ghz VS FlatBuffers

I once built a quick and dirty load testing tool for a public facing service we built. The tool was pretty simple - something like https://github.com/bojand/ghz but with traffic and data patterns closer to what we expected to see in the real world. We used argo-workflows to generate scale.

One thing which we noticed was that there was a considerable difference in performance characteristics based on how we parallelized the load testing tool (multiple threads, multiple processes, multiple kubernetes pods, pods forced to be distributed across nodes).

I think that when you run non-distrubuted load tests you benefit from bunch of cool things which happen with http2 and Linux (multiplexing, resource sharing etc) which might make applications seem much faster than they would be in the real world.

I'm not sure. Maybe you can write to the discussion section of the repo https://github.com/bojand/ghz/discussions

In part 1 we looked at ghz for load testing gRPC services, and now I want to cover k6, which claims to be a modern load testing tool built for developer happiness. After only a brief experience with it I can see why is that and why Grafana moved to acquire k6 earlier this year.

It should be unbound, in this particular benchmark we set ghz concurrency to 50 and connections to 5 and we don't set the rps flag of ghz (e.g. --rps=2000, from this tool)

a second container running ghz makes unary requests to the server

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Cap'n Proto was originally made for https://sandstorm.io/.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.