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All running in a single thread - but the work is executed asynchronously. This is preferred for I/O heavy throughput such as many HTTP requests.

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Why not distribute the concurrent work over multiple processes to get the most out of multiple cores? More event loops, better performance, and maybe more text preprocessing.

That would be a good idea, but it's non-trivial in Python as multi-processing async support is patchy. If you have to choose multi-processing vs async for HTTP requests, from experience, async is much faster (as your not CPU bound but IO bound) and easier to use.

I do both when I'm scaling to millions of requests per day. I also do some cpu bound preprocessing after the io bound async functions end.

Look into Go, specifically the colly pkg. Learnt Go just so I could use this library after wrestling with highly parralelized Python web scraping. The 1k/requests/sec/core figure is true and frankly deadly, and Go borrows a lot of useful stuff from Python like array slicing.

It was pretty remarkable to go from 32 cores being maxed for 100 concurrent workers to... A 10-15% usage spike on a 4 core XPS :)

https://github.com/gocolly/colly

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