Web scraping is a technique that involves automatically extracting and collecting data from websites using specialized tools or programs. It’s particularly valuable for companies who are looking to improve their data-driven decision-making processes.
However, due to the complex HTML structures, dynamic content, and diverse data formats found on most websites, the effectiveness of web scraping is dependent on the tools you use.
Scrapy and Selenium are powerful tools designed to facilitate web scraping. Scrapy extracts data from static websites, whereas Selenium can perform web browser automation and extract data from dynamic websites.
In this article, you’ll compare the two tools based on their ease of use, performance and scalability, suitability for different types of web content, and integration capabilities.
Ease of Use
Scrapy is a Python-based web scraping tool that can run on Linux, Windows, macOS, and Berkeley Software Distribution (BSD). Not only is Scrapy easy to use, but it also provides a high-level API for web scraping tasks, which can help further simplify the web scraping process.
To set Scrapy up, you just have to install it and configure some spiders using Python code (this does involve some understanding of web scraping concepts). When you execute a Scrapy command to initiate a project, it generates a folder dedicated to your project. Within this folder, you’ll find default Python files, such asitems.py,pipelines.py, andsettings.py. These files are organized in a simplified structure, making it easy to get started with web scraping.
Scrapy provides in-depth documentation, including curated articles and videos to help answer any questions you may have. Scrapy also has an active subreddit and Discord community where you can engage in different discussions or topics.
In comparison, Selenium supports multiple programming languages, including Java, JavaScript, Python, and C#, and it is compatible with many of the same operating systems as Scrapy, including Windows, macOS, and Linux. When compared with Scrapy, Selenium is not as easy to learn and requires more time, effort, and sometimes, resources before a person can become proficient.
To set Selenium up, you have to install the Selenium library and then configure the WebDrivers that handle browser automation. If you’re scraping data from a dynamic website that requires you to log in, you need to set up web automation to handle the login process before you can start scraping any data.
Selenium offers a rich set of navigation methods that you can customize to easily locate elements on a web page. Additionally, it offers chains of interactive actions, including clicks, double clicks, drags, drops, and scrolls, that enable effortless interaction with web pages.
The Selenium official documentation includes impressive guidelines, step-by-step instructions, and tutorials related to both web automation and web scraping.
Because Selenium is a more general-purpose tool for web automation, it has a larger and more diverse community. If you have any questions while working with Selenium, its official user group and subreddit community can help. Or if you have an issue that needs an answer right away, you can utilize their IRC chatroom.
Performance and Scalability
The effectiveness of any web scraping tool’s performance heavily relies on its speed as the objective is to collect a significant amount of data quickly.
Scrapy excels at scraping content from static web pages, resulting in faster data extraction than Selenium. This is because Selenium relies on browser instances to execute different interactions, such as clicking buttons or filling forms.
In a speed test collecting the titles and prices of 1,000 books from https://books.toscrape.com/, Scrapy was able to complete the task in 31.57 seconds. In contrast, Selenium took an average of 156.01 seconds to scrape the same content:
The Scrapy architecture efficiently handles memory by processing responses and items in a continuous process, avoiding the need to load entire web pages into memory at once. Scrapy also has built-in support for caching and incremental scraping, which improves the scalability by minimizing redundant requests and processing only new or updated content.
Additionally, Scrapy provides options to fine-tune memory usage through settings such as concurrent requests, depth limits, and item pipelines. These features enable you to optimize memory consumption according to the specific requirements of your web scraping project.
Selenium typically consumes a significant amount of memory when interacting with JavaScript-heavy websites, leading to higher memory consumption. This can negatively impact its scalability and performance, especially in large-scale scraping projects.
Scrapy’s built-in middleware called HTTPCacheMiddleware caches requests made by the spiders and their related responses. You can enable caching by adding the following code to thesetttings.pyfile in your project:
# Enable and configure HTTP caching (disabled by default)
HTTPCACHE_ENABLED = True
Scaling Selenium to handle large-scale data scraping requires deploying multiple instances across distributed systems, leading to increased resource demands, such as RAM and CPU.
Suitability for Different Types of Web Content
The majority of websites on the internet feature either dynamic or static web pages. Let’s take a look at how Scrapy and Selenium handle both types of web pages.
Dynamic Web Pages
The majority of dynamic web pages are powered by JavaScript frameworks, such as Angular and React, to update content without reloading the entire page.
Selenium can scrape dynamic content from various websites, but Scrapy doesn’t inherently support scraping dynamic content generated by JavaScript. You can integrate Scrapy with tools like Selenium and Splash to obtain this functionality.
Static Web Pages
Static web pages typically offer limited interaction compared to dynamic ones, usually allowing users to only view content or click links.
As mentioned previously, Selenium can scrape static pages, but it’s not the most efficient tool for the job. In contrast, Scrapy excels at scraping static data, providing a smooth and efficient experience for collecting the desired information.
Integration Capabilities
Scrapy can easily integrate with most Python tools, including databases like MySQL, PostgreSQL, and MongoDB, to store scraped data. You can even use object-relational mappers (ORMs), such as SQLAlchemy, to simplify the process of storing data in relational databases. If you want to process and analyze your data further, you can use pandas, a popular data manipulation and analysis library for Python.
Scrapy can also be integrated with web frameworks such as Django and Flask to build web applications that incorporate web scraping functionality. Moreover, integration with FastAPI allows you to build high-performance web APIs with asynchronous support that is suitable for handling scraping requests efficiently.
In contrast, Selenium provides browser drivers that act as intermediaries between Selenium WebDriver APIs and the browsers. You can download and install a WebDriver to integrate with the web browser of your choice. Selenium currently provides browser drivers for Chrome, Edge, Firefox, and Safari.
Selenium can also be used to automatically test web applications’ functionalities; however, keep in mind that it doesn’t have a built-in testing framework. You can integrate Selenium with other popular testing frameworks, including CodeceptJS, Helium, and Selenide.
Selenium used to integrate with CI tools, such as Jenkins and Travis CI, to enable automation scripts to be executed automatically as part of the continuous integration, continuous delivery (CI/CD) pipeline; however, now, they execute everything with GitHub Actions that support continuous testing and deployment processes.
Scrapy can be integrated with different proxy service providers, such as Bright Data, by passing the proxy IP and port as a request parameter. This method is recommended if you want to use a specific proxy for your project.
For example, if you wanted to integrate with a proxy server, you could use the pip commandpip3 install scrapyto install Scrapy, like this:
#import scrapy module
import scrapy
class BookSpider(scrapy.Spider):
name = "books"
def start_requests(self):
start_urls = ["https://example.com/products"]
for url in start_urls:
yield scrapy.Request(
url=url,
callback=self.parse,
# connect with proxy
meta={"proxy": "http://USERNAME:[email protected]:22225"},
)
def parse(self, response):
for book in response.css(".book-card"):
yield {
"title": book.css(".title ::text").get(),
"price": book.css(".price-wrapper ::text").get(),
}
Here, you import Scrapy and define a class calledBookSpiderinherited from Scrapy’s spider class to scrape a list of books from the website. Thestart_requests()method initiates requests with specified URLs and proxies, and theparse()method extracts book titles and prices using CSS selectors.
In contrast, Selenium supports straightforward proxy integration through various browser drivers, such as ChromeDriver and geckodriver. You just have to configure Selenium WebDriver to route its HTTP requests through a proxy server.
For example, you can integrate Selenium with proxies by specifying the proxy IP and port provided by Bright Data, like this:
#import selenium modules
from selenium import webdriver
from selenium.webdriver.common.proxy import Proxy, ProxyType
# Proxy configuration
proxy_address = "http://USERNAME:[email protected]"
proxy_port = "22225"
# Selenium options : integrate with proxy credentials from Bright data
options = webdriver.ChromeOptions()
options.add_argument('--proxy-server=%s:%s' % (proxy_address, proxy_port))
# Selenium webdriver instantiation
driver = webdriver.Chrome(options=options)
# Example usage: scraping a webpage
url = "https://example.com"
driver.get(url)
print(driver.page_source)
# Close the driver
driver.quit()
Here, you import the required Selenium modules and set up the proxy configuration. Then you configure Chrome to use defined proxy servers, instantiate a WebDriver, scrape a web page ("https://example.com"), print the page source, and close the WebDriver to finish the process.
Conclusion
In this article, you compared two popular web scraping tools: Scrapy and Selenium.
Scrapy is an easy-to-use Python-based scraping tool that’s ideal for data extraction with static websites. In contrast, Selenium provides automation and scraping capabilities using multiple programming languages, supports various web browsers, and is the better option when scraping dynamic and JavaScript-rendered content.
Whichever tool you decide to use, it’s recommended that you use a data platform like Bright Data. It can help you add functionalities to your web scraping scripts to avoid geographical restrictions, blocks, and solve CAPTCHAs. You can also utilize the Bright Data API and SDK to address a broader range of scraping requirements, ensuring the efficiency, speed, accuracy, and scalability of your web scraping project. Interested in taking your data collection even further? Purchase a custom dataset (free samples available).
