JDK 24: 50 ms (using Threads, not yet VirtualThreads)
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 hands the stats back to
the main thread that did the await, and the main
thread stops his waiting and continues to run
cooperatively with the other tasks in the current
event loop. The test case measures the wall time.
The results are:
node.js: 10 ms (usual Promises and stuff)
JDK 24: 50 ms (using Threads, not yet VirtualThreads)
pypy: 2000 ms
I am only using one main task, sequentially on
such await calles, with a couple of file, not
more than 50 files.
I could compare with removing the async detour,
to qualify the async I/O detour overhead.
Mild Shock schrieb:
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 async since the same file property routine
might also do a http HEAD request. But what I was
testing and comparing was a simple file system access
inside a wrapped thread, that is async awaited.
Such a thread is called for a couple of directory
entries to check a directory tree whether updates
are need. Here some measurement doing this simple
involving some little async I/O:
node.js: 10 ms (usual Promises and stuff)
JDK 24: 50 ms (using Threads, not yet VirtualThreads)
pypy: 2000 ms
So currently PyPy is 200x times slower than node.js
when it comes to async I/O. No files were read or
written in the test case, only "mtime" was read,
via this Python line:
stats = await asyncio.to_thread(os.stat, url)
Bye
Mild Shock schrieb:
Concerning virtual threads the only problem
with Java I have is, that JDK 17 doesn't have them.
And some linux distributions are stuck with JDK 17.
Otherwise its not an idea that belongs solely
to Java, I think golang pioniered them with their
goroutines. I am planning to use them more heavily
when they become more widely available, and I don't
see any principle objection that Python wouldn't
have them as well. It would make async I/O based
on async waithing for a thread maybe more lightweight.
But this would be only important if you have a high
number of tasks.
Lawrence D'Oliveiro schrieb:
Short answer: no.
<https://discuss.python.org/t/add-virtual-threads-to-python/91403>
Firstly, anybody appealing to Java as an example of how to design a
programming language should immediately be sending your bullshit
detector
into the yellow zone.
Secondly, the link to a critique of JavaScript that dates from 2015,
from
before the language acquired its async/await constructs, should be
another
warning sign.
Looking at that Java spec, a “virtual thread” is just another name for >>>> “stackful coroutine”. Because that’s what you get when you take away >>>> implicit thread preemption and substitute explicit preemption instead. >>>>
The continuation concept is useful in its own right. Why not
concentrate
on implementing that as a new primitive instead?
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 Prolog:
JDK 24: 30 ms (no Threads)
But already the using Threads version is quite optimized,
it basically reuse its own thread and uses a mutex
somewhere, so it doesn't really create a new secondary
thread, unless a new task is spawn. Creating a 2nd thread
is silly if task have their own thread. This is the
main potential of virtual threads in upcoming Java,
just run tasks inside virtual threads.
Bye
P.S.: But I should measure with more files, since
the 50 ms and 30 ms are quite small. Also I am using a
warm run, so the files and their meta information is already
cached in operating system memory. I am trying to only
measure the async overhead, but maybe Python doesn't trust
the operating system memory, and calls some disk
sync somewhere. I don't know. I don't open and close the
files, and don't call some disk syncing. Only reading
stats to get mtime and doing some comparisons.
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
Mild Shock schrieb:
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 Prolog:
JDK 24: 30 ms (no Threads)
But already the
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