transit-lang-cmp VS grpc_bench

> The axum::debug_handler macro is invaluable to debug type errors (there's some with axum too), like for example, accidentally having a non-Send type slip in.

Heh, yeah. For my recent project where I explored implementing the same little app in a few different languages[0], I chose Axum for the rust version.

The whole "extractor" system was pretty magical, and when I had this exact issue (non-Send argument), the compiler error was totally useless. I did see the docs about adding this extra macro crate for error messages but it seemed like a bit of a red flag that the framework was going against the grain of the language. Still, on the whole, I did enjoy working with Axum.

[0] https://github.com/losvedir/transit-lang-cmp

This is great! Just pushed up a commit that uses it and updated the benchmarks[0]. I'm seeing a 1.6X - 2X improvement in overall performance. Not bad for a drop-in replacement. And since it's based on serde, I trust it, and I feel like trying out a different JSON library is within scope for me of not just "gaming the benchmarks", as this is actually something I'd now consider using at work.

It's not quite as high as I was seeing with `jiffy` (3,800 req/sec here vs 4,000+ with jiffy), but I'm not confident that was a totally fair comparison. `jiffy` doesn't integrate as nicely with Phoenix, so I was just calling `:jiffy.encode(...)` in the controller and then doing a `text(...)` response. I need to double-check if `json(...)` is doing more work here.

[0] https://github.com/losvedir/transit-lang-cmp/commit/140d693b...

Nowadays, I reached out for some benchmark results. Scala is slower than Java and Kotlin. Can you explain it? https://github.com/losvedir/transit-lang-cmp https://github.com/kostya/benchmarks

I had a chance to update the Go code (commit) to pre-allocate the arrays based on the known length before all the appends, and saw ~30% increase in performance, with top requests per second going from about 8,600 to 11,000.

I recently did a little project to compare several languages (https://github.com/losvedir/transit-lang-cmp) so I contributed to a bunch of those points!

SayHello, GetUser, and Sum differ only by payload size. Sum is the simplest one - (int, int) -> int, GetUser is (long) -> User (medium payload), and SayHello uses exactly the same payload as this test: https://github.com/LesnyRumcajs/grpc_bench/tree/master/dotnet_grpc_bench

This is correct. The problem is not with the benchmark itself but with the implementation. If you look at the result, you can see that even with 6 "allowed" CPUs, the vertx server utilizes less than 100%. Apparently, the current vertx implementation (the one implemented in https://github.com/LesnyRumcajs/grpc_bench/tree/master/java_vertx_grpc_bench) is single-threaded or has some other limitation.

Another iteration of grpc_bench!

Because it's fast and runs on Windows, Linux, and MacOS

In general though, akka/pekko-streams are known to be one of the fastest implementations out there. Their grpc client for example even beats languages like Rust (see https://www.lightbend.com/blog/akka-grpc-update-delivers-1200-percent-performance-improvement and https://github.com/LesnyRumcajs/grpc_bench/wiki/2022-03-15-bench-results).

The whole point I was making is at least up until 8 months ago (at best, I can't commend on the stability/maturity/performance of shardcake) Akka was the only mature library/ecosystem solving this problem with also a very strong focus on performance (for example still to this day, akka/pekko-grpc is generally one of the fastest grpc implementations I am aware of, its even beating rust if you have at least 2 cores (see https://github.com/LesnyRumcajs/grpc_bench/wiki/2022-03-15-bench-results)

It would be interesting to create a new java benchmark with your implementation.

Also worth checking out the gRPC benchmarks: https://github.com/LesnyRumcajs/grpc_bench/discussions/284

dotnet is up there with Rust.