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Scrapy Auto – A Beginner’s Guide to Web Scraping

Web scraping is the process of collecting and storing data from websites. This is usually done through a program that makes HTTP requests to the site and manipulates the responses.

The process of web scraping can be difficult or time consuming, but it can yield a great deal of information. If you are planning on using scrapy auto, here are a few things that you can start working with right away:


There are many different spiders that you can use when building your scraping application. The most common ones are XPath, CSS selectors and Regular Expressions. Besides these, there are also other ways of extracting data such as using data classes and custom code.

Output files

After extracting data, you can save it in a variety of formats, including JSON, CSV, XML and Pickle. In addition, you can also write your own Python code to manipulate and extract the data in an appropriate manner.

Managing multiple spiders

In a typical real-world scenario, you may need to have many scrapers running for dedicated purposes. In such cases, Scrapy provides a way to manage all the spiders in one place.

It can be used to list the available spiders in a project, as well as in a global scope.

You can also create a new spider by providing it with the parameters of your choice. You can use this to scrape all the listings from Boston Realty Advisors website (Figure 1).

Spiders are defined in a scrapy hamilton class, which you define with the name and parameters that you provide. This helps you differentiate between scrapers and makes it easier to locate them.

A Spider consists of a callback function and the address of the webpage that the spider will crawl. It can be run from a URL or a file. You can specify a start_urls field to give the spider a starting page.

It also supports multiple calls to the same callback and can be customised by passing in arguments. You can also specify a max depth, which will ensure that the spider does not crawl too many pages at once.

Throttle based on load

The Scrapy engine can send multiple requests in parallel, which is very useful for large data volumes. Its scheduler dispatches these requests when they are needed by the engine, which improves the overall speed of processing.

Download latency is another key factor for efficient web scraping, and Scrapy has the ability to dynamically adjust throttling delays so that the user doesn’t have to tune them to find the optimum setting.

An extension called Automatic Throttle is available for this purpose. It automatically throttles the crawling rate according to both the website that is being scraped and the Scrapy server.

This can be set up by providing a maximum number of concurrent requests and the delay between each request in seconds. The extension then calculates a sane delay to use and adjusts it.

Interactive shell

Unlike the other asynchronous libraries, scrapy offers an interactive shell for manipulating XPath expressions and CSS selectors. This can be very useful when you have a lot of scraping projects and want to tweak the XPath expressions to get the best results.