scrapy

Create a project

$ scrapy startproject [project_name]

$ scrapy genspider [spider_name] [website_domain]

$ scrapy edit [spider_name]

$ scrapy crawl [spider_name]

$ scrapy fetch [url]

$ scrapy view [url]

$ scrapy shell [url]

scrapy <command> [options] [args]

startproject <project_name>
    Creates a new Scrapy project with a basic directory structure and configuration files. This is usually the first command used when starting a new scraping task.

genspider <name> <domain>
    Generates a new spider file inside the current Scrapy project. Spiders define how to crawl a site and extract data, specifying start URLs and parsing rules.

crawl <spider_name> [-o output_file]
    Starts the crawling process using a specific spider defined within the project. It's the primary command to run your scraper and collect data. Output can be saved to various formats.

shell [<url>]
    Opens an interactive Scrapy shell, which is a powerful tool for testing and debugging your XPath or CSS selectors on specific URLs directly, without running the entire spider.

parse <url> [--spider=spider_name]
    Fetches a given URL and parses it with the specified spider (or a dummy spider if none is given), showing how the items would be extracted. Useful for debugging parsing logic.

settings [--get=setting_name]
    Displays the Scrapy settings for the current project. Can be used to inspect individual setting values or all configured settings.

runspider <file.py>
    Runs a spider defined in a single Python file, without needing to create a full Scrapy project. Useful for quick, standalone scraping scripts.

version [-v]
    Displays the Scrapy version. The -v option provides more verbose information, including Python, Twisted, and lxml versions.

bench
    Runs a quick benchmark test to check Scrapy's performance on your system, measuring request/response processing speed.

Scrapy is an open-source web crawling framework written in Python. It provides a fast and powerful way to extract data from websites, process it, and store it in various formats. Built on an asynchronous architecture (Twisted), it efficiently handles numerous concurrent requests, making it suitable for large-scale data extraction tasks. Scrapy offers built-in functionalities for managing cookies, handling user agents, respecting robots.txt, and navigating complex website structures. Its extensible design, through middlewares, pipelines, and extensions, allows developers to tailor the scraping process to specific needs, making it a versatile tool for data mining, content aggregation, and automated testing.

Scrapy is a Python framework, not a native Linux command in the traditional sense; it's invoked via the Python environment. It requires Python 3.7+ and pip for installation. Large-scale crawling can be resource-intensive and may require careful management of system resources and network bandwidth. Websites may implement anti-bot measures, leading to IP blocking or CAPTCHAs, which require advanced handling strategies.

Scrapy is installed using pip, the Python package installer. The command is:
pip install scrapy
It's recommended to install it within a Python virtual environment to manage dependencies.

A Scrapy project typically consists of multiple components: spiders (defining the crawling logic), items (structured data containers), pipelines (for processing scraped items), and settings.py (for project-wide configuration). The startproject command sets up this basic structure.

Scrapy offers numerous extension points for customization:
- Item Pipelines: Process items once they are scraped.
- Downloader Middleware: Process requests and responses passing through the downloader.
- Spider Middleware: Process requests and items passing through the spider.
- Extensions: Implement custom functionality to hook into Scrapy's core.

Scrapy was initially released in 2008 by Scrapinghub (now Zyte), a company specializing in web scraping. It emerged from the need for a robust, flexible, and scalable framework for complex web data extraction projects. Built upon the Twisted asynchronous networking library, it was designed to handle high concurrency efficiently. Over the years, Scrapy has evolved significantly, adding new features, improving performance, and fostering a vibrant open-source community, making it a de-facto standard for web crawling in Python.

curl(1), wget(1), python(1), pip(1)