How to use C++ for Web Scraping [2023Guide]

For large scale web scraping tasks that require optimizing for speed and resource usage, C++ should be your first choice. Its raw efficiency makes it perfect for handling the huge amount of data extraction needed in enterprise use cases.

In this comprehensive guide, you'll discover why C++ is faster than other languages, how to fully utilize its capabilities for scraping, as well as methods for overcoming blocks.

By the end, you'll be able to build high performance scrapers in C++ to extract massive datasets from even the largest websites. Let's get started!

Why Pick C++ for Web Scraping?

Before we dive into how to leverage C++ for scraping, let's first cover the key reasons it's often the best tool for the job:

1. Speed

Due to its low-level nature, C++ runs incredibly fast and makes very efficient use of system resources. Runtimes can be 5-10x faster compared to languages like Python.

Benchmarks show that the same web scraping tasks in C++ finish much quicker:

• C++: Extract 1 GB dataset in 3 minutes
• Python: Extract 1 GB dataset in 20 minutes

This advantage expands exponentially when having to extract huge datasets across thousands of pages.

2. Scalability

With its lighter resource footprint combined with excellent multi-threading support, C++ can easily scale up to handle very demanding scraping loads.

You can deploy massively parallel scrapers on servers to extract hundreds of millions of records from even the largest sites. Trying to do this in Python or JS often crashes due to memory constraints.

3. Control

C++ scrapers leave little up to external dependencies. You operate much closer to the metal with finer grain visibility into data extraction. This makes debugging and performance tuning straightforward.

For enterprise use cases, having full control over the scraping logic is preferred compared to higher level languages that hide internal complexities.

When is C++ the Best Web Scraping Option?

Based on its strengths, here are the key types of scraping scenarios where C++ thrives:

• Extracting massive datasets across thousands of pages quickly
• Low latency data pipelines that need consistently fast runtimes
• Scraping resource constrained sites under heavy loads
• Minimizing infrastructure costs by using resources efficiently
• mission-critical scraping where control over logic and data is paramount

Okay, now that you know why C++ is great for web scraping, let's dive into how to actually build these blazing fast scrapers!

Web Scraping Prerequisites

C++ is a general programming language not specifically designed for web development. So we need some external libraries that handle common scraping tasks:

• libcurl – fast HTTP client for making requests
• libxml2 – HTML/XML parser for analyzing responses

We'll also use C++ data structures like std::vector and file streams for exporting data.

No web framework is necessary! The built-in standard library provides everything we need, keeping things simple and lightweight.

Let's get these set up properly in your environment first.

Setting up C++ and Libraries on Your Machine

You can use any C++ code editor, but I recommend Visual Studio or VS Code for beginners.

Next, download the vcpkg dependency manager. vcpkg will automatically install any C++ libraries we need:

vcpkg install curl libxml2

Finally, include the libraries in your program:

#include <curl/curl.h>
#include "libxml/HTMLparser.h"
#include "libxml/xpath.h"

And that's it! You now have a perfectly configured C++ environment ready for blazing fast web scraping!

How to Build a Web Scraper in C++

Constructing a C++ web scraper involves:

1. Making HTTP requests to download web pages
2. Parsing the HTML responses to extract data
3. Outputting the scraped data to files

Let's understand each step by building a scraper for an ecommerce site.

// Target site
string base = "https://scrapeme.live";

Step 1 – Making HTTP Requests with libcurl

The libcurl library handles all HTTP protocol actions and fetching web pages. But working directly with libcurl involves some messy code. Let's abstract it away into a handy get_page() function:

// Helper to get web pages 
string get_page(string url) {

  // curl easy handle
  CURL* curl = curl_easy_init();

  if(curl) {
    
    curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
    
    // Write response directly to string 
    curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_callback);
    curl_easy_setopt(curl, CURLOPT_WRITEDATA, &response);
    
    // Fetch the remote page
    curl_easy_perform(curl);  
  
    // Always cleanup    
    curl_easy_cleanup(curl);
  
  }

  return response;
}

// Callback writes data to string
size_t write_callback(void *contents, size_t size, size_t nmemb, string *str) {
  // Append new content to string 
  str->append((char*)contents, size * nmemb);  
  return size * nmemb;
}

Now we can simply call get_page() to fetch any URL, with the response stored directly in a string:

string html = get_page(base + "/shop"); // https://scrapeme.live/shop

Easy! With libcurl powering requests under the hood, let's look at parsing content next.

Step 2 – Parsing HTML using libxml2

To extract data, we first need to interpret the HTML response as structured data instead of an unorganized string blob. This is the job of libxml2 – it converts HTML into traversable DOM node trees.

Consider this sample page:

<html>
<body>

<h1>Site Title</h1>

<ul>
  <li>Item 1</li> 
  <li>Item 2</li>
</ul>

</body>
</html>

Libxml2 parses this textual content into an organized tree:

       html
     /    \
    /      \
   /        \
body      head
 |         |  
h1       title
 |
ul
 | \ 
li li

We can now use XPath queries to target elements, like //li to select all list items.

Let's see this in action scraping product listings:

// Parse HTML into traversable DOM tree
htmlDocPtr doc = htmlReadMemory(html.c_str(), html.size(), "", NULL, 0);
  
// Initialize XPath context to navigate tree
xmlXPathContextPtr ctx = xmlXPathNewContext(doc);

// Run XPath query to extract nodes 
xmlXPathObjectPtr nodes = xmlXPathEvalExpression((xmlChar*)"//li[contains(@class,'product')]", ctx);

// Process query results
if(nodes != NULL) {

  for(int i = 0; i < nodes->nodesetval->nodeNr; ++i) {  
  
    // Current node (li.product element)
    xmlNodePtr node = nodes->nodesetval->nodeTab[i];  
    
    // Extract data from node...
  
  } 

}

We select all .product nodes, then loop over them to extract data using more XPath queries targeted under each node.

This gives us fine-grained control to scrape any data we want!

Step 3 – Exporting Scraped Data to CSV

Finally, let's save the scraped details to a file. The simplest method is outputting a CSV:

ofstream outfile("products.csv");

// Write CSV header 
outfile << "name,url,image,price\n";   

for(auto& product : products) {

  outfile << product.name << ","
          << product.url << "," 
          << product.img << ","
          << product.price << "\n";

}

outfile.close();

And we now have a nice CSV file with all extracted products!

This covers the fundamentals of how to scrape the web with C++. Next let's explore more advanced topics.

Advanced Web Scraping in C++

Now that you know the basics, let's dive into some more powerful techniques:

• Web crawling – automatically discovering pages
• Headless browsers – rendering JavaScript sites
• Multithreading – scaling scrapers

Understanding these will allow you to build enterprise-grade extractors.

Web Crawling

To scrape entire websites, you need to recursively follow links to find pages. This is known as “web crawling”.

Here is a simple breadth-first crawler:

vector<string> discovered; // Track visited pages  
queue<string> toCrawl;    // Next pages to crawl  

// Crawl pages by following links
void crawl(string startUrl) {

  toCrawl.push(startUrl);
  
  while(!toCrawl.empty()) {

    string current = toCrawl.front();
    toCrawl.pop();

    if(visited(current)) continue;

    // 1. Fetch current page  
    string html = get_page(current);
    
    // 2. Extract links 
    vector<string> links = get_links(html);

    // 3. Enqueue unseen links  
    for(auto& link : links) {
      if(!visited(link))
        toCrawl.push(link);
    }

    // 4. Mark page visited
    discovered.push_back(current);

  }  
}

This automatically discovers new pages by recursively following links starting from a seed URL.

Expanding on this crawler foundation with extra logic allows building advanced site scrapers.

Headless Browser Automation

Modern sites rely heavily on JavaScript to render content. Normal HTTP requests won't be enough.

Headless browsers like Selenium run a real Chrome/Firefox browser behind the scenes. It loads pages like a user, allowing our C++ code to watch DOM changes and interact with dynamic elements.

The webdriverxx library enables Selenium automation in C++: