elements inside HTML.
- Creates a query environment for the parsed document
- Allows searching elements like:
- product titles
- prices
- links
- Acts like a “search engine inside HTML.”
Without this, you cannot extract structured data easily.
Step 6: Extract Product Titles
std::vector<std::string> extractTitles(xmlXPathContextPtr context) {
std::vector<std::string> titles;
xmlXPathObjectPtr result = xmlXPathEvalExpression(
(xmlChar*)"//h4[@class='product-title']",
context
);
xmlNodeSetPtr nodes = result->nodesetval;
for (int i = 0; i < nodes->nodeNr; i++) {
xmlChar* content = xmlNodeGetContent(nodes->nodeTab[i]);
titles.push_back((char*)content);
xmlFree(content);
}
xmlXPathFreeObject(result);
return titles;
}Explanation
This function extracts product titles from HTML.
- XPath query:
//h4[@class=’product-title’]
selects all <h4> elements with class product-title
- xmlXPathEvalExpression(): runs XPath query
- nodesetval: list of matched elements
- Loop goes through each element:
- extracts text content
- stores in vector
- xmlFree() prevents memory leaks
- returns list of titles
This is the data extraction phase (text scraping).
Step 7: Extract Prices
std::vector<std::string> extractPrices(xmlXPathContextPtr context) {
std::vector<std::string> prices;
xmlXPathObjectPtr result = xmlXPathEvalExpression(
(xmlChar*)"//span[@class='price']",
context
);
xmlNodeSetPtr nodes = result->nodesetval;
for (int i = 0; i < nodes->nodeNr; i++) {
xmlChar* content = xmlNodeGetContent(nodes->nodeTab[i]);
prices.push_back((char*)content);
xmlFree(content);
}
xmlXPathFreeObject(result);
return prices;
}Explanation
This works exactly like title extraction, but targets prices.
- XPath selects:
//span[@class='price']- Loops through all price elements
- Extracts text content
- Stores them in a vector
Now we have structured price data from HTML.
Step 8: Build a Structured Dataset
struct Product {
std::string title;
std::string price;
};
std::vector<Product> buildDataset(
const std::vector<std::string>& titles,
const std::vector<std::string>& prices
) {
std::vector<Product> products;
size_t size = std::min(titles.size(), prices.size());
for (size_t i = 0; i < size; i++) {
products.push_back({titles[i], prices[i]});
}
return products;
}Explanation
This step combines raw lists into structured data.
- struct Product: defines data formats
- Matches titles and prices by index
- Prevents mismatch using min()
- Creates a clean dataset like:
Product 1 → Title + Price
Product 2 → Title + PriceThis converts scraped data into a usable structured form.
Step 9: Save Data to CSV
void saveToCSV(const std::vector<Product>& products) {
std::ofstream file("products.csv");
file << "Title,Price\n";
for (const auto& p : products) {
file << "\"" << p.title << "\","
<< "\"" << p.price << "\"\n";
}
file.close();
std::cout << "CSV file created successfully" << std::endl;Explanation
This writes scraped data into a file.
- Opens CSV file
- Writes header row
- Loops through products
- Saves each row in CSV format
- Adds quotes to handle commas safely
- Closes the file after writing
This is the data storage/export stage.
Step 10: Main Function
int main() {
std::string url = "https://example.com/products";
std::string html = fetchHTML(url);
if (html.empty()) return 1;
htmlDocPtr doc = parseHTML(html);
if (!doc) return 1;
xmlXPathContextPtr context = createXPathContext(doc);
auto titles = extractTitles(context);
auto prices = extractPrices(context);
auto products = buildDataset(titles, prices);
saveToCSV(products);
xmlXPathFreeContext(context);
xmlFreeDoc(doc);
return 0;
}Explanation
This is the full pipeline controller:
- Fetch webpage
- Parse HTML
- Create XPath context
- Extract titles
- Extract prices
- Combine data
- Save CSV
- Free memory
This is the complete scraping workflow from start to finish
Automated Web Scraping with Python
Conclusion
C++ web scraping offers a powerful combination of speed, efficiency, and low-level system control, making it an excellent choice for high-performance data extraction projects. While it requires more setup and coding effort compared to languages like Python, the performance benefits become especially valuable when handling large-scale scraping workloads, multithreaded crawlers, or resource-intensive applications.
