• General Web Scraping

  How to compare Puppeteer and Scrapy for scraping dynamic data?

  Posted by Samir Sergo on 12/17/2024 at 9:44 am

  Scraping dynamic data often requires tools that can handle JavaScript-rendered content. Puppeteer and Scrapy are two popular options, but they serve different purposes. Puppeteer is a Node.js library that controls a headless Chrome browser, making it ideal for scraping dynamic sites by rendering JavaScript exactly as a user would see it. This means it can scrape content generated after page load, handle infinite scrolling, and interact with web elements like dropdowns or buttons.
  Scrapy, on the other hand, is a Python-based framework designed for high-performance web scraping. It excels at handling static content and large-scale scraping tasks, with built-in tools for crawling, parsing, and data storage. However, it struggles with dynamic data unless integrated with tools like Splash or Selenium.
  Choosing between Puppeteer and Scrapy depends on your project’s needs. For instance, if the site heavily relies on JavaScript to render data, Puppeteer is a better fit. If you’re dealing with static content or require scalability and speed, Scrapy is the go-to choice. Combining the two can sometimes provide the best of both worlds: using Puppeteer to gather dynamic data and Scrapy to handle crawling and data processing.
  Here’s an example of a Puppeteer script for scraping a dynamically loaded product list:

  const puppeteer = require('puppeteer');
  (async () => {
      const browser = await puppeteer.launch({ headless: true });
      const page = await browser.newPage();
      await page.goto('https://example.com/products', { waitUntil: 'networkidle2' });
      const products = await page.evaluate(() => {
          return Array.from(document.querySelectorAll('.product-item')).map(item => ({
              name: item.querySelector('.product-title')?.innerText.trim(),
              price: item.querySelector('.product-price')?.innerText.trim(),
          }));
      });
      console.log(products);
      await browser.close();
  })();
  

  And here’s a Scrapy spider for static data scraping:

  import scrapy
  class ProductSpider(scrapy.Spider):
      name = 'products'
      start_urls = ['https://example.com/products']
      def parse(self, response):
          for product in response.css('.product-item'):
              yield {
                  'name': product.css('.product-title::text').get(),
                  'price': product.css('.product-price::text').get(),
              }
          next_page = response.css('a.next-page::attr(href)').get()
          if next_page:
              yield response.follow(next_page, self.parse)
  

  How do you decide which tool to use? It often comes down to balancing the need for JavaScript rendering, project complexity, and scalability. Have you faced challenges using either tool, and how did you overcome them?

  Samir Sergo replied 5 days, 15 hours ago 1 Member · 0 Replies
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