Scrapy auto is a library to automate the validation and testing of scraping output by running it through middlewares, which can be configured to produce fixtures and testcases.
It can also pause and resume crawls for large spiders and adjust their rate dynamically based on load, ensuring that the scraper always performs at an ideal speed.
The first thing to know is that scrapy works on asynchronous processing, which means it doesn’t wait for the response from each request it makes, but instead continues on to the next task. This is especially useful when you’re requesting a large number of pages in parallel, as it allows the engine to work much faster than the hardware can handle.
You can also set allowed_domains which tells the crawler to only scrape domains from a specified list. This is a good safety feature to avoid accidental errors where the spider might accidentally wander off and scrape some other website by accident.
Another handy feature that Scrapy has is a way to automatically rotate proxies, which means that it can use a different proxy for every request sent. This is a great feature for avoiding websites being blocked due to too many requests, as it spreads the traffic and load out over a number of servers.
Alternatively, you can specify a range of requests that can be made at any given time by using the auto throttle setting. This allows the tool to adjust its speed based on the amount of traffic and load it’s receiving on the target website, so it doesn’t send too many requests at one time, thereby causing the site to get a higher number of hits.
This can be particularly helpful if you’re a scraper that doesn’t have any custom logic to validate the data, for example, when you’re simply iterating over a list of results and printing them out on the terminal.
It’s a good idea to check the log files from each spider that you’re using, as this will give you an idea of how it’s performing. It’s also a good idea to run a few of the same spiders at the same time, just to make sure that they are all functioning properly.
You can also use the scrapy parse command to verify that the output of your scrapers passes certain conditions. This is a good practice, as it will ensure that you’re not writing arbitrary test cases that don’t make sense in your application.
Lastly, scrapy has a built in system to store the scraped information in Items, which are similar to Python dictionaries but allow you to store multiple fields on each Item. This can be very useful when it comes to validating that all the information you’ve scraped is correct, and if you need to repeat scraping processes or want to do something with the data.
As well as the built in mechanisms for storing data, Scrapy can also be used to export it to various formats including JSON, CSV and XML. This can be very useful if you need to store the scraped information in a format that’s easier for other applications to read and use.