oteps VS FlatBuffers

Oh nice, thank you (and also solumos) for the links! It looks like oteps/pull/171 (merged June 2023) expanded and superseded the opentelemetry-proto/pull/346 PR (closed Jul 2022) [0]. The former resulted in merging OpenTelemetry Enhancement Proposal 156 [1], with some interesting results especially for 'Phase 2' where they implemented columnar storage end-to-end (see the Validation section [2]):

* For univariate time series, OTel Arrow is 2 to 2.5 better in terms of bandwidth reduction ... and the end-to-end speed is 3.1 to 11.2 times faster

* For multivariate time series, OTel Arrow is 3 to 7 times better in terms of bandwidth reduction ... Phase 2 has [not yet] been .. estimated but similar results are expected.

* For logs, OTel Arrow is 1.6 to 2 times better in terms of bandwidth reduction ... and the end-to-end speed is 2.3 to 4.86 times faster

* For traces, OTel Arrow is 1.7 to 2.8 times better in terms of bandwidth reduction ... and the end-to-end speed is 3.37 to 6.16 times faster

[0]: https://github.com/open-telemetry/opentelemetry-proto/pull/3...

[1]: https://github.com/open-telemetry/oteps/blob/main/text/0156-...

[2]: https://github.com/open-telemetry/oteps/blob/main/text/0156-...

https://github.com/open-telemetry/oteps/issues/139

It takes a lot of time and effort to bake a cross-vendor cross-language standard.

This. The statelessness of the OTLP is by design. I did consider stateful designs with e.g. shared state dictionary compression but eventually chose not to, so that the intermediaries can remain stateless.

An extension to OTLP that uses shared state (and columnar encoding) to achieve more compact representation and is suitable for the last network leg in the data delivery path has been proposed and may become a reality in the future: https://github.com/open-telemetry/oteps/pull/171

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.