When it comes to the choice between Scrapy or Selenium, there’s no one answer. There are numerous factors that can greatly impact the efficiency and outcome of your project. Hence, you must assess at least the major factors, like the project scale, overall speed requirements, and the difficulty of target websites. To help you decide, we’ve prepared this in-depth comparison of Scrapy vs. Selenium.
In this article, you’ll learn about their differences, fundamental features, and how to use each of them for successful public web data scraping.
Let’s kick things off with a short overview of both frameworks.
The fundamental distinction is that Selenium is a browser automation and testing framework that can be used for web scraping, while Scrapy is specifically a web scraping and web crawling framework.
Selenium is an open-source framework with a powerful trio of tools for web testing and automation on different browsers and devices. It supports popular programming languages, which you can use to command Selenium to interact with browsers and web elements. This way, it can perform various browser actions like clicking buttons, dropdown menus, filling out text fields, navigating websites, and performing other automated browser-based tasks.
Scrapy is a fast open-source framework explicitly built to crawl and scrape websites in order to extract data. While Scrapy web scraping is only possible in Python, its asynchronous method, ease-of-use, and overall high-speed makes it ideal for a project of any scale – even the largest one. Although its purpose is fixated on web scraping, Scrapy can also be used in other ways, for instance, web server load testing.
Scrapy has maintained a buzz around it, and deservedly so. It’s a free and powerful web scraping tool that enables concurrent requests, and its use is fairly streamlined. Hence, Scrapy helps developers carry out scraping projects of any scale while staying within the budget.
High-speed crawling and scraping
Large-scale data acquisition
Highly customizable and extensible
Smooth web scraping experience
Doesn’t support dynamic content rendering
No browser interaction and automation
Steep learning curve
Browser interactions and automation
Handles dynamic web pages
Cross-browser and device support
Relatively easy to use
Slow and resource-intensive
Doesn’t scale well for web scraping purposes
While there are disadvantages both tools bear in nature, they offer distinct features that make Scrapy and Selenium powerful in different situations:
Spiders are classes that specify how a website, or a batch of them, should be crawled and parsed. This feature enables efficient and highly customizable web scraping.
Requests and responses
Scrapy offers asynchronous networking, request prioritization, scheduling, automatic request retries, as well as built-in mechanisms to handle redirects, cookies, sessions, and common web scraping errors.
This extension allows automatic control of crawling speed based on Scrapy’s and the target website server’s load. In turn, your scraping requests don’t overcrowd the target site compared to default crawling speeds.
Scrapy allows XPath and CSS selectors for HTML node navigation and selection. This option enables you to leverage both methods for the best web scraping performance.
The extracted data is returned as items that are Python objects in key-value pairs, which you can configure and modify to suit your data needs. This feature enables easy access and manipulation of data accessible in a structured manner.
Item pipelines allow processing data before exporting and storing it. You can perform different tasks, such as validate, clean, transform, and then store the data in databases.
This is an in-built feature that enables you to export the data using various serialization formats and storage backends. While the default supported export formats are JSON, JSON lines, CSV, and XML, you can specify more formats through the feed export feature.
Middlewares, extensions, and signal handlers
Scrapy allows you to customize and extend various processes of web scraping through the use of middlewares, like spider and downloader middlewares, custom extensions, and event signals.
Additional Scrapy services
To further extend the functionality of your scraper, you can utilize built-in services like event logging, stats collection, email sending, and the telnet console.
Selenium can make your web requests resemble human behavior, allowing you to bypass anti-bot detection systems. What’s more, you can program Selenium to handle various browser tasks automatically, like clicking buttons and writing text, handling pop-ups and alerts, as well as solving CAPTCHAs.
Just like Scrapy, Selenium uses XPath and CSS selectors to navigate and select HTML nodes.
Selenium enables you to launch your script on separate machines, allowing you to scale your projects and run parallel tasks.
Browser profiles and preferences
You can load and customize different browser profiles and preferences, including cookies and user agents, making it possible for you to achieve greater scraping success.
|Web scraping and crawling
|Web testing and automation
|Small to large scale
|Small to medium scale
(See this Scrapy proxy integration guide)
(See this Selenium proxy integration guide)
|CSS and XPath
|CSS and XPath
|None, requires additional libraries
|Chrome, Edge, Firefox, and Safari
Another possible use of the Scrapy-Selenium combination is in situations where you need to interact with the website in order to access the desired data. You can use Selenium to automate user interactions and get the page source, which can then be passed on to Scrapy for further processing.
At its core, the answer depends on your target websites and the scale of your scraping project. Using only one framework de-clutters and eases up the whole process, so let’s review some cases where Selenium or Scrapy can potentially be the ultimate choice:
If you plan to extract low-volume data only from dynamically-rendered websites, then Selenium is the perfect solution due to its straightforward and fairly quick setup.
If your targets are static and you feel confident with your programming skills, then Scrapy is a winner here, no matter the scale of your project.
Yet, in case your project requires automatically clicking on buttons or filling out forms on the website, then the Selenium web scraping approach may be the best bet.
Having said that, both frameworks can supplement each other on different levels when used together, for instance:
If most of your target websites are static and only some require dynamic rendering, then you can use Selenium to render dynamic websites and Scrapy for the remaining steps.
The same principle as above applies in cases where you need to interact with website elements and mimic human-like behavior.
On the other hand, when it comes to larger-scale scraping projects that require dynamic rendering of content, you might want to consider using Scrapy with Splash. See our Scrapy Splash tutorial for more information.
Web scraping has been in the field for some time now, and so there are other popular web scraping tools you may want to consider instead of Selenium. Feel free to take a look at our other comparison articles on Playwright vs. Selenium, Scrapy vs. Beautiful Soup, and Puppeteer vs. Selenium.
Scrapy’s speed is a result of many factors, but three main ones can be distinguished:
Asynchronous web request processing;
Concurrently run spiders that allow parallel processing;
Efficient and optimized resource usage.
In short, Scrapy has a steep learning curve. It can take you from a few days to several months to grasp the fundamentals, but it all depends on your prior knowledge, skill, and experience.
Scrapy’s processes are well-documented, but for a smoother journey, you should have at least basic Python knowledge and an understanding of web scraping.
As Scrapy uses HTML elements to find and extract the data, you should also be familiar with HTML structure and syntax, alongside how to form CSS or XPath selectors.
About the author
Vytenis Kaubre is a Copywriter at Oxylabs. As his passion lay in creative writing and curiosity in anything tech kept growing, he joined the army of copywriters. After work, you might find Vytenis watching TV shows, playing a guitar, or learning something new.
All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.
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