jvm-serializers VS opentelemetry-specification

Compared with protobuf, fury is 3.2x faster. When comparing with avro, fury is 5.3x faster. Compared with flatbuffers, fury is 4.8x faster. See https://github.com/eishay/jvm-serializers/wiki for detailed benchmark data

First, there's benchmarks here if you haven't seen it: jvm-serializers. Not terribly scientific, but it's something. To make any decision, you really need to benchmark your own object graph and it's important to configure the serializer for your particular usage. Still, it is sort of useful for comparing frameworks. It would be interesting to see how Loial performs there. Ping me if you add it.

It depends. Some binary encodings such as flatbuffer are actually slower than some JSON libraries. There's a wide range of performance even in the JSON libraries themselves. Generally the faster JSON libraries are the ones that work on a predefined schema and so are able to generate code specifically for that JSON.

> I'm surprised this is up for debate.

I looked into logging in protobuf when I was seeing if there was a better binary encoding for ring-buffer logging, along the same lines as nanolog:

https://tersesystems.com/blog/2020/11/26/queryable-logging-w...

What I found was that it's typically not the binary encoding vs string encoding that makes a difference. The biggest factors are "is there a predefined schema", "is there a precompiler that will generate code for this schema", and "what is the complexity of the output format". With that in mind, if you are dealing with chaotic semi-structured data, JSON is pretty good, and actually faster than some binary encodings:

https://github.com/eishay/jvm-serializers/wiki/Newer-Results...

You could use any of the JVM serialisers which should still work.

It means that the OpenTelemetry project provides not only a specification to define the contract between the applications, collectors, and telemetry databases, but also a set of APIs, SDKs, and tools like instrumentation libraries (for different languages), collectors, operators, etc. OpenTelemetry is open-source and vendor-agnostic, so the project is not tied to any specific vendor or cloud provider.

Sure, happy to provide more specifics!

Our main issue was the lack of a synchronous gauge. The officially supported asynchronous API of registering a callback function to report a gauge metric is very different from how we were doing things before, and would have required lots of refactoring of our code. Instead, we wrote a wrapper that exposes a synchronous-like API: https://gist.github.com/yolken-airplane/027867b753840f7d15d6....

It seems like this is a common feature request across many of the SDKs, and it's in the process of being fixed in some of them (https://github.com/open-telemetry/opentelemetry-specificatio...)? I'm not sure what the plans are for the golang SDK specifically.

Another, more minor issue, is the lack of support for "constant" attributes that are applied to all metrics. We use these to identify the app, among other use cases, so we added wrappers around the various "Add", "Record", "Observe", etc. calls that automatically add these. (It's totally possible that this is supported and I missed it, in which case please let me know!).

Overall, the SDK was generally well-written and well-documented, we just needed some extra work to make the interfaces more similar to the ones were were using before.

OpenTelemetry is an open-source collection of tools, APIs, and SDKs that aims to standardize the way we generate and collect telemetry data. It follows a specification-driven development. The OpenTelemetry specification  has design and implementation guidelines for how the instrumentation libraries should be implemented. In addition, it provides client libraries in all the major programming languages that follow the specification.

Two problems with OpenTelemetry:

1. It doesn't know what the hell it is. Is it a semantic standard? Is a protocol? It is a facade? What layer of abstraction does it provide? Answer: All of the above! All the things! All the layers!

2. No one from OpenTelemetry has actually tried instrumenting a library. And if they have, they haven't the first suggestion on how instrumenters should actually use metrics, traces, and logs. Do you write to all three? To one? I asked this question two years ago, not a single response. [1]

[1] https://github.com/open-telemetry/opentelemetry-specificatio...

OpenTelemetry Specification GitHub

One update to this: we proposed replacing the count suffix in OpenTelemetry with total to match Prometheus/OpenMetrics. That discussion resulted in the count suffix being removed from the OpenTelemetry semantic conventions. We'll soon update our metric from being called function.calls.count to just function.calls and the generated Prometheus queries will refer to function_calls_total. That resolves one of the main conflicts between the two specs.

This is your best bet if you want to track status updates: https://github.com/open-telemetry/opentelemetry-specification/issues/2911

OpenTelemetry is a standard for implementing telemetry in your applications. It provides a specification, containing the requirements that all implementations should follow as well as some implementations for major languages, including an API and a SDK to interact with it.

At this point, we are about to do the real work where we have to configure OpenTelemetry and export telemetry data to New Relic. Exporting this kind of data relies on a specific protocol; the OpenTelemetry Protocol or OTLP.

OpenTelemetry provides instrumentation libraries for your application. The development of these libraries is guided by the OpenTelemetry specification. The OpenTelemetry specification describes the cross-language requirements and design expectations for all OpenTelemetry implementations in various programming languages.