> From my experience it’s really hard to make async outperform sync when databases are involved because the async layer adds so much overhead

Highly disagree as the database is just another IO connection to a server, which is asyncio bread and butter. Being able to stream data from longer running queries without buffering and whilst serving other requests (and making other queries) is really quite powerful.

But yeah, if you're maxing out your database with sync code then async isn't going to make it magically go faster.

Hi, take a look at my benchmarks from five years ago at https://techspot.zzzeek.org/2015/02/15/asynchronous-python-a.... The extra variable with Python is that it's a very CPU-heavy interpreted language and it's really unlikely for an application to be significantly IO bound with a database server on the same network within the realm of CRUD-style queries. asyncio was significantly slower than threads (noting they've made performance improvements since then) and gevent was about the same (which I'm pretty sure is close to as fast as you can get for async in Python).

The database is mostly just idle IO. You send a query and then you wait for results. That’s something sync python is decent at because when you wait for that IO the GIL is released. The situation is different if there is a lot of activity on the epoll/kqueue etc. (connects, data ready etc.).

Apologies - I completely misread your initial comment. Yeah that's correct.

Despite this I think it's quite rare to hit this limit, at least in the orchestration-style use cases I use asyncio for. With those I value making independent progress on a number of async tasks rather than potentially being blocked waiting for a worker thread to become available.