EA Standard Template Library VS nativejson-benchmark

> you can still use it with smart pointers provided by any other library

Is the point of having a kitchen-sink library like this not that you dont have to reach for a 3rdparty library for things that you need 'all the time'?

Certainly, not everyone needs it.

...but, not everyone needs threads either. Not everyone needs an http server; and yet, if you have an application framework that provides them, when you do need them, it saves you reaching for yet-another-dependency.

Was that no the point from the beginning?

unique_ptr is a fundamental primitive for many, as you see from some other frameworks (1), and implementation is not always either a) trivial, or b) as simple as 'just use std::unique_ptr'.

This does seem like a very opinionated decision with reasonably unclear justification.

[1] - eg. https://github.com/EpicGames/UnrealEngine/blob/release/Engin..., https://github.com/electronicarts/EASTL/blob/master/include/...

Can I introduce you to EASTL

For games focused stuff have a look at EASTL https://github.com/electronicarts/EASTL also perhaps some of the Data Oriented Design stuff (see Mike Acton's CPP Con Talks). This also have loads of good stuff https://www.dataorienteddesign.com/dodbook/

C++, but generally treat it as C. STL is pretty slow while debugging so we avoid it and write our own replacements. If you don't want to drive that deep use something like EASTL: https://github.com/electronicarts/EASTL

In my experience game devs don't eschew the STL because of compile times, but because (a) its memory management is not sufficiently tunable and (b) it uses exceptions heavily which many game engines disable entirely due to perceived performance problems. For this reason, some of the engines I've worked on have used their own forks of the STL in the spirit of EASTL to rectify these issues. Others like my current project just don't use the STL at all (outside of some third-party library code) and use custom libraries for everything.

In my opinion, you should also benchmark it against something like boost::container::flat_set or eastl::vector_set and you should expect the same performance as with your ordered functionality. Another interesting idea for organization of such flat and sorted container can be found here.

finally, if you're worried about performance (a lot of aspiring game programmers seem to be, even though for anything but high-end engines the STL is likely fine if you know what you're doing..) you could look into the EA STL or other STL replacements..

check it out: https://github.com/electronicarts/EASTL

I just took a look at their atomic.h and wow, "well-documented" is an understatement !

Well it would depend on the specifics of the JSON file but eyeballing the stats at https://github.com/miloyip/nativejson-benchmark/tree/master seems to indicate that even on a 2015 MacBook the parsing proceeds using e.g. Configuru parser at several megabytes per second.

Maybe you need to try some other third party json library and see if it helps. This is a good list https://github.com/miloyip/nativejson-benchmark

Glancing at some benchmarks, RapidJSON stringifies at around 250MB/s on a single core (content-dependent, of course). Does not look like a bottleneck.

How does it compare to the immensely popular JSON for Modern C++ library by nlohmann? https://github.com/nlohmann/json

Also, you should add your library to the JSON benchmarks here: https://github.com/miloyip/nativejson-benchmark#parsing-time

I like your ideas, but they seem difficult to enforce. It assumes good faith on all sides. One of the biggest complaints about AI/ML research results: It is frequently hard/impossible to replicate the results.

One idea: The edge competitors can create a public (SourceHut?) project that runs various daily tests against themselves. This would similar to JSON library benchmarks. [1] Then allow each competitors to continuously tweak there settings to accomplish the task in the shortest amount of time.

Also: It would be nice to see a cost analysis. For years, IBM's DB2 was insanely fast if you could afford to pay outrageous hardware, software license, and consulting costs. I'm not in the edge business, but I guess there are some operators where you can just pay a lot more and get better performance -- if you really need it.

[1] https://github.com/miloyip/nativejson-benchmark

There's some useful benchmarks here. I found it while looking for stats on json-c vs parson, which I've used a fair amount.

If you looking for benchmarks to show in which cases your library is better than other 30 or so competitors, then see this repo https://github.com/miloyip/nativejson-benchmark

JSON data files from this project: https://github.com/miloyip/nativejson-benchmark

Such a shame, really. There is a ton fast json parsers there, like https://github.com/miloyip/nativejson-benchmark#parsing-time. And second issue is just hilarious: let's scan array millions of times, who needs hashmaps anyway?