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When it comes to programming, I generally decide my thoughts based on pain-in-my-ass levels. If I constantly have to fiddle with something to get it working, if

When it comes to programming, I generally decide my thoughts based on pain-in-my-ass levels. If I constantly have to fiddle with something to get it working, if it's fragile, if it frequently becomes a pain point - then it's not great. And out of all the tools and architecture I work with, C++ has been some of the least problematic. The STL is well-formed and easy to work with, creating user-defined types is easy, it's fast, and generally it has few issues when deploying. If there's something I need, there's a very high chance a C or C++ library exists to do what I need. Even crossing multiple major compiler versions doesn't seem to break anything, with rare exceptions. The biggest problem I have with C++ is how easy it is to get very long compile times, and how hard it feels like it is to analyze and fix that on a 'macro' (whole project) level. I waste ungodly amounts of time compiling. I swear I'm going to be on deaths door and see GCC running as my life flashes by. Some others that have been not-so-nice: * Python - Slow enough to be a bottleneck semi-frequently, hard to debug especially in a cross-language environment, frequently has library/deployment/initialization problems, and I find it generally hard to read because of the lack of types, significant whitespace, and that I can't easily jump with an IDE to see who owns what data. Also pip is demon spawn. I never want to see another Wheel error until the day I die. * VSC's IntelliSense - My god IntelliSense is picky. Having to manually specify every goddamn macro, one at a time in two different locations just to get it to stop breaking down is a nightmare. I wish it were more tolerant of having incomplete information, instead of just shutting down completely. * Fortran - It could just be me, but IDEs struggle with it. If you have any global data it may as well not exist as far as the IDE is concerned, which makes dealing with such projects very hard. * CMake - I'm amazed it works at all. It looks great for simple toy projects and has the power to handle larger projects, but it seems to quickly become an ungodly mess of strange comments and rules that aren't spelled out - and you have no way of stepping into it and seeing what it's doing. I try to touch it as infrequently as possible. It feels like C macros, in a bad way.

bluGill 1y ago
CMake is not a great language, but great effort has been put into cleaning up how things should be done. However you can't just upgrade, someone needs to go through the effort of using all that new stuff. In almost all projects the build system is an after thought that developers touch as little as possible to make things work and so it builds cruft constantly. You can do much better in CMake if you put some effort into cleaning it up - I have little hope anyone will do this though. We have a hard time getting developers to clean up messes in production code and that gets a lot more care and love.
- palata 1y ago
  I agree. Unless the project is huge, it's totally possible to use CMake in a maintainable way. It just requires some effort (not so much, but not nothing).
zahlman 1y ago
> I never want to see another Wheel error until the day I die. What exactly do you mean by a "Wheel error"? Show me a reproducer and a proper error message and I'll be happy to help to the best of my ability. By and large, the reason pip fails to install a package is because doing so requires building non-Python code locally, following instructions included in the package. Only in rare cases are there problems due to dependency conflicts, and these are usually resolved by creating a separate environment for the thing you're trying to install — which you should generally be doing anyway. In the remaining cases where two packages simply can't co-exist, this is fundamentally Python's fault, not the installer's: module imports are cached, and quite a lot of code depends on the singleton nature of modules for correctness, so you really can't safely load up two versions of a dependency in the same process, even if you hacked around the import system (which is absolutely doable!) to enable it. As for finding significant whitespace (meaning indentation used to indicate code structure; it's not significant in other places) hard to read, I'm genuinely at a loss to understand how. Python has types; what it lacks is manifest typing, and there are many languages like this (including Haskell, whose advocates are famous for explaining how much more "typed" their language is than everyone else's). And Python has a REPL, the -i switch, and a built-in debugger in the standard library, on top of not requiring the user to do the kinds of things that most often need debugging (i.e. memory management). How can it be called hard to debug?
- Night_Thastus 1y ago
  Unfortunately that Wheel situation was far enough back now that I don't have details on hand. I just know it was awful at the time. As for significant whitespace, the problem is that I'm often dealing with files with several thousand lines of code and heavily nested functions. It's very easy to lose track of scope in that situation. Am I in the inner loop, or this outer loop? Scrolling up and down, up and down to figure out where I am. Feels easier to make mistakes as well. It works well if everything fits on one screen, it gets harder otherwise, at least for me. As for types, I'm not claiming it's unique to Python. Just that it makes working with Python harder for me. Being able to see the type of data at a glance tells me a LOT about what the code is doing and how it's doing it - and Python doesn't let me see this information. As for debugging, it's great if you have pure Python. Mix other languages in and suddenly it becomes pain. There's no way to step from another language into Python (or vice-versa), at least not cleanly and consistently. This isn't always true for compiled->compiled. I can step from C++ into Fortran just fine.
ryao 1y ago
If you are willing to give up incremental compilation, concatenating all C++ files into a single file and compiling that on a single core will often outperform a multi-core compilation. The reason is that the compiler spends most of its time parsing headers and when you concentrate everything into a single file (use the C preprocessor for this), it only needs to parse headers once. Merely parsing C++ code requires a higher time complexity than parsing C code (linear time parsers cannot be used for C++), which is likely where part of the long compile times originate. I believe the parsing complexity is related to templates (and the headers are full of them), but there might be other parts that also contribute to it. Having to deal with far more abstractions is likely another part. That said, I have been incrementally rewriting a C++ code base at a health care startup into a subset of C with the goal of replacing the C++ compiler with a C compiler. The closer the codebase comes to being C, the faster it builds.
kergonath 1y ago
> Fortran - It could just be me, but IDEs struggle with it. If you have any global data it may as well not exist as far as the IDE is concerned, which makes dealing with such projects very hard. You really should not have global data. Modules are the way to go and have been since Fortran90. > CMake - I'm amazed it works at all. It looks great for simple toy projects and has the power to handle larger projects, but it seems to quickly become an ungodly mess of strange comments and rules that aren't spelled out - and you have no way of stepping into it and seeing what it's doing. I try to touch it as infrequently as possible. It feels like C macros, in a bad way. I like how you wrote my feelings so accurately :D
- Night_Thastus 1y ago
  >You really should not have global data. Modules are the way to go and have been since Fortran90. Legacy code, just have to deal with it. This code predates F90.

bluGill 1y ago

CMake is not a great language, but great effort has been put into cleaning up how things should be done. However you can't just upgrade, someone needs to go through the effort of using all that new stuff. In almost all projects the build system is an after thought that developers touch as little as possible to make things work and so it builds cruft constantly. You can do much better in CMake if you put some effort into cleaning it up - I have little hope anyone will do this though. We have a hard time getting developers to clean up messes in production code and that gets a lot more care and love.

palata 1y ago
I agree. Unless the project is huge, it's totally possible to use CMake in a maintainable way. It just requires some effort (not so much, but not nothing).

zahlman 1y ago

> I never want to see another Wheel error until the day I die. What exactly do you mean by a "Wheel error"? Show me a reproducer and a proper error message and I'll be happy to help to the best of my ability. By and large, the reason pip fails to install a package is because doing so requires building non-Python code locally, following instructions included in the package. Only in rare cases are there problems due to dependency conflicts, and these are usually resolved by creating a separate environment for the thing you're trying to install — which you should generally be doing anyway. In the remaining cases where two packages simply can't co-exist, this is fundamentally Python's fault, not the installer's: module imports are cached, and quite a lot of code depends on the singleton nature of modules for correctness, so you really can't safely load up two versions of a dependency in the same process, even if you hacked around the import system (which is absolutely doable!) to enable it. As for finding significant whitespace (meaning indentation used to indicate code structure; it's not significant in other places) hard to read, I'm genuinely at a loss to understand how. Python has types; what it lacks is manifest typing, and there are many languages like this (including Haskell, whose advocates are famous for explaining how much more "typed" their language is than everyone else's). And Python has a REPL, the -i switch, and a built-in debugger in the standard library, on top of not requiring the user to do the kinds of things that most often need debugging (i.e. memory management). How can it be called hard to debug?

Night_Thastus 1y ago
Unfortunately that Wheel situation was far enough back now that I don't have details on hand. I just know it was awful at the time. As for significant whitespace, the problem is that I'm often dealing with files with several thousand lines of code and heavily nested functions. It's very easy to lose track of scope in that situation. Am I in the inner loop, or this outer loop? Scrolling up and down, up and down to figure out where I am. Feels easier to make mistakes as well. It works well if everything fits on one screen, it gets harder otherwise, at least for me. As for types, I'm not claiming it's unique to Python. Just that it makes working with Python harder for me. Being able to see the type of data at a glance tells me a LOT about what the code is doing and how it's doing it - and Python doesn't let me see this information. As for debugging, it's great if you have pure Python. Mix other languages in and suddenly it becomes pain. There's no way to step from another language into Python (or vice-versa), at least not cleanly and consistently. This isn't always true for compiled->compiled. I can step from C++ into Fortran just fine.

ryao 1y ago

If you are willing to give up incremental compilation, concatenating all C++ files into a single file and compiling that on a single core will often outperform a multi-core compilation. The reason is that the compiler spends most of its time parsing headers and when you concentrate everything into a single file (use the C preprocessor for this), it only needs to parse headers once. Merely parsing C++ code requires a higher time complexity than parsing C code (linear time parsers cannot be used for C++), which is likely where part of the long compile times originate. I believe the parsing complexity is related to templates (and the headers are full of them), but there might be other parts that also contribute to it. Having to deal with far more abstractions is likely another part. That said, I have been incrementally rewriting a C++ code base at a health care startup into a subset of C with the goal of replacing the C++ compiler with a C compiler. The closer the codebase comes to being C, the faster it builds.

kergonath 1y ago

> Fortran - It could just be me, but IDEs struggle with it. If you have any global data it may as well not exist as far as the IDE is concerned, which makes dealing with such projects very hard. You really should not have global data. Modules are the way to go and have been since Fortran90. > CMake - I'm amazed it works at all. It looks great for simple toy projects and has the power to handle larger projects, but it seems to quickly become an ungodly mess of strange comments and rules that aren't spelled out - and you have no way of stepping into it and seeing what it's doing. I try to touch it as infrequently as possible. It feels like C macros, in a bad way. I like how you wrote my feelings so accurately :D

Night_Thastus 1y ago
>You really should not have global data. Modules are the way to go and have been since Fortran90. Legacy code, just have to deal with it. This code predates F90.