How to Scrape eBay Data at Scale: Complete Proxy Solution by Massive

eBay is one of the largest e commerce marketplaces globally, featuring billions of ebay listings and attracting millions of daily visitors. For businesses, eBay product data offers a wealth of valuable data that can be invaluable for business intelligence, market research, and competitive analysis. However, the web scraping process for extracting data from eBay on a large scale poses significant technical challenges due to the platform's advanced anti-scraping mechanisms.

This step by step guide explains how to build an eBay web scraper using Massive's proxy network to efficiently extract eBay listings data while minimizing the risk of being blocked.

Without further ado, let's get started!

Why Scrape eBay Data?

The raw data from the ebay website gives businesses invaluable insights that drive informed decision-making and competitive advantage. Here's how publicly available data from eBay is transforming business strategies:

1. Market Research: By tracking competitor pricing, promotions, and customer preferences in real-time, you can identify gaps in the market and adjust your strategy to outperform competitors.
2. Smart Pricing: Monitoring price fluctuations on eBay allows you to adjust your prices to remain competitive while maintaining healthy margins. This is particularly useful in fast-moving markets where prices change frequently or during seasonal peaks.
3. Product Decisions: Leverage eBay's sales and feedback data to validate your product choices before investing in new inventory. By analyzing what sells well and understanding customer feedback, you can refine your product offerings and time your launches for maximum impact.
4. Inventory Management: eBay data shows clear demand patterns, allowing you to maintain optimal inventory levels. This helps you optimize stock levels, avoid overstocking, and prevent stockouts.

Avoid Getting Blocked While Scraping eBay

Building an eBay scraper is not as easy as it looks. Scraping eBay at scale presents significant challenges due to IP-based rate limiting, CAPTCHAs, and strict anti-scraping measures. When you exceed eBay's request limits, your IP gets blocked or faces CAPTCHAs.

Why Do You Need Proxies for eBay Scraping?

When building an eBay scraper to scrape eBay data at scale, proxies aren't just helpful – they're essential. Using proxies lets you distribute your requests across multiple IP addresses, helping you:

• Avoid getting blocked or throttled
• Maintain consistent scraping speeds
• Prevent IP blacklisting (which can be a huge headache to fix)
• Scale your data collection reliably

Why Choose Massive Proxies for Scraping eBay?

As we have already discussed above, scraping data from eBay is not straightforward. You have to consider using some kind of proxies, and residential proxies are the most preferred. Our residential proxies use IPs from real desktop and mobile devices, making them highly effective at bypassing eBay's anti-scraping measures. Here's what we offer:

1. High Success Rate: Our residential IPs significantly reduce blocking risks, allowing reliable large-scale data collection.
2. Global Access: Collect data seamlessly from any eBay marketplace worldwide using region-specific proxies.
3. Precise Local Data: Target specific cities or countries to get accurate market insights and pricing data.
4. Guaranteed Performance: Enjoy 99% success rates with fast response times and 24/7 uptime monitoring.
5. Flexible Usage: Choose from various bandwidth options to match your scraping needs, whether small-scale or enterprise-level.

💡 Pro Tip: Always use proxies from your target country when scraping different eBay marketplaces - it ensures you get actual local pricing and availability data. Massive Proxies provides location-specific IPs across all major eBay markets for consistent results.

Getting Started with Massive Proxies

If you’re new to Massive, sign up for an account. Choose a plan for your needs.

Note: We offer a 2 GB free trial for companies. To get started, fill out this form. If you need more bandwidth, contact our sales team, and we’ll assist you.

After signing up, go to the Massive Dashboard to retrieve your proxy credentials (username and password).

‍Configuration Steps:

Visit the Quickstart section to customize your proxy settings:

• Choose your preferred protocol (HTTP, HTTPS, or SOCKS5)
• Select between rotating or sticky proxies
• Set geo-targeting preferences (country, state, city, or ZIP code)

Once configured, you'll get a ready-to-use cURL command for your specific use case.

For advanced features like location-based targeting and sticky sessions, refer to the Massive Documentation. The docs provide step-by-step instructions for getting the most out of Massive Residential Proxies.

With this setup, you can use Massive Proxies to scrape eBay data from major marketplaces including the US, UK, Germany, and Australia.

What Product Data Can You Extract from eBay?

Scraping eBay can provide you with a wealth of product information. Here's a comprehensive breakdown of available data:

• Product URL: The link to the eBay product page
• Title: The name of the item
• Subtitle: Additional descriptive text
• Current Price: The current selling price
• Was Price: The previous price (if available)
• Discount: The percentage or amount discounted
• Availability: Quantity available and stock status
• Sold Count: Number of items sold
• Shipping Details: Cost and estimated delivery time
• Location: The shipping origin
• Returns: Return policy details
• Condition: Whether the item is new, used, or refurbished
• Brand: Manufacturer or brand name
• Type: The category or product type
• Seller Information:
  • Seller/store name
  • Customer feedback percentage
  • Total sales by the seller

Required User Inputs

To start scraping eBay, you'll need to specify two key parameters:

1. Target Country: You can extract data from 10 regional eBay domains:

"US": "https://www.ebay.com",
"GB": "https://www.ebay.co.uk",
"DE": "https://www.ebay.de",
"ES": "https://www.ebay.es",
"FR": "https://www.ebay.fr",
"IT": "https://www.ebay.it",
"CA": "https://www.ebay.ca",
"MX": "https://www.mx.ebay.com",
"NL": "https://www.ebay.nl",
"AU": "https://www.ebay.com.au"

2. ‍Search Terms: Enter what you want to scrape (e.g., "recliner chair", "drone camera"). You can input multiple terms using commas.

2. Item Limit: (Optional) Specify how many items to scrape. Skip this to collect all available data across all pages.

The extracted data will be saved in a structured JSON file.

Building an eBay Data Scraper with Massive Proxies

This guide explores step-by-step how to scrape eBay at scale. While we focus on scraping eBay.com (US site), the same principles apply to other country-specific eBay sites with minor adjustments to selectors.

Step #1: Project Setup

First, make sure you have Python 3 installed on your system. If not, download and install it.

Now, create a directory for your project:

mkdir ebay_scraper

Open the project folder in your preferred IDE (like VS Code) and create a file named usa_ebay.py. This file will contain our scraping logic to scrape eBay data.

You'll also need to create a .env file to store your Massive Proxy credentials:

PROXY_USERNAME=your_username
PROXY_PASSWORD=your_password

Now, your project structure should look like this:

ebay_scraper/
├── .env
└── usa_ebay.py

‍

Step #2: Installing Dependencies

To scrape eBay data efficiently, you will need to use several key libraries:

• curl_cffi: A high-performance HTTP client that supports JA3/TLS fingerprinting and HTTP/2
• beautifulsoup4: For HTML parsing
• python-dotenv: For environment variable management
• aiofiles: For asynchronous file operations

Now, you can install these dependencies using pip as shown below:

pip install curl-cffi beautifulsoup4 python-dotenv aiofiles

‍

Step #3: Configuring Massive Proxies

This step sets up a proxy to enable geotargeting, ensuring requests are routed through specific countries for extracting eBay data accurately:

def setup_proxy(self):
    """Configure proxy settings for geotargeted requests"""
    self.proxy_host = "network.joinmassive.com:65534"
    self.username = os.getenv("PROXY_USERNAME")
    self.password = os.getenv("PROXY_PASSWORD")
    self.proxy_auth = f"{self.username}-country-{self.domain}:{self.password}"

‍

Step #4: Request Configuration

Configure HTTP requests using the curl_cffi library. The setup involves two methods:

1. _get_proxy_config: Formats proxy authentication credentials and host details
2. _make_request: Handles HTTP request execution with features like rate limiting and browser emulation

def _get_proxy_config(self) -> Dict[str, str]:
    """Generate proxy configuration dictionary"""
    return {"https": f"http://{self.proxy_auth}@{self.proxy_host}"}


async def _make_request(self, session: AsyncSession, url: str, page_type: str):
    """Make HTTP request with proxy and browser emulation"""
    async with self.semaphore:
        response = await session.get(
            url,
            proxies=self._get_proxy_config(),
            impersonate="chrome124",
            timeout=self.page_timeout,
        )

‍

Step #5: Processing Search Pages

This method _process_search_page manages the processing of individual search result pages. It takes an async session, page number, and search term as inputs. Here's how it works:

The function constructs the search URL by building query parameters that include:

• The search keyword (_nkw)
• Page number (_pgn)
• Items per page setting (_ipg) optimized to 240 items

Then, it makes an async request using the previously configured request method. If the content is retrieved successfully, it parses it using BeautifulSoup with the lxml parser. Then, it extracts product URLs from the parsed HTML and processes the extracted product URLs in batches.

async def _process_search_page(
    self, session: AsyncSession, page_num: int, search_term: str
):
    """Process a single search results page"""
    try:
        params = {
            "_nkw": search_term,
            "_pgn": page_num,
            "_ipg": 240,  # Maximum items per page
        }
        url = self.base_url + urlencode(params)

        status_code, html_content = await self._make_request(session, url, "search")
        if html_content:
            soup = BeautifulSoup(html_content, "lxml")
            urls = self._extract_product_urls(soup)

            logger.info(f"Found {len(urls)} products on page {page_num}")
            return await self._process_product_batch(session, urls)
    except Exception as e:
        logger.error(f"Error processing page {page_num}: {str(e)}")
        return False, False

‍

Step #6: Extracting Product URLs

This method handles the crucial task of extracting product URLs from search result pages. Here's how the product URL extraction works:

The _extract_product_urls method accepts a BeautifulSoup object that contains the parsed HTML content and returns a list of valid product URLs. It implements a focused approach to URL extraction:

• Uses CSS selector a.s-item__link to find all product link elements
• Iterates through each link element to extract the href attribute
• Validates URLs by checking for the presence of itm/ in the URL path
• Builds a filtered list containing only valid product URLs

def _extract_product_urls(self, soup: BeautifulSoup) -> List[str]:
    """Extract product URLs from search results page"""
    urls = []
    for link in soup.select("a.s-item__link"):
        url = link.get("href", "")
        if url and "itm/" in url:
            urls.append(url)
    return urls

‍

Step #7: Scraping Product Details

The _extract_product_details method systematically extracts product information from eBay product pages. It processes a BeautifulSoup object and returns a ProductDetails object containing structured data.

def _extract_product_details(self, soup: BeautifulSoup, url: str) -> ProductDetails:
    """Extract all product details from page"""
    details = ProductDetails(url=url)

    try:
        details.store_info = DataExtractor.extract_store_info(soup)

        # Title section
        if title_div := soup.select_one("div.x-item-title"):
            if title := title_div.select_one("h1.x-item-title__mainTitle span"):
                details.title = title.text.strip()
            if subtitle := title_div.select_one("div.x-item-title__subTitle span"):
                details.subtitle = subtitle.text.strip()

        # Price section
        if price_section := soup.select_one("div.x-price-section"):
            if current_price := price_section.select_one("div.x-price-primary span"):
                details.current_price = current_price.text.strip()
            if was_price := price_section.select_one(
                "span.ux-textspans--STRIKETHROUGH"
            ):
                details.was_price = was_price.text.strip()

            # Discount calculation
            discount = None
            if emphasis_discount := price_section.select_one(
                "span.ux-textspans--EMPHASIS"
            ):
                discount = emphasis_discount.text.strip()
            elif secondary_discount := price_section.select_one(
                "span.ux-textspans--SECONDARY"
            ):
                discount = secondary_discount.text.strip()
            if discount and (percentage_match := re.search(r"(\d+)%", discount)):
                details.discount = percentage_match.group(1) + "%"

        # Quantity section
        if quantity_div := soup.select_one("div.x-quantity__availability"):
            spans = quantity_div.select("span.ux-textspans")
            if spans:
                details.availability = spans[0].text.strip()
                if len(spans) > 1:
                    details.sold_count = spans[1].text.strip()

        # Shipping section
        if shipping_div := soup.select_one("div.d-shipping-minview"):
            if shipping_section := shipping_div.select_one(
                "div.ux-labels-values__values-content"
            ):
                details.shipping, details.location = (
                    DataExtractor.extract_shipping_info(shipping_section)
                )

        # Returns section
        if returns_div := soup.select_one("div.x-returns-minview"):
            if returns_section := returns_div.select_one(
                "div.ux-labels-values__values-content"
            ):
                details.returns = DataExtractor.extract_returns_info(returns_section)

        # Additional details
        if condition_span := soup.select_one(
            "div.x-item-condition-max-view .ux-section__item > span.ux-textspans"
        ):
            details.condition = condition_span.text.strip().split(".")[0] + "."
        if (brand_dl := soup.select_one("dl.ux-labels-values--brand")) and (
            brand_value := brand_dl.select_one("dd .ux-textspans")
        ):
            details.brand = brand_value.text.strip()
        if (type_dl := soup.select_one("dl.ux-labels-values--type")) and (
            type_value := type_dl.select_one("dd .ux-textspans")
        ):
            details.type = type_value.text.strip()
    except Exception as e:
        logger.error(f"Error extracting details from {url}: {str(e)}")
    return details

‍

Step #8: Handling Pagination

The _has_next_page method uses two different approaches to check for pagination:

• First, it looks for a next page link by searching for an anchor tag with type="next" attribute. If this link exists and has a valid href attribute, it confirms the presence of a next page.
• As a fallback mechanism, it also checks for a next button element. This looks for a button with type="next" and verifies if it's not disabled by checking the aria-disabled attribute. If the button exists but isn't disabled, it indicates more pages are available.

def _has_next_page(self, soup: BeautifulSoup) -> bool:
    """Determine if there is a next page of results"""
    next_link = soup.select_one('a[type="next"]')
    if next_link and next_link.get("href"):
        return True
    next_button = soup.select_one('button[type="next"]')
    return not (next_button and next_button.get("aria-disabled") == "true")

‍

Step #9: Data Storage

Finally, save the extracted data to a JSON file:

class FileHandler:
    """Handle file operations with error handling and backups"""

    @staticmethod
    async def save_to_file(filename: str, data: Dict):
        """Save data with automatic backup creation"""
        temp_file = f"{filename}.temp"
        backup_file = f"{filename}.backup"

        try:
            # Create directory structure
            os.makedirs(os.path.dirname(filename), exist_ok=True)

            # Save to temporary file
            async with aiofiles.open(temp_file, "w", encoding="utf-8") as f:
                await f.write(json.dumps(data, indent=2, ensure_ascii=False))

            # Create backup of existing file
            if os.path.exists(filename):
                os.replace(filename, backup_file)

            # Replace with new file
            os.replace(temp_file, filename)

            logger.info(f"Data successfully saved to {filename}")
        except Exception as e:
            logger.error(f"Error saving data: {str(e)}")
            raise

‍

Step #10: Run the Scraper

The complete implementation for scraping eBay across 10 marketplace domains is available on GitHub. The scraper extracts product data and generates a structured JSON file, with each entry containing:

{
    "url": "https://www.ebay.com/itm/294701001393",
    "title": "Manual Recliner Armchair PU Sofa Chair w/ Adjustable Leg Rest & 135° Reclining",
    "subtitle": "Comfortable & Easy to Clean & 360° Swivel & Steel Frame",
    "current_price": "US $228.99",
    "was_price": "US $651.99",
    "discount": "65%",
    "availability": "More than 10 available",
    "sold_count": "93 sold",
    "shipping": "Free shipping - Arrives by Christmas",
    "location": "Wilsonville, Oregon, United States",
    "returns": "30 days returns Buyer pays for return shipping",
    "condition": "A brand-new, unused, unopened, undamaged item in its original packaging (where packaging is applicable).",
    "brand": "Homcom",
    "type": "Recliner Armchair",
    "store_info": {
        "name": "Aosom-Direct",
        "feedback": "97.5% positive feedback",
        "sales": "482K items sold",
    },
}

‍

Conclusion

This guide has shown how to build an eBay scraper that works across different eBay marketplaces. By using residential proxies, you can gather accurate product data while minimizing blocking risks. The approach we've covered makes it possible to reliably collect data from eBay's various regional sites.

If you need more details on proxy configuration or best practices, you'll find everything in the documentation.

Ready to get started? Sign up for Massive Proxies today 🚀