Hi,
I tested this one:
Python 3.11.11 (0253c85bf5f8, Feb 26 2025, 10:43:25)
[PyPy 7.3.19 with MSC v.1941 64 bit (AMD64)] on win32
I didn't test yet this one, because it is usually slower:
ython 3.14.0b2 (tags/v3.14.0b2:12d3f88, May 26 2025, 13:55:44)
[MSC v.1943 64 bit (AMD64)] on win32
Bye
I honestly have no idea what's being measured, but here are some
numbers to compare this to, and then some explanation about async I/O
in general.
1. No I/O to a local disk on a modern controller should take
milliseconds. The time you are aiming for is below millisecond. That
is, writing a block t
Hi,
I have some data what the Async Detour usually
costs. I just compared with another Java Prolog
that didn't do the thread thingy.
Reported measurement with the async Java Prolog:
> JDK 24: 50 ms (using Threads, not yet VirtualThreads)
New additional measurement with an alternative Java Prol
Other languages uses thread pool, instead of creating new thread.
In Python,loop.run_in_executor uses thread pool.
https://docs.python.org/3.13/library/asyncio-eventloop.html#asyncio.loop.run_in_executor
2025年6月24日(火) 8:12 Mild Shock :
>
> So what does:
>
> stats = await asyncio.to_thread(os.sta
So what does:
stats = await asyncio.to_thread(os.stat, url)
Whell it calls in a sparate new secondary thread:
os.stat(url)
It happends that url is only a file path, and
the file path points to an existing file. So the
secondary thread computs the stats, and terminates,
and the async framework
Hi,
async I/O in Python is extremly disappointing
and an annoying bottleneck.
The problem is async I/O via threads is currently
extremly slow. I use a custom async I/O file property
predicate. It doesn't need to be async for file
system access. But by some historical circumstances
I made it asy
