scrapy

Scrape websites to extract structured data

TLDR

Create a project

$ scrapy startproject [project_name]

Create a spider (in project directory)

$ scrapy genspider [spider_name] [website_domain]

Edit spider (in project directory)

$ scrapy edit [spider_name]

Run spider (in project directory)

$ scrapy crawl [spider_name]

Fetch a webpage as Scrapy sees it and print the source to stdout

$ scrapy fetch [url]

Open a webpage in the default browser as Scrapy sees it (disable JavaScript for extra fidelity)

$ scrapy view [url]

Open Scrapy shell for URL, which allows interaction with the page source in a Python shell (or IPython if available)

$ scrapy shell [url]

bench
    Run a quick benchmark test.

fetch url
    Fetch a URL using the Scrapy downloader.

genspider name domain
    Generate a new spider using pre-defined templates.

runspider spider_file
    Run a self-contained spider (no project required).

settings [options]
    Get settings values.

shell url
    Interactive console for testing scraping code.

startproject project_name
    Create a new Scrapy project.

version [-v]
    Print Scrapy version.

view url
    Open a URL in your browser, as seen by Scrapy.

DESCRIPTION

Scrapy is a powerful Python framework designed for large-scale web crawling and web scraping. It allows you to efficiently extract structured data from websites, which can then be used for a variety of purposes such as data mining, information processing, or historical archiving. Scrapy handles many of the complexities of web crawling, including request scheduling, concurrency management, and response processing, allowing developers to focus on defining the extraction rules for specific websites.

Its extensible architecture allows for customization through middlewares and pipelines, enabling sophisticated data manipulation and storage. It's built on top of Twisted, an asynchronous networking framework, which gives it excellent performance and scalability. Scrapy is commonly used for building web crawlers for a wide variety of applications, from price comparison websites to news aggregators and academic research.

CAVEATS

Scrapy requires Python and typically relies on having appropriate system packages installed for handling certain content types (e.g., `libxml2`, `libxslt`, and `zlib`). Scraping websites should always be done ethically and in compliance with the website's `robots.txt` file and terms of service.

PROJECT STRUCTURE

A Scrapy project typically contains spiders (which define how to crawl specific websites), items (which define the data structure for scraped data), pipelines (which process and store scraped data), and settings (which configure various aspects of the crawler).

TWISTED FRAMEWORK

Scrapy's asynchronous architecture, based on the Twisted framework, enables it to handle a large number of concurrent requests, making it highly efficient for large-scale web crawling.

HISTORY

Scrapy was originally developed at Scrapinghub, a web scraping services company, and has since become a widely used open-source project. Its development has been driven by the needs of real-world scraping projects, focusing on reliability, performance, and flexibility. It evolved from simpler web scraping tools into a sophisticated framework, incorporating features like auto-throttling, spider middleware, and data pipelines.