grpc_bench VS FlatBuffers

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.

I don't work at Cloudflare but follow their work and occasionally work on performance sensitive projects.

If I had to guess, they looked at the landscape a bit like I do and regarded Cap'n Proto, flatbuffers, SBE, etc. as being in one category apart from other data formats like Avro, protobuf, and the like.

So once you're committed to record'ish shaped (rather than columnar like Parquet) data that has an upfront parse time of zero (nominally, there could be marshalling if you transmogrify the field values on read), the list gets pretty short.

https://capnproto.org/news/2014-06-17-capnproto-flatbuffers-... goes into some of the trade-offs here.

Cap'n Proto was originally made for https://sandstorm.io/. That work (which Kenton has presumably done at Cloudflare since he's been employed there) eventually turned into Cloudflare workers.

Another consideration: https://github.com/google/flatbuffers/issues/2#issuecomment-...

Much more in the discussion of this particular issue onGitHub: flatbuffers:Reverse engineering #4258

Related but not Rust-specific: FlatBuffers, Cap'n Proto.

If json files are a concern because of space, you can always look into something like protobuffers or flatbuffers. But whatever you use, you should try to find a solution where you don't have to think about the actual serialization/deserialization of your objects, and can just concentrate on the data.

Or, as an alternative to protobuffers, there's also flatbuffers, which is lighter weight and needs less memory: https://google.github.io/flatbuffers/

As the title suggests, I am considering using Flatbuffers as a way to parse sensor data that has been stored in local datafiles. The project language is python.