This is a great review of what is going on "behind the scenes." As the maintainer of about 5 little services with this structure I have vowed never to write an

This is a great review of what is going on "behind the scenes." As the maintainer of about 5 little services with this structure I have vowed never to write another one. The memory overhead alone is a source of eternal irritation ("Surely there must be a better way...."). Echoing other commenters here, the real cost isn't actually discussed. Namely that there is a solution to some of these problems (re long running tasks?), but it carries with it a major increase in complexity. Its name is Celery and oh boy have fun with the ops overhead that that is going to induce. A while back I did some unscientific benchmarking of the various worker classes for python3.6 and pypy3 (7.0 at the time I think?). Quoting my summary notes: 1. "pypy3 with sync worker has roughly the same performance, gevent is monstrously slow gthread is about 20 rps slower than sync (1s over 1k requests), sync can get up to ~150rps" 2. "pypy3 clearly faster with tornado than anything running 3.6" 3. "pypy3 is also about 4x faster when dumping nt straight from the database, peaking at about 80MBps to disk on the same computer while python3.6 hits ~20MBps" I won't mention the workload because it was the same for both implementations and would only confuse the point, which is that there are better solutions out there in python land if you are stuck with one of these systems. One thing I would love to hear from others is how other runtimes do this in a sane and performant way. What is the better solution left implicit in this post?