From Newsgroup: comp.lang.python

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?
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

Lawrence D'Oliveiro <ldo@nz.invalid> writes:

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.

Try using Erlang a little, It has preemptive lightweight processes and
it is great. Much better than async/await imho.
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python


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?

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

On Sat, 14 Jun 2025 04:29:07 -0700, Paul Rubin wrote:

Try using Erlang a little, It has preemptive lightweight processes and
it is great. Much better than async/await imho.

Those are called “threads”. Python already has those, and the ongoing “noGIL” project will make them even more useful.

There’s a reason why the old coroutine concept was brought back (albeit in this new “stackless” guise): because threads are not the best answer to everything.
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

Lawrence D'Oliveiro <ldo@nz.invalid> writes:

Try using Erlang a little, It has preemptive lightweight processes and
it is great. Much better than async/await imho.

Those are called “threads”. Python already has those, and the ongoing “noGIL” project will make them even more useful.

Erlang's lightweight processes are called "processes" rather than
"threads" since they don't give the appearance of having shared memory.
They communicate by passing data through channels. From the
application's perspective, that is always done by copying the data,
although the VM sometimes optimizes away the copying behind the scenes.

Python has OS threads but they are way more expensive than Erlang
processes. Programming with them in an Erlang-like style still can work
pretty well.
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

On Sat, 14 Jun 2025 18:25:26 -0700, Paul Rubin wrote:

Erlang's lightweight processes are called "processes" rather than
"threads" since they don't give the appearance of having shared memory.
They communicate by passing data through channels. From the
application's perspective, that is always done by copying the data,
although the VM sometimes optimizes away the copying behind the scenes.

Python has OS threads but they are way more expensive than Erlang
processes.

Sharing process context is cheaper than having to keep copying data back
and forth. Clever tricks with the paging hardware can often be more
trouble than they’re worth.

Remember, Python’s threads are OS threads. If you’re thinking “expensive”,
you must be assuming “Microsoft Windows”.
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

Lawrence D'Oliveiro <ldo@nz.invalid> writes:

Remember, Python’s threads are OS threads. If you’re thinking “expensive”,
you must be assuming “Microsoft Windows”.

Let's see how CPython holds up with a million OS threads running. Even
being able to disable the GIL and use more than one core is very recent.
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

On Sun, 15 Jun 2025 13:24:56 -0700, Paul Rubin wrote:

Lawrence D'Oliveiro <ldo@nz.invalid> writes:

Remember, Python’s threads are OS threads. If you’re thinking
“expensive”, you must be assuming “Microsoft Windows”.

Let's see how CPython holds up with a million OS threads running.

Linux can already run hundreds of thousands of processes/threads (there’s not a lot of difference between the two on Linux). Remember why pid_t is
32 bits, not 16 bits.

See the definition of /proc/sys/kernel/threads-max <https://manpages.debian.org/proc_sys_kernel(5)>.
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

Lawrence D'Oliveiro <ldo@nz.invalid> writes:

Let's see how CPython holds up with a million OS threads running.

Linux can already run hundreds of thousands of processes/threads

To misquote Austin Powers, "one MILLLLLION threads". Here Erlang
does it in less than 1GB of memory:

https://hauleth.dev/post/beam-process-memory-usage/
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

On Sun, 15 Jun 2025 14:33:28 -0700, Paul Rubin wrote:

Lawrence D'Oliveiro <ldo@nz.invalid> writes:

Let's see how CPython holds up with a million OS threads running.

Linux can already run hundreds of thousands of processes/threads

To misquote Austin Powers, "one MILLLLLION threads". Here Erlang does
it in less than 1GB of memory:

https://hauleth.dev/post/beam-process-memory-usage/

Just a note that Erlang dates from before the current state of CPU architectures, where you have a 100:1 disparity between RAM-access speeds
and CPU register-access speeds.

In other words, you do not want to copy stuff between processes if you can help it. With threads sharing common memory, that data can reside in
caches that multiple threads in the same process context can share without copying.
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

Lawrence D'Oliveiro <ldo@nz.invalid> writes:

In other words, you do not want to copy stuff between processes if you can help it.

I'd be interested in seeing some benchmarks of multi-threaded Python
beating Erlang, if you have any to show. Otherwise, you are guessing.
Stuff copied between Erlang processes tends to be pretty small, fwiw.
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

On Sun, 15 Jun 2025 21:02:46 -0700, Paul Rubin wrote:

I'd be interested in seeing some benchmarks of multi-threaded Python
beating Erlang, if you have any to show.

Since you ask, I tried running up a simple program that creates lots of
dummy threads that do nothing but sleep for a few seconds, and reports on
its RAM usage by reading /proc/self/statm.

I am currently up to a bit over 25,000 threads (the default limit is
somewhere just under 26,000). The program reports its VM usage as over
200GB, which is way more than my total RAM + swap space, but in fact the free(1) command reports that RAM and swap usage are nowhere that high. The resident RAM usage while all the threads are running is reported at about 430MB.

In other words, multiply that by 40, and a million threads should get the program’s RAM usage up to maybe 18GB.
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

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?

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

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?

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

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.

Mild Shock schrieb:

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?

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

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 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.

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: comp.lang.python

Hi,

Everybody who puts me personally on CC: , and
posts form python-list@python.org . Please note,
I cannot respond on python-list@python.org .

Somebody blocked me on python-list@python.org .
If you want a discussion, post on comp.lang.python .
And stop spamming me with your CC: .

Bye

P.S.: BTW, I got blocked after this moron wrote
this nonsense. It is complete nonsense, now
that everybody is talking about AsyncAPI, and

since Dogelog Player evolved into Async, simply
by its 2nd target JavaScript. What company was he
working for? A looser company Teledyne ?

------------------- begin moron ---------------------

Opinion: Anyone who is counting on Python for truly
fast compute speed is probably using Python for the
wrong purpose. Here, we use Python to control Test
Equipment, to set up the equipment and ask for a
measurement, get it, and proceed to the next measurement;
and at the end produce a nice formatted report. If we
wrote the test script in C or Rust or whatever it
could not run substantially faster because it is
communicating with the test equipment, setting it up
and waiting for responses, and that is where the vast
majority of the time goes. Especially if the measurement
result requires averaging it can take a while. In my
opinion this is an ideal use for Python, not just
because the speed of Python is not important, but also
because we can easily find people who know Python, who
like coding in Python, and will join the company
to program in Python ... and stay with us.
--- Joseph S.

Teledyne Confidential; Commercially Sensitive Business Data

https://mail.python.org/archives/list/python-list@python.org/thread/RWEKXFW4WED7KNI67QBMDTC32EAEU3ZT/

------------------- end moron -----------------------


Mild Shock schrieb:

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 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.

--- Synchronet 3.21a-Linux NewsLink 1.2