During the formative years of the World Wide Web (WWW), a service that enables the access of information over the internet, static HTML pages were the order of the day. This was sometime in the early 1990s. Since then, however, a lot has changed, with the introduction of new server-side processing technologies, programming languages, and frameworks giving rise to dynamic websites.
This evolution has had a few implications for the web scraping industry. In this article, we detail the challenges that have arisen and the different web scraping technologies that have been improved to deal with the impediments.
Static vs. Dynamic Websites
Static Websites
A static website is the most basic type of website, which means it’s the easiest to create. As the name suggests, it’s characterized by non-changing components and layouts. As a result, the website looks the same to everyone who accesses it, even the site administrator.
A static website uses client-side programming languages such as HTML, CSS, and JavaScript. It’s called like so because they’re executed in a web browser, also known as a web client. These languages are used to create files the server retrieves without alteration upon request. Therefore, the web server doesn’t undertake server-side processing before sending the requested files.
Dynamic Websites
On the other hand, dynamic websites are characterized by content that’s fine-tuned to every visitor’s individual characteristics. These characteristics are generated from previous browsing history (e.g., shopping habits), location, local time, settings and preferences, and much more. This way, the dynamic sites make for an individualized and interactive experience.
Unlike static websites, dynamic websites are more flexible and often require more back-end activity. Whereas a static website’s server retrieves the HTML files and accompanying CSS or JavaScript files and sends them as is without alteration, a dynamic website creates the HTML files on a user-by-user basis. It uses the visitor’s characteristics to construct an HTML file. Upon construction, the server fully renders the file and sends it to the browser, which then displays the content of the webpage. This is known as server-side rendering (SSR) and is carried out by scripts written with languages such as Ruby, Python, PHP, and server-side JavaScript.
Notably, if your browser doesn’t have a cache of a particular web page, the server will construct and render that particular web page every time it receives a request. And while it’s taxing to the server, it offers excellent benefits regarding Search Engine Optimization (SEO). This is because search engines can easily crawl and index the rendered web pages without individually rendering them.
In some cases, however, developers may reduce the load the web server is subjected to during server-side rendering by opting for client-side rendering (CSR). They achieve this by embedding JavaScript scripts into the HTML code that the browser then executes and displays. Unfortunately, unlike SSR, CSR greatly impacts SEO, as most search engines are largely incapable of rendering the pages during the crawling and indexing stages.
Web Scraping Static and Dynamic Websites with Python
Python is an easy-to-learn language that boasts many web scraping libraries to ease extracting data from static and dynamic websites. Given the differences associated with static and dynamic websites, however, the approach to extracting data from each of them using Python is markedly different. However, in both cases, it’s important to use the Python web scraping library known as Requests, which sends HTTP/HTTPS requests to initiate the data collection process.
How to Scrape Static Websites with Python
Static websites are generally made up of HTML files. Thus, creating a web scraper that can extract data from such sites requires you to use parsing libraries such as Beautiful Soup and lxml. Beautiful Soup is designed to parse HTML data, while lxml is designed to parse data from either an HTML or XML file. Notably, both libraries can save the structured data in a JSON or CSV file.
How to Scrape Dynamic Websites with Python
The approach to extracting data from dynamic websites, particularly those that contain CSR scripts, differs from the procedure detailed above. For these websites, it’s important to use a Python web scraping library called Selenium.
Selenium enables you to launch and run Chrome in headless mode, effectively creating a headless browser. This browser will then execute JavaScript scripts as defined by the website’s code. Because such websites also contain HTML files, you should also incorporate either Beautiful Soup or lxml into your Python web scraping tool.
Conclusion
In a bid to improve the user experience, web developers are increasingly creating dynamic websites. This can impact web scraping by making it more complicated. However, with Python web scraping libraries, you can easily scrape data from static and dynamic websites.
