Scrapy Auto – How to Use the AutoThrottle Scrapy Auto Extension

Scrapy auto is an extension to the Scrapy web scraping framework that dynamically adjusts the crawling speed based on load of both the Scrapy server and the website you are scraping. This is useful for applications that need to keep crawling speed low in a real-world scenario where the crawling server might be very busy and unable to attend requests on average.

Using this extension is as simple as setting the maximum concurrent read more requests it allows, and then watching how the delays are adjusted to make sure they are in line with your requirements. You can also use the AutoThrottle debug mode to see stats on each response received so you can watch how this works in real time.

The AutoThrottle extension uses download latency to compute the throttling delay. The delay is calculated by sending a request each ‘latency’/N seconds, where N is the number of concurrent requests you want Scrapy to send in parallel at any given time point.

In order to make Scrapy work, you need to install Python (Python 2.7 and higher or Python 3.4 and higher – it should work with both) and the required libraries. Then, you can scrape data from websites and web services like Amazon API, Twitter/Facebook API.

For this, we need to define a Scrapy spider in the “spider” folder, which is used to scrape the data from a web page. We then need to specify the URL for which we want to scrape data, and the callback function that will be called when the webpage responds.

Once you’ve done that, you can use the XPath and CSS selectors to find elements from the web pages. These can be stored in an item object that contains the elements, their values and other properties. Then, the item can be exported in various formats such as JSON, CSV and XML depending on the ‘export_format’ option that is passed to the ‘output’ method.

The extracted data can be used to build various types of apps. We can write a program that displays all of the comments from Reddit’s GoT season 7 release on a screen, or we can scrape email addresses from a constituent page of a political party and display them on a screen to let people vote for a candidate.

One of the most important things that makes Scrapy so powerful is its ability to scrape multiple pages at the same time, a feature that other scraping libraries lack. This is especially helpful if you need to scrape hundreds of pages in a short amount of time.

You can then run the scraped data through several filters and export them to different formats such as CSV, XML or JSON. You can even set a custom filter to scrape specific information on a website such as whether it has an RSS feed, how many comments have been posted, and so on.

In addition to all this, Scrapy can be run asynchronously which means that it can process requests and responses in parallel, and therefore be very efficient when a large number of pages need to be downloaded at the same time. This is a big advantage in terms of performance, and it is why we recommend that Scrapy be used when scraping large websites.