Scrapy is a Python web crawling framework that provides an easy way to get data from websites. It is designed to be scalable and fault-tolerant, while providing a lot of flexibility for you to customize the scraping process.
XPath, CSS selectors and Regular Expressions are used to define what elements should be scraped from a webpage. Extracted data can be stored in “Item” objects, which are akin to Python dictionaries. You can easily use those to export your scraped data in a variety of formats, such as CSS or JSON.
Writing XPath queries is an excellent first step in building a Scrapy scraper. XPath queries are particularly useful when scraping a lot of pages, as they are flexible and can be used to scrape very large web sites.
Once you have your XPath queries ready, try scraping a few detail pages on the web site to ensure you are getting consistent results. In particular, it is a good idea to verify that the query you are using is working by running it in the Scrapy shell mode.
Alternatively, you can simply use a tool like iPython to run the XPath queries learn more on a webpage and print out all of the extracted data. This way, you can then check that it is working properly before running it on a large scale.
The next step is to store the data that has been scraped in a way that is useful for you. It’s common to want to output the data in a format that can be accessed by other applications.
To do this, you can write a scrapy item pipeline that will automatically generate an XML or CSV file, for example. You can also write a media pipeline to download images (or other media) associated with the scraped items.
In addition, Scrapy provides a built-in logging mechanism that lets you monitor your crawler’s progress and error rates. Additionally, you can send email notifications to a specified address when certain events occur.
You can also set a global concurrency limit for your scraper, which is a good idea when you’re crawling lots of domains in parallel. The higher the concurrency limit, the faster your crawlers will be able to work, but it may increase the memory usage of your crawler.
Another feature that makes Scrapy especially good for crawling a lot of domains in parallel is its thread pool, which allows it to send multiple concurrent requests at once. This increases your crawl’s responsiveness, but it may also slow down DNS resolution, if you have a central DNS server that is being used by many crawlers.
Besides these features, Scrapy is an extensible scraping framework with an API that is well-suited for developing custom spiders and middlewares. It can be used for a wide range of projects, including scraping social media, tracking and analyzing data, and managing large web applications.
For more information on how to make the most of Scrapy, read the documentation and follow the tutorials. For help and support, the Scrapy community is a great place to go.