Scrapy vs requests: What are the differences?

Introduction

Scrapy and requests are both widely used Python libraries for web scraping and data extraction. While both serve the purpose of making HTTP requests and handling web-related tasks, there are key differences between Scrapy and requests that distinguish their functionality and use cases.

1. Asynchronous vs. Synchronous: One major difference between Scrapy and requests is their approach to handling HTTP requests. Scrapy is an asynchronous framework that allows multiple requests to be made concurrently, making it more efficient for scraping large amounts of data from multiple sources simultaneously. On the other hand, requests is a synchronous library, meaning that it handles requests sequentially, one at a time. This makes it more suitable for simple, single-threaded tasks that require a straightforward workflow.

2. Built-in Parsing and Parsing Libraries: Scrapy provides built-in support for parsing and extracting data from HTML and XML responses using its powerful XPath and CSS selectors. This makes it easier to navigate and extract specific elements from the response content. In contrast, requests does not have built-in parsing capabilities, and developers would need to use external libraries like BeautifulSoup or lxml to parse and extract data from the response content.

3. Spider Framework: Scrapy is not just a library but a full-fledged web scraping framework that includes a built-in "spider" system. This spider system allows developers to define how to follow links, extract data, and handle pagination in a structured and reusable manner. Requests, on the other hand, is a lower-level library that does not provide a specific framework for these tasks, requiring developers to handle these aspects themselves.

4. Middleware and Pipelines: Scrapy provides a flexible middleware and pipeline system that allows developers to define custom processing steps for the scraped data. Middleware can be used to handle different aspects of the request-response cycle, such as rotating user agents or implementing custom proxies. Pipelines, on the other hand, can be used for post-processing data, storing it in databases, or performing additional tasks. Requests does not have built-in middleware or pipeline capabilities, requiring developers to handle these tasks manually or use external libraries.

5. Scalability and Performance: Due to its asynchronous nature and built-in concurrency handling, Scrapy is better suited for large-scale and high-performance scraping tasks. It can efficiently handle large volumes of data and distribute the workload across multiple threads or processes. Requests, while efficient for small to medium-scale tasks, may face performance limitations when dealing with a significant amount of concurrent requests or large datasets.

6. Community and Documentation: Scrapy has a well-established and active community with extensive documentation, tutorials, and resources available. It is widely used and has a large ecosystem built around it. Requests also has a significant user base, but the community and resources may not be as extensive as Scrapy. However, requests has a simpler and more straightforward API, making it easier for beginners to get started quickly.

In Summary, Scrapy is an asynchronous web scraping framework with built-in parsing, a spider system, middleware and pipeline capabilities, scalability, and an active community. Requests, on the other hand, is a synchronous library that lacks built-in parsing and advanced web scraping features, making it more suitable for simpler tasks with less complexity and scalability requirements.