How to Build a Web Crawler for Amazon
If you’ve ever tried to make sense of Amazon’s massive marketplace, you know how overwhelming it can be. Thousands of listings, constantly shifting prices, endless variations… sure, it’s a goldmine of data, but also a maze.
So, just like you have to walk before you can run, you have to crawl before you can scrape.
Whether you’re tracking competitors, monitoring product availability, or building a product database, building your own Amazon web crawler can help you access the specific URLs you need.
In this guide, we’ll walk you through why a crawler is useful, how to build one in Python using real code examples, and how it fits into a larger scraping strategy. No jargon overload, just a step-by-step approach to help you get started.
Why Web Crawling Is Useful with Amazon
Web crawling is useful with Amazon for multiple reasons. First of all… it’s huge! Finding the pages relevant to your task is not easy. With an Amazon web crawler, you have a solution that finds all of these pages for you.
And you’ll quickly find there are numerous use cases where you need to scrape a key section:
- Building a category-wide product dataset that allows you to monitor all of the products that fit within a specific category relevant to your business
- Discovering new product listings as they become available, including ASINs, to quickly capture competitive products
- Cataloging seller storefronts so that you can see what a specific seller is selling and consider its impact on your own sales
- Monitoring product availability across the specific target products important to your business over time to notice changes in inventory
Let’s also quickly look at this from the technical perspective. Consider our Web Scraping API. It can turn a URL into raw data in seconds, but you still need to give us the URLs you want to scrape. And if you’re doing that manually, you’re wasting the time saved from automated scraping. Plus, we’re pretty sure you’d always miss a few key pages.
Web Crawling vs Scraping
An important element to this process is understanding the difference between web crawling and scraping.
- Web crawling refers to the process of discovering and indexing web pages, usually for the purpose of mapping out a site or the web in general.
- Web scraping refers to extracting structured data from those discovered web pages.
In this context, your web crawler finds all the relevant product or seller pages on Amazon. Then your web scraper takes over and pulls specific details like price, stock availability, or seller names.
The Quickest Way to Scrape?
Just give us the URLs and our Web Scraping API will give you raw data in seconds.
How to Build a Basic Web Crawler for Amazon
Keep in mind that building a web crawler for Amazon can quite a broad task, or very specific, depending on your exact requirements. To help you get started, we’ll provide you with the specific steps to create a basic web crawler for Amazon, which you can then modify as you need.
Since Python is an ever-popular language for web scraping, let’s use that. We’ll pair it with Requests and BeautifulSoup as well. The former will send HTTP requests to the site, and the latter will parse the HTML to extract the information we need.
Step 1. Install Libraries
You will need to have the latest version of Python installed. Next, let’s install Requests, BeautifulSoup, and lxml for faster parsing. Use this code to do so:
pip install requests beautifulsoup4 lxml
Step 2. Send Requests.
The next step is to send an HTTP request to the site you wish to crawl. Note that Amazon uses advanced anti-bot mechanisms, so a basic request may not return usable content.
You should include realistic headers like a User-Agent and be prepared to handle blocks or redirects. Here is a simplified example for educational purposes:
import requests
url = 'https://www.amazon.com' # Replace with a target URL relevant to your task<br>headers = {
'User-Agent': 'Mozilla/5.0 (compatible; RayobyteBot/1.0; +https://rayobyte.com)'
}
response = requests.get(url, headers=headers, timeout=10)<br>if response.status_code == 200:
print("Successfully fetched the webpage!")
else:
print(f"Failed to retrieve the webpage. Status code: {response.status_code}")
Step 3. Parse the HTML content.
Next, parse the HTML to make it navigable and extractable. BeautifulSoup helps you understand the page structure:
from bs4 import BeautifulSoup soup = BeautifulSoup(response.text, 'html.parser') print(soup.prettify()) # Optional: view the page structure
Step 4. Extract Information
Now that you have the parsed content, extract specific elements relevant to your goals. For example, to extract all hyperlinks:
links = soup.find_all('a')
for link in links:
print(link.get('href'))
Step 5. Follow links to crawl additional pages.
You can build a function that crawls multiple pages by following links recursively. Use proper URL handling to ensure links remain valid:
from urllib.parse import urljoin<br>def crawl(url, depth=2):
if depth == 0:
return
print(f"Crawling URL: {url}")
response = requests.get(url, headers=headers, timeout=10)
if response.status_code != 200:
print(f"Failed to fetch {url}")
return<br> soup = BeautifulSoup(response.text, 'html.parser')
links = soup.find_all('a', href=True)
for link in links:
href = link.get('href')
next_url = urljoin(url, href)
print(next_url)
crawl(next_url, depth - 1)
crawl('https://www.amazon.com', depth=2)
Note: In practice, many of these pages may not render properly without a headless browser like Selenium or Puppeteer due to JavaScript.
Step 6. Send URLS for Scraping
Once you’ve run the crawler and gathered your list of URLs, you can then begin scraping them for more specific content using tools like our web scraping API. The crawler provides a discovery mechanism to help you build datasets tailored to your objectives.
Time to Get Scraping!
Now you can crawl, it’s time to run. Get raw data in seconds with our powerful API.
Ready for more ways to modify and update this language to achieve your goals? Use our previous guides to help you:
- How to Web Scrape in Python
- 13 Python Web Scraping Projects to Try
- A Comprehensive Guide to Python Web Crawlers
Common Challenges and How to Solve Them with an Amazon Web Crawler
A web crawler for Amazon can be a highly effective tool for business owners who want to ensure their products receive sufficient attention and market share. However, there are some concerns that can happen along the way.
One of the most important steps to take is to use proxies. A proxy blocks your personal IP address from being revealed to the website. That way, you do not get banned for visiting the site too many times. Additionally, rotating proxies will allow you to avoid the common problem of rate limiting, which means you will be less likely to be banned from accessing the site due to excessive data usage. Rayobyte’s rotating proxies are a solution that you can easily plug into this process to get better and more robust long-term benefits.
Also consider:
- Adding realistic headers to every request (especially a User-Agent string)
- Respecting crawl delays and rate limits
- Using headless browsers for pages rendered with JavaScript
- Exporting collected data to structured formats like JSON or CSV for easier scraping and analysis
Get Started with Creating a Web Crawler for Amazon
At Rayobyte, we aim to help you master the process of capturing valuable data safely. If you are building a web crawler for Amazon, use our tools and strategies here to help you do so ethically.
If you also hep with your wider Amazon web scraping needs, you can use our proxies to improve your success rates. Or, if you want a reliable solution that takes care of all these technical challenges for you, try our Web Scraping API!
Whichever option you choose, a reliable Amazon web crawler will ensure you only scrape the preselected URLs that you need, keeping resource usage optimal and speeding up an otherwise painful and near-impossible manual process.
The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.
