grpc_bench VS µPickle

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.

The upickle library has traditionally had great performance for handling json in Scala apps so is likely to be seen as a safe choice for someone starting a Scala project. It appears though that not just upickle, but other json library projects are having difficulties maintaining their old level of performance when they release using Scala 3's macros. uPickle currently has an open issue where you can see some of these issues: https://github.com/com-lihaoyi/upickle/issues/389 and here you can see the weePickle folks are also having the same performance problems. Looks like things changed up significantly enough between Scala 2 and Scala 3 so that in order to maintain the same functionality they have resorted to using runtime reflection for mapping to/from case classes.

See here for sample code size numbers (not picking on upickle specifically, it's just what I'm using myself. I've heard similar reports about e.g. circe)

A good starting point is the note in MacroImplicits.scala in upickle sources. "derives Writer" for a specific case class Foo simply adds a given Writer[Foo] to a companion object of the same specific class. However, this cannot be done automatically for a trait defining sum type - in this case trait Thing. The required given must be defined manually, and the ones automatically obtained for case classes can be used in it.