How to Scrape eBay for Rolex Watches Over $15,000?

Did you know web scraping can uncover hidden insights about online marketplaces, like how prices fluctuate or when products are most likely to go on sale?

Web scraping is your personal assistant in tech, which automatically pulls information from websites for you. Think of this as some kind of robot that can navigate through web pages in order to find what you need, keeping it neat for you without the tiring work of copying and pasting. Instead of having to dedicate many hours to gathering data, we leave it to code to get it done in a quick and more accurate manner.

In this project, we plunge into eBay, the enormous online marketplace where people buy and sell just about everything one could think of. More precisely, we will narrow our focus to Rolex watches that cost in excess of $15,000. Why Rolex? It is among the most iconic luxury watch brands, well-renowned for its high-quality, prestigious timepieces. Watches within this price bracket are not only time-telling gadgets but collectibles, investments, and a sign of status.

Scraping data about these ultra-luxury Rolex watches provides a view into one of the most intriguing slices of the luxury watch market on eBay: an opportunity to reveal trends and interesting patterns that help understand what makes high-end items desirable.

We are dividing our web scraping task into two major steps:

• To begin with, we have specialised tools that search eBay to create a list of web addresses (URLs) for the sales of Rolex watches with a price of more than $15,000.

• Getting the Details: We go to all of these URLs to collect relevant information for each watch, including its price, condition, and features.

In our project on web scraping, we are applying various specialised technologies. Each of them plays its unique role in helping us collect and store data about Rolex watches found on eBay. Let’s take a closer look at each of these tools:

Scrapy is our primary web scraping framework. Think of Scrapy as a type of robot that can read web pages. We do tell Scrapy what to fetch, and it goes into the website and collects that for us. Scrapy can handle really loads of web pages really quick. It is like having a super fast reader who can go through hundreds of pages in minutes only. Scrapy is written in Python, which is one of the widely used programming languages, a favorite among many programmers, as they find it easier to understand and write. It means that we describe how we’ll scrape data from web pages and how to process and store it using the Scrapy.

Playwright is a very powerful browser automation tool. Many websites, such as eBay, make heavy use of JavaScript, which can cause pages to change dynamically without reloading. Basic web scrapers really struggle with handling that kind of thing. Playwright is like a web browser we can control through our code. It can click buttons, scroll down pages, and perform other actions just like a real person would, which allows it to scrape information from sections of the page that otherwise might not be viewable.

Scrapy-Playwright is a special plugin which enables smooth cooperation between Scrapy and Playwright. This is the bridge between these two tools. Using Scrapy-Playwright, we can apply the full power of Scrapy in terms of defining our scraping logic, as well as use Playwright when tackling dynamic web pages. This combination is particularly useful when scraping modern websites like eBay that have lots of interactive elements.

SQLite is our database. We will also need a place to put all the data that Scrapy and Playwright help collect. A filing cabinet for our computer is what SQLite represents. It keeps all of the information we have collected neatly organized so it doesn’t get lost later. SQLite is easy to use and does not require a separate server, which makes it an ideal choice for projects like ours. We could use SQLite to save all the detail concerning the Rolex we find so that it shall be easy to work with this data later.

These technologies work together in our project like a well-oiled machine. Scrapy defines how we extract data, interaction with the website as a real user would, these capabilities combine thanks to Scrapy-Playwright, and SQLite stores all the data we collect. Working with all of these tools, we can gather various pieces of information about prices of Rolex watches on eBay and organize them for further analysis.

Data cleaning is part and parcel of our data scraping process. When we collect data from websites, it often comes in inconsistent formats or may contain errors. To make our data more useful and accurate, we use the specific cleaning and organization tools on it:

OpenRefine is a powerful tool for working with messy data. It’s kind of like a smart spreadsheet that can automatically detect and correct common data issues. With OpenRefine, we can easily standardize formats, correct spelling errors, and even merge similar entries. This tool is particularly useful for cleaning up text data, like product descriptions or seller information.

Pandas is a Python library that is great for data manipulation and analysis. Imagine the Swiss Army knife which can handle innumerable data formats, perform complex calculations, and even visualise our data. We use Pandas to clean numerical data — prices, for example — deal with missing values, and transform our data into a form that’s easy to analyze.

By applying these tools, we are assured that the data collected about Rolex watches is valid, consistent, and ready for analysis.

We’ll then proceed with the setup of a Scrapy project and explain how to create a Scrapy project. We will be starting an adventure in web scraping, where we will be collecting and analyzing data about luxury watches on eBay.

Before starting our eBay scraping project, we’ll need to set up our development environment. We’ll be using Scrapy as the main web scraping framework, along with Scrapy Playwright for handling dynamic content.

Open our terminal or command prompt, and if we are using a virtual environment (which is recommended for all Python projects), activate it. Then, execute the following in the terminal or command prompt:

pip install scrapy scrapy-playwright

This command installs both Scrapy and the Scrapy Playwright integration. Scrapy is our core scraping tool, while Scrapy Playwright lets us deal with JavaScript-rendered content, which is ubiquitous on modern websites like eBay.

Next, we want to install the browser binaries that Playwright will use. Run this command:

This downloads and installs the browser engines that need to simulate real browser behavior-which are Chromium, Firefox, and WebKit-needed for scraping sites with dynamic content.

Now that our tools are installed, let’s set up the Scrapy project structure. Within our terminal navigate to the location we want our project created. Then run:

scrapy startproject ebay_watches

The command creates a new directory called `ebay_watches` with Scrapy’s default project structure. It includes a `scrapy.cfg` file and an `ebay_watches` subdirectory containing several python files. The idea behind this structure is that our code will be better organized, following conventions set by Scrappy.

Now, after setting up the project, let’s get into the newly created directory

The scraper will be divided into two spiders. The first spider is responsible for gathering URLs from the Rolex watch page. The second spider then scrapes the detailed data from those URLs.

To run the first spider that collects product URLs, type in this command:

This assumes we’ve already created a spider named `watches` in our `spiders` directory. It should be one that walks eBay’s search results for Rolex watches over $15,000 and scrape the URLs of individual product pages.

Now, when we have our collection of URLs, we run our second spider to scrape out all the detailed product data:

scrapy crawl watches_data

This spider, which we will create and name `watches_data`, should read the URLs found by the first spider and visit each product page extracting such information as price, condition, and features.

Don’t forget to create `spiders` directory with both spider files, `watches.py` and `watches_data.py`. Every spider must define in turn logic for page navigation and data extraction.

By following these steps, we will have a basic Scrapy project set up and ready to scrape data from eBay. The two-spider approach allows for efficient data collection, gathering first of all the URLs and then the more detailed information. As we build our spiders, we will add more specific logic for navigating eBay’s pages and extracting the very data we need about the Rolex watches.

In this chapter, we will discuss the details of implementing our product URL scraper for Rolex watches selling above $15,000 on eBay. We have used the Scrapy-Playwright framework; this framework integrates the mighty capabilities of Scrapy along with the potency of handling dynamic content through Playwright. Our scraping process consists of three major components:

• Spider Code (watches.py) – This spider would have all its logic regarding crawling of web pages, extracting product URLs, and yielding items with a ‘product_url’ field for every found URL.

• Pipeline (pipelines.py) – The configuration file would include the configuration to turn on SQLitePipeline and set in that configurations the database and table names to be used to store the product URLs.

• Settings (settings.py) – This file would carry the SQLitePipeline class, which deals with SQLite database connection along with storing unique product URLs.

Let’s examine each component in detail:

import scrapy
from scrapy_playwright.page import PageMethod

In programming, we often need to use tools that other people have created. That is what we are doing here. We are telling our program to use Scrapy, which is like a Swiss Army knife for web scraping. It has lots of useful tools built-in that make our job easier. We also import something called PageMethod from scrapy_playwright. Playwright will be a tool that will help Scrapy handle websites that make very heavy use of JavaScript, like eBay’s site. Think of Playwright as a puppet master for web browsers – we could control them and make them do what we want. By importing these tools, we get our toolkit ready for the scraping task.

class EbayWatchesSpider(scrapy.Spider):
   """
   Spider for scraping product URLs of Rolex watches listed on eBay for over $150,000.

   This spider uses Scrapy Playwright to handle JavaScript rendering and interact with the web page dynamically. It starts by loading the initial page, waits for the product listings to load, and extracts the URLs of individual product listings. It also handles pagination to scrape product URLs from multiple pages. The extracted URLs are saved to an SQLite database.
   """
   name = "watches"
   allowed_domains = ["ebay.com"]
   start_urls = ["https://www.ebay.com/b/Rolex-Watches/31387/bn_2989578?LH_BIN=1&rt=nc&_udlo=15%2C000&mag=1"]

Here we are creating a blueprint for our spider. In programming lingo, we call this a class. Our class, EbayWatchesSpider, based on Scrapy’s Spider class, means it acquires all the basic spider abilities Scrapy provides. Now, we have to add some specific information for our spider. We will name it “watches,” how Scrapy is going to refer to this spider. The allowed_domains tells our spider it’s only allowed to visit pages on ebay.com-this is how we limit our spider. Finally, start_urls is where we tell our spider where to begin its journey. That long URL is a specific eBay search for Rolex watches priced over $15,000. It’s like giving our spider a starting point on a map.

def start_requests(self):
    for url in self.start_urls:
        yield scrapy.Request(
            url,
            meta=dict(
                playwright=True,
                playwright_include_page=True,
                playwright_page_methods=[
                    PageMethod("wait_for_selector", "div.s-item__wrapper.clearfix"),
                ],
            ),
            callback=self.parse,
        )

This is the first step of the spider. It takes our starting URL defined earlier and makes a very special kind of request. This is not just any kind of request – this is a request that uses Playwright to manipulate a web browser. We instruct Playwright to wait for it to see this part of the page (div.s-item__wrapper.clearfix) before it considers the page ready to be scraped. This is important because eBay contains JavaScript, which will load most of its content, and we won’t scrape until we’re sure those product listings are there. It’s like saying to someone, “Don’t read until the page is fully loaded.” After establishing this special request, we inform it to use our parse function – which we’ll talk about next – to handle the response. This function is returning the request, which in Scrapy means it’s passing the request off to be processed.

async def parse(self, response):
       """
       Parses the response to extract product URLs and handle pagination.

       This method waits for the page content to load, simulates scrolling to trigger lazy-loading of additional content, and then extracts product URLs from the page. If there are more pages to scrape, it recursively follows the pagination links to continue extracting URLs.
      
      Args:
           response (scrapy.http.Response): The response object containing the page content.
       """
       page = response.meta["playwright_page"]

       # Simulate scrolling down the page to trigger lazy-loaded content
       await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")

       # Wait for any lazy-loaded content to appear
       await page.wait_for_timeout(5000)  # Wait 5 seconds after scrolling

       # Wait for the product listings to load on the page
       try:
           await page.wait_for_selector("div.s-item__wrapper.clearfix", timeout=60000)
       except Exception as e:
           self.logger.error(f"Error waiting for products to load: {e}")
           await page.close()  # Close the page to free resources
           return

       # Optional: Log the HTML content length for debugging purposes
       html = await page.content()
       self.logger.info(f"HTML content length: {len(html)}")

       # Extract and yield product URLs from the page
       for product in response.css("div.s-item__wrapper.clearfix"):
           product_url = product.css("div.s-item__image-section > div > a::attr(href)").get()
           if product_url:
               yield {"product_url": product_url}  # Yield the product URL as an item

       # Handle pagination to go to the next page, if available
       next_page = response.css("a.pagination__next::attr(href)").get()
       if next_page:
           self.logger.info(f"Next page URL: {next_page}")
           yield scrapy.Request(
               response.urljoin(next_page),
               callback=self.parse,
               meta=dict(
                   playwright=True,
                   playwright_include_page=True,
                   playwright_page_methods=[
                       PageMethod("wait_for_selector", "div.s-item__wrapper.clearfix"),
                   ],
               ),
           )
       else:
           self.logger.info("No more pages to process.")

       # Close the page after processing to free up resources
       await page.close()

This is where the magic of our actual parse function begins to unravel. It is like a set of instructions for our spider to crawl the page on eBay and collect the information we want. Now, let’s break it down step by step:

First, our spider goes to the bottom of the page. This is important because some websites, like eBay, load more content as we scroll down. It’s like checking to make sure we unrolled the whole scroll before we read it. Then, it waits 5 seconds, or 5000 milliseconds so any new content has a chance to appear.

Next, we instruct our spider to wait for the listings to display. We give it up to a minute to find these listings. If it still couldn’t find any after a minute, then it logs an error message-you know, writing a note in its diary-and stops working on this page, a safety measure to prevent our spider from being stuck in such situations.

Our spider will now start looking for product URLs if everything has loaded correctly. It does this by going through the HTML of the page looking for specific patterns as to how eBay structures their product listings, for each and every one of these product URLs that it finds it harvests them. This is the main purpose of our spider: collecting those URLs.

After it’s iterated over all the items on the page, the spider looks for the “Next Page” link. If it finds such a thing, it makes up a new request to visit that page, and the whole process repeats over again on the new page. This is how our spider can iterate over all the pages of the search results, not just the first one.

Now, if there is no “Next Page” link, our spider realizes that it has gotten to the very end of the result set. It logs a message saying it’s done, like leaving a note saying “finished reading”.

Finally, our spider closes the web page. This is like closing a book when we’re done reading – it helps keep things tidy and frees up computer resources.

During this, our spider is using async operations (as for what ‘async’ and ‘await’ keywords are). A little like multitasking-it allows our spider to do other things while waiting for pages to load or while waiting for something to complete and be ready for the next action.

Our spider uses this parse function as its heart to navigate through eBay’s search results, collect all the URLs for Rolex watches, and make sure it goes through all pages of results. Using Playwright, we can interact with eBay’s website as a human user would, scrolling and waiting for content. So we are able to get accurate and complete results from our scraping.

# Bot and spider configuration
BOT_NAME = "ebay_watches"
SPIDER_MODULES = ["ebay_watches.spiders"]
NEWSPIDER_MODULE = "ebay_watches.spiders"

This part sets up the basic structure of your Scrapy project. It tells Scrapy what to call the bot and where to find spider code. The bot name is “ebay_watches” and spiders are located in the folder “ebay_watches.spiders”. It helps Scrapy know how to organize your web scraping code.

# URL scraping settings
DOWNLOAD_DELAY = 2  # Time delay between requests
CONCURRENT_REQUESTS = 1  # Number of concurrent requests

These settings control how the spider behaves when making requests. It sets a delay of 2 seconds between requests and limits the spider to one request at a time. This helps prevent overloading the target website and makes the scraping more polite.

# Configure Playwright as the download handler
DOWNLOAD_HANDLERS = {
   "http": "scrapy_playwright.handler.ScrapyPlaywrightDownloadHandler",
   "https": "scrapy_playwright.handler.ScrapyPlaywrightDownloadHandler",
}
TWISTED_REACTOR = 'twisted.internet.asyncioreactor.AsyncioSelectorReactor'

This section establishes Playwright as the tool for web-page downloads. Playwright is a browser automation tool that functions better with dynamic websites than Scrapy alone. It is being utilized for both HTTP and HTTPS requests. Additionally, it specifies a special reactor for dealing with asynchronous operations when working with Playwright.

# Set default headers for requests
DEFAULT_REQUEST_HEADERS = {
   "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
   "Accept-Language": "en",
   "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36",
}

These settings define the information sent with each request to make a spider appear more like a real web browser. It may include things like accepted content types, language, as well as a user agent string. This, therefore helps in avoiding those sites that block a spider through a technique called anti-scraping.

# Playwright-specific settings
PLAYWRIGHT_DEFAULT_NAVIGATION_TIMEOUT = 60 * 1000  # 60 seconds timeout
PLAYWRIGHT_BROWSER_TYPE = "chromium"
PLAYWRIGHT_LAUNCH_OPTIONS = {
   "headless": False,  # Run browser in non-headless mode
}

These options configure how play works. That is, it sets a timeout for loading the page and sets the type of browser to Chromium, and sets headless mode to off, meaning that when the spider is running, we can actually see the browser window, which may aid in debugging if needed.

# Scraping behaviour settings
ROBOTSTXT_OBEY = False  # Don't obey robots.txt rules
RETRY_ENABLED = True
RETRY_TIMES = 5  # Number of retries for failed requests
RETRY_HTTP_CODES = [500, 502, 503, 504, 408]  # HTTP codes to retry on

This section determines how the spider behaves. It disables respect for robots.txt files, which is not very polite but may be required for certain projects. It also initializes retry behaviour for failed requests. Each failed request due to certain server errors would be retried up to 5 times.

# AutoThrottle settings
AUTOTHROTTLE_ENABLED = True
AUTOTHROTTLE_START_DELAY = 5
AUTOTHROTTLE_MAX_DELAY = 60
AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0

AutoThrottle automatically throttles the spider’s download speed based on what the website responds with. It prevents downloading too much off of that site and getting banned. It starts at a 5-second delay that can go up to 60 seconds if needed.

# Miscellaneous settings
REQUEST_FINGERPRINTER_IMPLEMENTATION = "2.7"
FEED_EXPORT_ENCODING = "utf-8"
LOG_LEVEL = 'INFO'

These are various other settings for Scrapy. They set the encoding for exported data, the logging level, and a specific implementation for request fingerprinting.

# SQLite pipeline settings
ITEM_PIPELINES = {
   'ebay_watches.pipelines.SQLitePipeline': 300,
}
SQLITE_DB = 'ebay_watches.db'
SQLITE_TABLE = 'product_urls'

This last section creates a pipeline to store the scraped data into a SQLite database. It designates what code to use in the data handling (the SQLitePipeline) and sets the name of the database and table to be used. This means the spider will store the scraped information into a local database to be retrieved at a later time.

The code starts by importing the sqlite3 module. This is a crucial step as it brings in all the necessary tools to work with SQLite databases in Python.

class SQLitePipeline:
   """
   A Scrapy pipeline for storing product URLs in a SQLite database.

   This pipeline creates a SQLite database (if it doesn't exist) and a table
   to store unique product URLs. It handles the database connection,
   insertion of new URLs, and proper closure of the database connection.

   Attributes:
       db_name (str): The name of the SQLite database file.
       table_name (str): The name of the table to store product URLs.
       conn (sqlite3.Connection): The SQLite database connection.
       cursor (sqlite3.Cursor): The database cursor for executing SQL commands.
   """

Now, we define the SQLitePipeline class. It is designed to work within the Scrapy framework as a pipeline for processing scraped data. In Scrapy, pipelines are aimed at processing items right after they have been scraped by spiders. In this case, our pipeline is devoted to storing URLs of products in a SQLite database. A high-level docstring in the class describes exactly what the pipeline does. This is particularly useful for developers who may need to use or maintain this code. In this case, the pipeline does the following: it will create, if they do not already exist, a SQLite database and a table where unique product URLs will be stored. Docstring also lists primary attributes of the class, which will be handy for quick reference as to what data the class will work with.

 def __init__(self, db_name, table_name):
       """
       Initialise the SQLitePipeline.

       Args:
           db_name (str): The name of the SQLite database file.
           table_name (str): The name of the table to store product URLs.
       """
       self.db_name = db_name
       self.table_name = table_name

The init method in our SQLitePipeline class is its constructor, which gets called during the creation of a new instance of the class. There are two parameters for this method, namely db_name and table_name. These have enabled us to specify the name of the SQLite database file and the name of the table where we would be storing our product URLs. Accepting these as parameters makes our pipeline more flexible-it may be easily used with different database and table names without changing the code at all. It just stores these values as instance attributes, and they are thus available to be used in the other methods in the class.

  @classmethod
   def from_crawler(cls, crawler):
       """
       Create a pipeline instance from a Crawler.

       This class method is used by Scrapy to create an instance of
       the pipeline. It uses the Scrapy settings to get the database
       and table names.

       Args:
           crawler (scrapy.crawler.Crawler): The crawler that uses this pipeline.

       Returns:
           SQLitePipeline: An instance of the pipeline.
       """
       return cls(
           db_name=crawler.settings.get('SQLITE_DB', 'ebay_watches.db'),
           table_name=crawler.settings.get('SQLITE_TABLE', 'product_urls')
       )

from_crawler designate the method as class method, denoted by decorator @classmethod. Scrapy uses this to create an instance of our pipeline using the Crawler object. In Scrapy, all related information to the scraping process are kept in the Crawler object. This method fetches database and table names from the settings of the crawler object, with fallback default values when such settings are not defined. This method allows configuring the pipeline through the settings of the Scrapy project. This pattern is common in Scrapy projects. This approach makes the pipeline more flexible and easier to configure without changing the code.

 def open_spider(self, spider):
       """
       Open database connection when spider is opened.

       This method is called by Scrapy when the spider is opened. It
       establishes a database connection and creates the table if it
       doesn't exist.

       Args:
           spider (scrapy.Spider): The spider being opened.
       """
       # Establish database connection
       self.conn = sqlite3.connect(self.db_name)
       self.cursor = self.conn.cursor()

       # Create table if it doesn't exist
       self.cursor.execute(f'''
           CREATE TABLE IF NOT EXISTS {self.table_name} (
               id INTEGER PRIMARY KEY AUTOINCREMENT,
               product_url TEXT UNIQUE
           )
       ''')
       self.conn.commit()

open_spider method is called by Scrapy when a spider gets run. This is where we create our database connection. In order to create our database connection, we use sqlite3.connect function that connects us to our SQLite database file. SQLite will automatically build the file if it doesn’t already exist. We also acquire a cursor object that we will make use of to execute SQL commands. We then run a SQL command to create our table if it doesn’t already exist. The table has two columns: an auto-incrementing ID and a unique product URL. By using “IF NOT EXISTS” in our SQL, we ensure that we don’t get errors if the table already exists from a previous run. Finally, we commit our changes to the database. This helps ensure that our database and table are ready to accept data before the spider begins its crawl.

 def close_spider(self, spider):
       """
       Close database connection when spider is closed.

       This method is called by Scrapy when the spider is closed.
       It ensures that the database connection is properly closed.

       Args:
           spider (scrapy.Spider): The spider being closed.
       """
       self.conn.close()

The close_spider method is the counterpart of open_spider. Scrapy calls it when a spider finishes running. Its job is simple but important: closing the database connection. This is crucial in proper resource management. If we didn’t close the connection, we might leave the database in some possibly inconsistent state or resource leakages. Obviously, closing the connection explicitly when we’re done with it ensures that all our data is saved properly and that we’re good stewards of system resources.

def process_item(self, item, spider):
       """
       Process a scraped item.

       This method is called for every item pipeline component. It
       inserts the product URL into the database if it's not already
       present.

       Args:
           item (scrapy.Item): The item scraped by the spider.
           spider (scrapy.Spider): The spider which scraped the item.

       Returns:
           scrapy.Item: The processed item.
       """
       # Insert the product URL into the database, ignoring if it already exists
       self.cursor.execute(f'''
           INSERT OR IGNORE INTO {self.table_name} (product_url)
           VALUES (?)
       ''', (item['product_url'],))
       self.conn.commit()

       return item

The heart of our pipeline is the process_item method. Scrapy calls this method for every item our spider scrapes. In this method, we take the product URL from the scraped item and insert it into our database. We use a SQL INSERT OR IGNORE statement, which is a nice SQLite feature. This will add the URL to the database if it is not already there but will not give an error if it is already in the database. This is perfect for ensuring we only store unique URLs. After executing the insert, we commit the change to the database to ensure it’s saved. Finally, we return the item, which allows it to be processed by any subsequent pipelines in the Scrapy project.

This SQLitePipeline is the right way to get unique product URLs and store them as part of a Scrapy spider workflow. The SQLitePipeline gets around database connection issues, maintains data integrity, and blends well with Scrapy’s pipeline system.

We will now go into the implementation of our products data scraping system for Rolex watches priced over $15,000 in eBay. As we have previously discussed, we are utilizing the Scrapy framework, to efficiently extract and organize data from website. We define our scraping process in three main components:

• Spider(watches_data.py): The spider would comprise the logic for crawling eBay watch listings, extracting detailed information about each watch, and yields EbayWatchesItem instances featuring url, title, sale_price, price, discount, condition, shipping_charge, returns, and details.

• Items(items.py): The items.py contains a class called EbayWatchesItem inheriting from scrapy.Item. All those fields are declared within this class for url, title, sale_price, price, discount, condition, shipping_charge, returns, and details regarding such eBay watch listings.

• Settings(settings.py): there should be configurations to enable the SQLitePipeline, database and table names for saving the watch data, etc; probably with settings specific for scraping on eBay (for example, about user agents or request delay).

• Pipelines(pipelines.py): The pipelines.py file would hold the SQLitePipeline class. This class should be implemented such that it can handle all fields in EbayWatchesItem, not just the product URL. It will manage connections to the SQLite database and hold complete watch listing data.

Let’s examine each part in detail:

import scrapy
import sqlite3
from ebay_watches.items import EbayWatchesItem

This section is like packing our bag before a trip. We’re bringing in the tools we need for our web scraping job. Scrapy is our main tool for web scraping. It’s like a swiss army knife for getting information from websites. SQLite3 helps us work with our database, where we stored the links to the watch pages. It’s like a filing cabinet where we keep important information. EbayWatchesItem is something we created earlier to hold all the details about each watch. Think of it as a form we’ll fill out for each watch we find.

class SQLiteUrlSpider(scrapy.Spider):
   """
   A spider that crawls eBay watch listings using URLs stored in a SQLite database.

   This spider fetches unscraped URLs from a specified table in the database,
   crawls each URL, extracts relevant information about the watch listing,
   and yields an EbayWatchesItem for each listing.

   Attributes:
       name (str): The name of the spider.
       db_name (str): The name of the SQLite database file.
       url_table (str): The name of the table containing product URLs.
   """

   name = "watches_data"

   def __init__(self, *args, **kwargs):
       """
       Initialize the SQLiteUrlSpider.

       Args:
           *args: Variable length argument list.
           **kwargs: Arbitrary keyword arguments.
       """
       super().__init__(*args, **kwargs)
       self.db_name = 'ebay_watches.db'
       self.url_table = 'product_urls'

Our SQLiteUrlSpider is a specialized robot designed for a specific task. We give it a name, “watches_data”, and this name is how we’ll refer to this spider when we want to use it. This name is important because if we have multiple spiders, we need to identify them. It is just like giving a name to each of our tools, and that’s why we know which to grab when we need it.

When we make our spider, we have to initialize it with some basic info. This is done in the __init__ method. Think of it as like a setup manual for the spider. We give it two important pieces of information: the name of our database file, (ebay_watches.db); and the name of the table in that database where we stored our URLs (product_urls). We’re essentially giving our robot a location to find its list of tasks. The database is just as if it were a huge book of information, and the table is basically a page in that book where we have all the web addresses we would like to visit.

We’re making our spider smart and efficient by programming it this way. Instead of hardcoding a list of URLs or searching the entire eBay website, our spider knows exactly where to look for the information it needs to start its job. This also makes our spider flexible enough; if we wish to scrape a different set of URLs in the future, we will only need to change the database or table name and our spider will automatically update without our having to re-write its code.

  def start_requests(self):
       """
       Generate initial requests for the spider.

       This method connects to the SQLite database, fetches unscraped URLs,
       and yields a Request object for each URL.

       Yields:
           scrapy.Request: A request object for each unscraped URL.
       """
       conn = sqlite3.connect(self.db_name)
       cursor = conn.cursor()

       # Fetch unscraped URLs from the database
       cursor.execute(f"SELECT product_url FROM {self.url_table} WHERE scraped = 0")
       urls = cursor.fetchall()
       conn.close()

       for url in urls:
           yield scrapy.Request(url=url[0], callback=self.parse, errback=self.errback)

The start_requests function is where our spider actually starts its journey. Imagine this as the spider waking up and checking its to-do list. It first opens up the database we told it about earlier. That is like opening a book to the exact page where we wrote down all the web addresses we want to visit. The spider then requests the database to provide it with all those URLs that have yet to be scrapped. Just like when one goes through their checklists and crosses out those which are completed to only view the ones that are yet to be done.

Once the spider has its list of URLs, it closes the database. Good practice: we don’t leave our book lying open for when we are done with it, let’s close it-thus making things neat and preventing any accidental changes. Then the spider makes a special request for every URL on the list. Every request is like a particular mission: “Go to this web page, and when we’re done, apply the ‘parse’ function to understand what we found.” The spider also has a backup plan: if something bad happens while visiting a page, it knows how to use the ‘errback’ function to handle the problem.

This function is very important because it is where we transform our list of URLs into actual web scraping activities. It is efficient because it only looks at URLs it hasn’t scraped before, thus saving time and preventing duplicate work. By yielding each request one at a time, we’re also being gentle with the website we’re scraping – instead of bombarding it with all our requests at once, we’re spacing them out, which is more polite and less likely to get us blocked.

def parse(self, response):
       """
       Parse the response and extract watch listing information.

       This method creates an EbayWatchesItem and populates it with data
       extracted from the response using CSS selectors.

       Args:
           response (scrapy.http.Response): The response to parse.

       Yields:
           EbayWatchesItem: An item containing the extracted watch listing information.
       """
       item = EbayWatchesItem()
      
       # Extract data using CSS selectors
       item['url'] = response.url
       item['title'] = response.css('div.vim.x-item-title > h1 > span::text').get()
       item['sale_price'] = response.css('div.x-price-primary > span::text').get()
       item['discount'] = response.css('div.x-price-transparency > span.x-price-transparency--discount > span.ux-textspans.ux-textspans--EMPHASIS::text').get()
       item['price'] = response.css('div.x-price-transparency > span.x-price-transparency--discount > span.ux-textspans.ux-textspans--SECONDARY.ux-textspans--STRIKETHROUGH::text').get()
       item['condition'] = response.css('#mainContent > div.vim.d-vi-evo-region > div.vim.x-item-condition.mar-t-20 > div.x-item-condition-text > div > span > span:nth-child(1) > span::text').get()
       item['shipping_charge'] = response.css('#mainContent > div.vim.d-vi-evo-region > div.vim.d-shipping-minview.mar-t-20 > div > div > div > div:nth-child(1) > div > div > div.ux-labels-values__values.col-9 > div > div > span.ux-textspans.ux-textspans--BOLD::text').get()
       item['returns'] = response.css('#mainContent > div > div.vim.x-returns-minview.mar-b-20 > div > div > div > div > div > div.ux-labels-values__values.col-9 > div > div > span:nth-child(1)::text').get()
      
       # Extract additional details using a separate method
       item['details'] = self.extract_details(response)

       yield item

Our spider mainly does the following work in the parse function. After visiting any web page, this helps it to understand what exactly it sees. To begin with, it creates a brand new EbayWatchesItem. Think of that as a form we’re going to fill out about the watch. Think of that like a checkbox list of all the information we want to collect. The URL, the title of the listing-the price, and so forth.

We employ a thing called CSS selectors when completing this form. It’s like we’re sending some instructions on where to locate certain bits of information on the page. Imagine telling our spider, “The title is in the huge header at top,” or “The price is within the box on the right-hand side of the page.” The spider then follows those instructions and writes down the information found. This is a very effective technique because it enables us to target what we want specifically, with the rest of the page being irrelevant.

Then, we use the extract_details function to grab still more details about the watch. This is like turning the form over and doing the back side, filling in all the additional detail. Last but not least, we hand that completed form over to Scrapy using yield. This is like turning in our completed homework. Scrapy will now know what to do with it next, whether it’s saving it to a file or passing it through for further processing, or putting it in a database.

 def extract_details(self, response):
       """
       Extract additional details from the watch listing page.

       This method parses the details section of the listing page and
       creates a dictionary of key-value pairs for additional watch details.

       Args:
           response (scrapy.http.Response): The response to parse.

       Returns:
           dict: A dictionary containing additional details about the watch.
       """
       details_dict = {}
       details = response.css('div.ux-layout-section-evo__row > div.ux-layout-section-evo__col')
      
       for div in details:
           dt_text = div.css('dt.ux-labels-values__labels span.ux-textspans::text').get()
           dd_text = div.css('dd.ux-labels-values__values span.ux-textspans::text').get()
          
           # Only add to the dictionary if both key and value are present
           if dt_text and dd_text:
               details_dict[dt_text.strip()] = dd_text.strip()
      
       return details_dict

The extract_details function is like a detective searching for clues. Its job is to find and organize all the extra information eBay has available about the watch. This is typically found in a certain section of the webpage, often in a table or list. Our function knows where to find that treasure trove of details.

This process will pass over this portion of the page, looking for an information pair-a keyword like “Brand” or “Model” and its corresponding value like “Rolex” or “Submariner”. It is reading a list of facts about the watch. Each time it comes across one of these pairs, it adds it to a special list called a dictionary. In our dictionary, the label becomes the key, and the value becomes, well, the value. This is a good way of presenting the information because, later on, we will easily be able to look up specific details about the watch.

Our function then returns this dictionary after going through all the details on the page. This is like handing over a neatly organised list of facts about the watch. Back in our main parse function, we add this dictionary to our EbayWatchesItem under the ‘details’ field. This way, we capture both the main information about the listing – like price and condition – and all these extra details in one comprehensive package.

def errback(self, failure):
       """
       Handle errors that occur during the crawling process.

       This method is called when an error occurs while processing a request.
       It logs the error and updates the crawler statistics.

       Args:
           failure (Failure): A twisted.python.failure.Failure object that encapsulates the error information.
       """
       # Extract error information
       url = failure.request.url
       error_message = str(failure.value)
      
       # Update crawler statistics
       self.crawler.stats.inc_value('failed_urls')
       self.crawler.stats.inc_value(f'failed_urls/{failure.value.__class__.__name__}')
      
       # Log the error
       self.log(f"Error on {url}: {error_message}")
      
       # Use the pipeline to log the error if the method exists
       pipeline = self.crawler.engine.scraper.itemproc._middleware[0]
       if hasattr(pipeline, 'log_error'):
           pipeline.log_error(url, error_message)

The errback function is the safety net to our spider. After all, things don’t always go according to plan when we have to deal with web scraping. The page might not load, the website could be down, or it mightn’t work in the format that we expect. This function catches those problems and handles them gracefully.

If there’s an error, this function springs into action, marking down what URL caused the error and what the error was itself. It is like a log of issues we encounter. It’s extremely useful for debugging later on; we can go back and see exactly what went wrong and where. The function also updates certain counters so that we can track how many errors we’ve had and what sort they are. This gives us a big-picture view of how well our scraping is going.

Finally, if we’ve established an special way of recording errors (that’s the ‘pipeline’ part), it uses that to make a detailed note of the error. This might involve writing to a log file or updating a database. The point here is that we’re not ignoring the fact that, somewhere along the line, something bad occurred. We’re acknowledging them, recording them, and setting ourselves up to deal with them. This would make our spider stronger and more reliable enough to cope with the nonpredictable nature of web scraping.

import scrapy

class EbayWatchesItem(scrapy.Item):
   """
   Defines the structure for storing data about a Rolex watch listing on eBay.

   This item contains fields for various details of a watch listing, including
   its URL, title, pricing information, condition, shipping details, and more.
   Each field is defined as a scrapy.Field(), which allows Scrapy to process
   and store the data efficiently.
   """

   # The URL of the eBay listing
   url = scrapy.Field()

   # The title of the watch listing
   title = scrapy.Field()

   # The current sale price of the watch
   sale_price = scrapy.Field()

   # The original price of the watch (if discounted)
   price = scrapy.Field()

   # The discount amount or percentage (if applicable)
   discount = scrapy.Field()

   # The condition of the watch (e.g., "New", "Used")
   condition = scrapy.Field()

   # The shipping charge for the watch
   shipping_charge = scrapy.Field()

   # The return policy for the watch
   returns = scrapy.Field()

   # Additional details about the watch (stored as a dictionary)
   details = scrapy.Field()

In our web scraping project, we need a method to structure the information we collect about each Rolex watch. We do this using something called an EbayWatchesItem. Think of this item as a special container or a form that has a spot for each piece of information we want to gather. We create the container by defining a class inheriting from scrapy.Item, which has some special powers when working with Scrapy. Inside this class, we list out all the pieces of information we wish to collect: for instance, the URL of the listing, the title of the watch, its price, condition, etc. Each of these is called a field, and we create them using scrapy.Field().

Thus, setting up our data like this will make all the watches easier to scrape, as it provides a standard structure that we can easily populate upon finding a watch. It will be as if we fill out one standard form for all of them. In addition, this will make it far easier to deal with all this data afterwards, as we do know that every watch will have a ‘title’ field, a ‘price’ field and more. We also have a special ‘details’ field that can contain extra information, which might vary for each watch. This setting keeps our data organized in that we can store it without much hassle or easily analyze it later to perform whatever operations we need with the information once collected.

import random
# Bot and spider configuration
BOT_NAME = "ebay_watches"
SPIDER_MODULES = ["ebay_watches.spiders"]
NEWSPIDER_MODULE = "ebay_watches.spiders"

This sets up the bot’s name and where to find the spider code. It tells Scrapy what to call our bot and where to look for the spiders that do the actual scraping.

# Respect robots.txt rules
ROBOTSTXT_OBEY = True
# Configure maximum concurrent requests performed by Scrapy
CONCURRENT_REQUESTS = 1
# Configure a delay for requests for the same website (default: 0)
DOWNLOAD_DELAY = 1

These settings make our bot behave nicely. It follows the rules set by websites, only makes one request at a time, and waits a second between requests to avoid overwhelming the server.

# Disable cookies (enabled by default)
COOKIES_ENABLED = False
# Disable Telnet Console (enabled by default)
TELNETCONSOLE_ENABLED = False

This turns off cookies and the telnet console. It makes our bot act less like a typical browser, which can sometimes help avoid detection.

# List of User-Agent strings to rotate through
USER_AGENTS = [
   'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3',
   'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36',
   'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:57.0) Gecko/20100101 Firefox/57.0',
   'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0',
]

# Configure default request headers
DEFAULT_REQUEST_HEADERS = {
  'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
  'Accept-Language': 'en',
  "User-Agent": random.choice(USER_AGENTS),
}

This sets up a list of different browser identities. Our bot will randomly pick one of these for each request, which helps it look more like a real user.

# Enable and configure the AutoThrottle extension (disabled by default)
# AutoThrottle extension adjusts download delays dynamically
RETRY_ENABLED = True
RETRY_TIMES = 5
RETRY_HTTP_CODES = [500, 502, 503, 504, 408]

If a request fails due to server errors, this tells our bot to try again. It will retry up to 5 times for specific error codes, which helps deal with temporary server issues.

# SQLite database settings
ITEM_PIPELINES = {
   'ebay_watches.pipelines.SQLitePipeline': 300,
}
SQLITE_DB = 'ebay_watches.db'
URL_TABLE = 'product_urls'
DATA_TABLE = 'product_data'
ERROR_TABLE = 'scraping_errors'

This sets up a SQLite database to store the scraped data. It defines tables for storing product URLs, actual product data, and any errors that occur during scraping.

import sqlite3
import json

Here we import two very powerful tools: sqlite3 and json. sqlite3 can be envisioned as compact, portable filing cabinet that fits inside our code; one can imagine it organizes data into tables, much like spreadsheets. The json tool is like a universal translator for complicated data. The two tools help us handle information that can be nested or of several parts by simplifying it into a straightforward string that we can store and retrieve easily. These two tools working together help us handle all forms of data in our web scraping project.

class SQLitePipeline:
   """
   A Scrapy pipeline for storing scraped data in a SQLite database.

   This pipeline handles the storage of product data, URL tracking, and error logging
   in separate tables within a SQLite database. It provides methods for initializing
   the database connection, creating necessary tables, processing scraped items,
   and logging errors.

   Attributes:
       db_name (str): Name of the SQLite database file.
       url_table (str): Name of the table storing product URLs.
       data_table (str): Name of the table storing scraped product data.
       error_table (str): Name of the table for logging scraping errors.
       conn (sqlite3.Connection): SQLite database connection object.
       cursor (sqlite3.Cursor): SQLite database cursor object.

   Methods:
       from_crawler: Class method to create a pipeline instance from a crawler.
       open_spider: Opens the database connection and initializes tables.
       close_spider: Closes the database connection.
       process_item: Processes and stores a scraped item in the database.
       log_error: Logs an error message associated with a URL.
   """

   def __init__(self, db_name, url_table, data_table, error_table):
       """
       Initialize the SQLitePipeline with database and table names.

       Args:
           db_name (str): Name of the SQLite database file.
           url_table (str): Name of the table storing product URLs.
           data_table (str): Name of the table storing scraped product data.
           error_table (str): Name of the table for logging scraping errors.
       """
       self.db_name = db_name
       self.url_table = url_table
       self.data_table = data_table
       self.error_table = error_table

The SQLitePipeline class and its __init__ method are like preparing a new office for our data processing needs. When we create a new instance of this class, we are, in effect, throwing open the shop doors. The class’s init method is like the blueprint for our office layout. We choose to name our main database (db_name), which is like choosing the building for our office. Then we specify some names for different tables – url_table, data_table, error_table – as if they were different departments in our office. In a table dedicated to one particular task, for instance, one table tracks the URLs we visit, another stores the actual data we obtain, and another tracks all errors encountered. By setting them up in the initializer, we are ensuring that every instance of our SQLitePipeline has everything prepared for dealing with all aspects of our data management needs.

 @classmethod
   def from_crawler(cls, crawler):
       """
       Create a pipeline instance from a crawler.

       This class method allows Scrapy to instantiate the pipeline with
       settings defined in the crawler's configuration.

       Args:
           crawler (scrapy.crawler.Crawler): The crawler instance.

       Returns:
           SQLitePipeline: An instance of the pipeline.
       """
       return cls(
           db_name=crawler.settings.get('SQLITE_DB', 'ebay_watches.db'),
           url_table=crawler.settings.get('URL_TABLE', 'product_urls'),
           data_table=crawler.settings.get('DATA_TABLE', 'product_data'),
           error_table=crawler.settings.get('ERROR_TABLE', 'scraping_errors')
       )

The from_crawler method is like having a smart office manager who can set up our whole operation based on a set of instructions provided (that would be our crawler settings). This method looks into the settings of the crawler, something like a company policy document, and figures out how to set up our SQLite Pipeline. It checks for specific instructions about what to name our database and tables. If it finds these directions, it uses them. Otherwise, it has some reasonable default names. It is very useful since it will allow us to easily adjust how our pipeline is configured by only adjusting the crawler settings and not by having to dive into the code itself. It’s like organizing the entire office just by updating a single document.

def open_spider(self, spider):
       """
       Open database connection and initialize tables when the spider opens.

       This method is called when the spider is opened. It establishes a database
       connection, creates necessary tables if they don't exist, and adds a 'scraped'
       column to the URL table if it's not present.

       Args:
           spider (scrapy.Spider): The spider instance.
       """
       self.conn = sqlite3.connect(self.db_name)
       self.cursor = self.conn.cursor()
      
       # Check if 'scraped' column exists in the URL table, if not, add it
       self.cursor.execute(f"PRAGMA table_info({self.url_table})")
       columns = [column[1] for column in self.cursor.fetchall()]
       if 'scraped' not in columns:
           self.cursor.execute(f'''
               ALTER TABLE {self.url_table} ADD COLUMN scraped INTEGER DEFAULT 0
           ''')
      
       # Create the product data table if it doesn't exist
       self.cursor.execute(f'''
           CREATE TABLE IF NOT EXISTS {self.data_table} (
               id INTEGER PRIMARY KEY AUTOINCREMENT,
               url TEXT UNIQUE,
               title TEXT,
               sale_price TEXT,
               price TEXT,
               discount TEXT,
               condition TEXT,
               shipping_charge TEXT,
               returns TEXT,
               details TEXT
           )
       ''')

       # Create the error logging table if it doesn't exist
       self.cursor.execute(f'''
           CREATE TABLE IF NOT EXISTS {self.error_table} (
               id INTEGER PRIMARY KEY AUTOINCREMENT,
               url TEXT,
               error_message TEXT,
               timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
           )
       ''')

       self.conn.commit()

open_spider is kind of like the morning routine; we are opening the office for business for the first time. First, it opens a connection to our database-unlocking the main door and switching on the lights, so to speak. Then it runs a series of checks and setups. It is scanning our URL tracking table, ensuring that it has a column that marks whether a URL has been scraped or not. This column gets added if such does not exist in the table – it’s somewhat like having an epiphany that we need a new filing category and then quickly doing so to make sure all is on track. It also scans for tables for the product data and errors we might encounter. If these tables don’t exist yet, it will create them. It’s kind of like setting up new filing cabinets if we have to because we don’t have what we need. At the end of this process, our whole data storage system is now in place and operational, ready to digest whatever data our spider finds out.

 def close_spider(self, spider):
       """
       Close the database connection when the spider closes.

       Args:
           spider (scrapy.Spider): The spider instance.
       """
       self.conn.close()

Our end-of-day routine is the close_spider method. After all the busy work in scraping and storing data, this method closes everything down properly. Its main job is to close the connection to our database. That’s very important because it ensures that all our data is properly saved as well as the closing of open connections that may cause problems later on. It’s like locking all the filing cabinets, shutting down computers, and locking up the office door before we leave. It is a mundane but very important step in keeping our data safe and running smoothly with our system.

  def process_item(self, item, spider):
       """
       Process a scraped item and store it in the database.

       This method inserts or replaces the scraped item data in the product data table
       and updates the 'scraped' status in the URL table.

       Args:
           item (dict): The scraped item containing product data.
           spider (scrapy.Spider): The spider instance.

       Returns:
           dict: The processed item.
       """
       # Convert the dictionary to a JSON string before saving it to the database
       details_json = json.dumps(item['details'])
      
       # Insert the item data into the product data table
       self.cursor.execute(f'''
           INSERT OR REPLACE INTO {self.data_table}
           (url, title, sale_price, price, discount, condition, shipping_charge, returns,
           details)
           VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
       ''', (item['url'], item['title'], item['sale_price'], item['price'], item['discount'],
             item['condition'], item['shipping_charge'], item['returns'], details_json))
      
       # Update the 'scraped' status in the URL table
       self.cursor.execute(f'''
           UPDATE {self.url_table} SET scraped = 1 WHERE product_url = ?
       ''', (item['url'],))
      
       self.conn.commit()
       return item

Then there’s the process_item method, where all the real action happens. So, every time our spider reaches out and grabs information from a website, this method processes that info and puts it away in the right place. First, it converts the data’s ‘details’ part into a JSON-encoded string. It’s kind of like taking a complex document, then summarising it so it’s easy to file. Now, it gathers all the fragments of information about anything – for example, the URL of the product, its title, price, and our summarised details – and writes them to our product data table. If we have seen this product before, instead of writing a duplicate entry, it updates the information. This is similar to how we create a new file for a product or update an existing one with fresh information. In addition to storing the product data, it also updates our URL tracking table and marks this URL as ‘scraped’. It is in much the same vein as checking off a task on our to-do list.

def log_error(self, url, error_message):
       """
       Log an error message associated with a URL.

       This method inserts an error log entry into the error table.

       Args:
           url (str): The URL associated with the error.
           error_message (str): The error message to log.
       """
       self.cursor.execute(f'''
           INSERT INTO {self.error_table} (url, error_message)
           VALUES (?, ?)
       ''', (url, error_message))
       self.conn.commit()

We use the log_error method to keep records of what goes wrong while we’re trying to scrape. Whenever something does go wrong, like when a page refuses to load or we just can’t find what we’re looking for, it invokes the log_error method. It logs both the errant URL and what exactly has gone wrong. Then it stores this information into our error table, along with the timestamp of when that error actually occurred. This essentially becomes a dedicated troubleshooting notebook where we jot down any problems we find and, importantly note exactly what happened and at what time. This can come in very handy later if we need to troubleshoot our scraper or if we want to retry those problematic URLs again. It helps learn from mistakes and continue improving the process of scraping.

And that’s a wrap! In this blog, we learn how to scrape Rolex watches over $15,000 from eBay by using Scrapy and Playwright. We configured our spider in Scrapy, managed dynamic content with Playwright, and efficiently stored the data we scrapped. Meanwhile, we managed to handle pagination, regulate delay, and structure the data for further analysis.

Web scraping might sound a bit fiddly initially, but the moment you have the right tool and approach the task in steps, it will prove to be an incredibly potent means of collecting data. In terms of suggestions for further improving this project: try to extract other watch brands, add other fields to scrape, or you could even make some analysis over it to get the trends from pricing.

Hope this blog helped you understand it better! Comment below if you have any questions or suggestions. Happy coding!

Connect with Datahut for top-notch web scraping services that bring you the valuable insights you need hassle-free.

1. Is it legal to scrape eBay for Rolex watch listings?

Web scraping eBay is subject to their terms of service, and scraping without permission may violate their policies. To stay compliant, use eBay’s API for structured data access or ensure that your scraping approach respects robots.txt and legal considerations.

2. What tools are best for scraping eBay for Rolex watches over $15,000?

Python libraries like BeautifulSoup and Scrapy can help scrape eBay pages, while Selenium can handle JavaScript-heavy pages. However, eBay’s API is the best option for structured data extraction, ensuring accuracy and compliance.

3. How can I avoid getting blocked while scraping eBay?

To reduce the risk of getting blocked, follow these best practices:

• Use rotating proxies and user agents

• Implement delays and randomized request intervals

• Limit the frequency of requests to avoid detection

• Prefer API access if available for reliability

4. Can your web scraping service help extract Rolex watch data from eBay?

Yes! As a web scraping service provider, we offer custom data extraction solutions to collect high-value product listings like Rolex watches. We ensure data accuracy, compliance with best practices, and automation to fetch real-time pricing, seller ratings, and listing details. Reach out to us for a tailored solution.