SpacetimeDB VS ffi-overhead

I don't use Rust for game dev but I do for low level libraries and find it easier than C++ to get started. I have enjoyed it more than Java and like it for different reasons than Go, but it feels good to program in.

As for the design patterns that a complex game requires, if you are considering Rust for game dev and ecs design patterns it might be useful to check out projects that are Rust centric like https://spacetimedb.com/.

Yeah, I worked on https://tablam.org and https://spacetimedb.com.

It becomes pretty clear that `order` is a significant property to make useful (and performant!) programs. "Duplicates" is also required to make usefull programs.

One nonobvious reason for this: You wanna report that a `customer` has a duplicated key `1`. If you CAN'T model `[(customer.id = 1), (customer.id = 1)]` then you can't report errors! And `erroneous` data is VITAL to make useful programs because then the only possibility is "perfect" data, and that is not possible!

Another reason is that we want to `count` duplicates, to see `duplicates`, and other NON-obvious at first: "What is a duplicate?". Get fun with floats, Unicode, combining case and non-case sensitive input... and is obvious that for useful programs IS REQUIRED to support bags in an extended version of the relational model.

And yet...

IS very important to remember about `set semantics` and try to adhere to it when makes sense. Your query planner will like it. You "valid" constraints like it. And `unique index` like it. And so on...

> I asked was about querying data without ever using a SQL language, like tapping directly into the data.

I agree (making https://tablam.org to try a fix & working on https://github.com/clockworklabs/SpacetimeDB in the SQL conformance).

Before I think SQL was bad. *Now I'm certain*. SQL is absurdly massive for things that could have collapse all the features 10x or more.

However, working in an RDBM now I also understand why is not desirable to make "raw" calls to the DB: The engine MUST mediate all the calls to make things works (from query optimization, execution, iteration, lock management, transaction management, etc).

Is incredible how much sophistication is in a simple `SELECT * FROM table`.

What I wish is to build a `Wasm-like` IR so that is what anybody target, and `SQL` is not the mediator.

I wouldn't ordinarily chirp about this because it seems to be such a common typo/mistake but the fact you have a badge for it <https://github.com/clockworklabs/SpacetimeDB/blob/0f1fdf62d0...> as well as typoing it down in the license section <https://github.com/clockworklabs/SpacetimeDB/blob/0f1fdf62d0...> makes it worth pointing out in hopes of correction

The SPDX for BUsiness Source License is BUSL https://spdx.org/licenses/BUSL-1.1.html but the SPDX for Boost Source License is BSL https://spdx.org/licenses/BSL-1.0.html

Based on a search <https://shields.io/search?q=license> it seems you're using the custom badge syntax <https://shields.io/badges/static-badge> so you have influence over the correction

The overhead for Go in benchmarks is insane in contrast to other languages - https://github.com/dyu/ffi-overhead Are there reasons why Go does not copy what Julia does?

What about the other benchmarks on the same site? https://docs.sciml.ai/SciMLBenchmarksOutput/stable/Bio/BCR/ BCR takes about a hundred seconds and is pretty indicative of systems biological models, coming from 1122 ODEs with 24388 terms that describe a stiff chemical reaction network modeling the BCR signaling network from Barua et al. Or the discrete diffusion models https://docs.sciml.ai/SciMLBenchmarksOutput/stable/Jumps/Dif... which are the justification behind the claims in https://www.biorxiv.org/content/10.1101/2022.07.30.502135v1 that the O(1) scaling methods scale better than O(log n) scaling for large enough models? I mean.

> If you use special routines (BLAS/LAPACK, ...), use them everywhere as the respective community does.

It tests with and with BLAS/LAPACK (which isn't always helpful, which of course you'd see from the benchmarks if you read them). One of the key differences of course though is that there are some pure Julia tools like https://github.com/JuliaLinearAlgebra/RecursiveFactorization... which outperform the respective OpenBLAS/MKL equivalent in many scenarios, and that's one noted factor for the performance boost (and is not trivial to wrap into the interface of the other solvers, so it's not done). There are other benchmarks showing that it's not apples to apples and is instead conservative in many cases, for example https://github.com/SciML/SciPyDiffEq.jl#measuring-overhead showing the SciPyDiffEq handling with the Julia JIT optimizations gives a lower overhead than direct SciPy+Numba, so we use the lower overhead numbers in https://docs.sciml.ai/SciMLBenchmarksOutput/stable/MultiLang....

> you must compile/write whole programs in each of the respective languages to enable full compiler/interpreter optimizations

You do realize that a .so has lower overhead to call from a JIT compiled language than from a static compiled language like C because you can optimize away some of the bindings at the runtime right? https://github.com/dyu/ffi-overhead is a measurement of that, and you see LuaJIT and Julia as faster than C and Fortran here. This shouldn't be surprising because it's pretty clear how that works?

I mean yes, someone can always ask for more benchmarks, but now we have a site that's auto updating tons and tons of ODE benchmarks with ODE systems ranging from size 2 to the thousands, with as many things as we can wrap in as many scenarios as we can wrap. And we don't even "win" all of our benchmarks because unlike for you, these benchmarks aren't for winning but for tracking development (somehow for Hacker News folks they ignore the utility part and go straight to language wars...).

If you have a concrete change you think can improve the benchmarks, then please share it at https://github.com/SciML/SciMLBenchmarks.jl. We'll be happy to make and maintain another.

If you're calling back and forth between C and Go in a performance critical way. It's one of the slowest languages for wrapping C that there is. I've personally hit this bottleneck in numerous projects, wrapping things like libutp and sqlite. See also https://github.com/dyu/ffi-overhead

Piling on about overhead (and SQLite), many high-level languages take some hit for using an FFI. So you're still incentivized to avoid tons of SQLite calls.

https://github.com/dyu/ffi-overhead

Go doesn't even have native thread stacks. When call any FFI function Go has to switch over to an on-demand stack and coordinate the goroutine and the runtime to avoid preemption and starvation. This is part of why Go's calling overhead is over 30x slower than C/C++/Rust (source). It's understandbly become Go community culture to act like FFI is just not even an option and reinvent everything in Go, but that reinvented Go suffers from these other problems plus many more (such as optimizing far worse than GCC or LLVM).

Some of the results look outdated. The Dart results look bad (25x slower than C), but looking at the code (https://github.com/dyu/ffi-overhead/tree/master/dart) it appears to be five years old. Dart has a new FFI as of Dart 2.5 (2019): https://medium.com/dartlang/announcing-dart-2-5-super-charge... I'm curious how the new FFI would fare in these benchmarks.

In that case, the libcontainer library would be faster if written in most other languages seeing as Go has unfortunate C-calling performance. In this FFI benchmark Rust is on par with C with 1193ms (total benchmarking time), while Go took 37975ms doing the same.

Hi there folks! I'm going to call the Windows API as rapidly as possible and will be doing some calculations with the results, and I thought Julia might be perfect for this task as its FFI is impressively fast, and of course, Julia is fast regarding numbers as well :).