defer | : soon : :bomb : Deferrable errors | SMS library

Simply decoding to the struct and encoding again will satisfy your goal.

Please check this: https://go.dev/play/p/69vjlve4P6p

this looks like a simplified version of what the reactor-pool does, in essence. have you considered using that with eg. a maximum size of 1?

https://github.com/reactor/reactor-pool/

https://projectreactor.io/docs/pool/0.2.7/api/reactor/pool/Pool.html

The pool is probably overkill, because it has the overhead of having to deal with multiple resources on top of multiple competing borrowers like in your case, but maybe it could provide some inspiration for you to go further.

In some sense, the mess you experience is a consequence of Kotlin coroutines having been an early success, before they became stable. In their experimental days, one thing they lacked was structured concurrency, and a ton of web material got written about them in that state (such as your link 1 from 2017). Some of the then-valid preconceptions remained with people even after their maturation, and got perpetuated in even more recent posts.

The actual situation is quite clear — all you have to understand is coroutine hierarchy, which is mediated through the Job objects. It doesn't matter whether it's a launch or an async, or any further coroutine builder — they all behave uniformly.

With this in mind, let's go through your examples:

I just solve this issue by correcting the RxJS version to 7.4.0. I hope this can solve others issue as well.

The 2 solutions are not very different. Solution #2 fits better to your question, but choose whatever you prefer.

Solution 1 - create a response with the html's body from the driver and scraping it right away (you can also pass it as an argument to a function):

async/await might not be the proper paradigm if you want cancellation. The reason is that the new structured concurrency support in Swift allows you to write code that looks single-threaded/synchronous, but it fact it's multi-threaded.

Take for example a naive synchronous code:

change focus happens when mousedown event fires on element, you can use mousedown event handler to prevent change the active element.

In theory, as you say, CPython is designed to be buildable and usable on any platform without caring about what floating-point format their C double is using.

In practice, two things are true:

• To the best of my knowledge, CPython has not met a system that's not using IEEE 754 binary64 format for its C double within the last 15 years (though I'd love to hear stories to the contrary; I've been asking about this at conferences and the like for a while). My knowledge is a long way from perfect, but I've been involved with mathematical and floating-point-related aspects of CPython core development for at least 13 of those 15 years, and paying close attention to floating-point related issues in that time. I haven't seen any indications on the bug tracker or elsewhere that anyone has been trying to run CPython on systems using a floating-point format other than IEEE 754 binary64.

• I strongly suspect that the first time modern CPython does meet such a system, there will be a significant number of test failures, and so the core developers are likely to find out about it fairly quickly. While we've made an effort to make things format-agnostic, it's currently close to impossible to do any testing of CPython on other formats, and it's highly likely that there are some places that implicitly assume IEEE 754 format or semantics, and that will break for something more exotic. We have yet to see any reports of such breakage.

There's one exception to the "no bug reports" report above. It's this issue: https://bugs.python.org/issue27444. There, Greg Stark reported that there were indeed failures using VAX floating-point. It's not clear to me whether the original bug report came from a system that emulated VAX floating-point.

I joined the CPython core development team in 2008. Back then, while I was working on floating-point-related issues I tried to keep in mind 5 different floating-point formats: IEEE 754 binary64, IBM's hex floating-point format as used in their zSeries mainframes, the Cray floating-point format used in the SV1 and earlier machines, and the VAX D-float and G-float formats; anything else was too ancient to be worth worrying about. Since then, the VAX formats are no longer worth caring about. Cray machines now use IEEE 754 floating-point. The IBM hex floating-point format is very much still in existence, but in practice the relevant IBM hardware also has support for IEEE 754, and the IBM machines that Python meets all seem to be using IEEE 754 floating-point.

Rather than exotic floating-point formats, the modern challenges seem to be more to do with variations in adherence to the rest of the IEEE 754 standard: systems that don't support NaNs, or treat subnormals differently, or allow use of higher precision for intermediate operations, or where compilers make behaviour-changing optimizations.

The above is all about CPython-the-implementation, not Python-the-language. But the story for the Python language is largely similar. In theory, it makes no assumptions about the floating-point format. In practice, I don't know of any alternative Python implementations that don't end up using an IEEE 754 binary format (if not semantics) for the float type. IronPython and Jython both target runtimes that are explicit that floating-point will be IEEE 754 binary64. JavaScript-based versions of Python will similarly presumably be using JavaScript's Number type, which is required to be IEEE 754 binary64 by the ECMAScript standard. PyPy runs on more-or-less the same platforms that CPython does, with the same floating-point formats. MicroPython uses single-precision for its float type, but as far as I know that's still IEEE 754 binary32 in practice.

The only plausible, general approach is to rewrite all relevant code to not only use _ to request translation but to never cache the result. That’s not a fun idea and it’s not a new idea—you already list Refactoring and Deferred translation that rely on the cooperation of the gettext clients—but it is the “best way […] in practice”.

You can try to do a super-reload by removing many things from sys.modules and then doing a real reimport. This approach avoids understanding the import relationships, but works only if the relevant modules are all written in Python and you can guarantee that the state of your program will retain no references to any objects (including types and modules) that used the old language. (I’ve done this, but only in a context where the overarching program was a sort of supervisor utterly uninterested in the features of the discarded modules.)

You can try to walk the whole object graph and replace the strings, but even aside from the intrinsic technical difficulty of such an algorithm (consider __slots__ in base classes and co_consts for just the mildest taste), it would involve untranslating them, which changes from hard to impossible when some sort of transformation has already been performed. That transformation might just be concatenating the translated strings, or it might be pre-substituting known values to format, or padding the string, or storing a hash of it: it’s certainly undecidable in general. (I’ve done this too for other data types, but only with data constructed by a file reader whose output used known, simple structures.)

Any approach based on partial reevaluation combines the problems of the methods above.

The only other possible approach is a super-LazyString that refuses to translate for longer by implementing operations like + to return objects that encode the transformations to eventually apply, but it’s impossible to know when to force those operations unless you control all mechanisms used to display or transmit strings. It’s also impossible to defer past, say, if len(_("…"))>80:.