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Scraping Dynamic Websites

Dynamic websites are those that send additional JavaScript files along with the HTML content. These JavaScript files, once executed in the browser, fetch more data and modify the HTML content. In this tutorial, we'll learn how to scrape such dynamic websites using Scrapy.

What is Scrapy?​

Scrapy is an open-source web crawling framework written in Python. It allows us to write spiders that can crawl websites, extract data, and store it in our preferred format. Scrapy's built-in capabilities for handling HTTP requests/responses and extracting data make it a powerful tool for web scraping.

Basics of Dynamic Websites​

Before we start, you should be familiar with a few concepts:

JavaScript and AJAX​

JavaScript is a language used to create dynamic content on websites. AJAX (Asynchronous JavaScript and XML) is a technique that uses JavaScript to communicate with the server without refreshing the page. This is used to fetch data on demand and update the webpage dynamically.

HTML vs. Dynamic Content​

Traditional web scraping techniques involve sending an HTTP request to the server, receiving an HTML response, and parsing that HTML to extract the required data. However, in dynamic websites, the initial HTML response doesn't contain all the data. Additional data is fetched by JavaScript via AJAX calls.

Scrapy and Dynamic Websites​

Scrapy, by default, doesn't execute JavaScript. When Scrapy fetches a page, it doesn't wait for or process the AJAX calls, so the additional data fetched by AJAX calls is not available. To overcome this, we have two main options:

  1. Analyze the AJAX calls and mimic them in our Scrapy spider.
  2. Use a headless browser or a tool that can execute JavaScript.

We'll explore both these options below.

Analyzing AJAX Calls​

The first option involves analyzing the AJAX calls made by the web page and replicating them in our Scrapy spider.

Here's a step-by-step process:

  1. Inspect the Web Page: Open the web page in a browser, open the Developer Tools (F12 in Chrome), and go to the Network tab. Refresh the page and observe the AJAX calls being made.

  2. Analyze the AJAX Call: Click on the AJAX call to see the details. Check the Request URL, Request Method, Form Data, and Response. We're essentially trying to understand the API the website uses to fetch data.

  3. Mimic the AJAX Call in Scrapy: Now that we know the details of the AJAX call, we can use Scrapy to make the same call. We can use the scrapy.Request function with the AJAX URL and parameters to fetch the data.

  4. Parse the AJAX Response: The response of an AJAX call is usually in JSON format. Use Python's json module to parse the response and extract the required data.

Using a Tool to Execute JavaScript​

If analyzing AJAX calls is not feasible or the website uses complex JavaScript, we can use a tool that executes JavaScript. One such tool is Splash. Splash is a lightweight, scriptable browser as a service with an HTTP API. Scrapy has built-in support for Splash.

Here's a step-by-step process:

  1. Install Splash: Splash can be run as a Docker container. Install Docker and run the following command to start Splash: docker run -p 8050:8050 scrapinghub/splash.

  2. Configure Scrapy to Use Splash: In your Scrapy settings, add the following lines:

DOWNLOADER_MIDDLEWARES = {
    'scrapy_splash.SplashCookiesMiddleware': 723,
    'scrapy_splash.SplashMiddleware': 725,
    'scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware': 810,
}

SPLASH_URL = 'http://localhost:8050'
  1. Use Splash in Your Spider: In your spider, use scrapy_splash.SplashRequest instead of scrapy.Request to fetch pages. This will execute JavaScript on the page before fetching it.
yield SplashRequest(url, self.parse_result, endpoint='render.html', args={'wait': 0.5})

Note: Be aware that using Splash or any similar tool can make your spider slower as it waits for all JavaScript to execute.

Conclusion​

Scraping dynamic websites can be a bit challenging, but with the right tools and techniques, it's definitely achievable. It's all about understanding how the website fetches its data and replicating that in our Scrapy spider. Remember to respect the website's terms of service and don't overload their servers with too many requests. Happy scraping!