Building a Scalable Web Scraper with Python and Scrapy

In one of my recent backend projects, I needed to extract structured data from multiple e-commerce sites at scale. After evaluating several tools, I chose Scrapy — a powerful Python framework for web scraping — and combined it with Docker and MongoDB for reliability and scalability.

This is how I built a maintainable, fault-tolerant scraper pipeline.

Why Scrapy?

While requests + BeautifulSoup works for simple scripts, Scrapy offers:

• Built-in request throttling
• Middleware support (proxies, retries)
• Async crawling via Twisted
• Extensible pipelines
• Stats collection and logging

import scrapy

class ProductSpider(scrapy.Spider):
    name = "product_spider"
    start_urls = [
        "https://example-shop.com/products?page=1"
    ]

    def parse(self, response):
        for product in response.css('.product-item'):
            yield {
                'name': product.css('.title::text').get(),
                'price': product.css('.price::text').get(),
                'url': product.css('a::attr(href)').get(),
            }

        # Follow pagination
        next_page = response.css('.next-page::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)