opentelemetry-specificatio
jvm-serializers
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opentelemetry-specificatio
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Migrating to OpenTelemetry
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.
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OpenTelemetry in 2023
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...
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Go standard library: structured, leveled logging
That's why you have otel logging: https://github.com/open-telemetry/opentelemetry-specificatio...
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Monarch: Google’s Planet-Scale In-Memory Time Series Database
There are a large amount of subtle tradeoffs around the bucketing scheme (log, vs. log-linear, base) and memory layout (sparse, dense, chunked) the amount of configurability in the histogram space (circllhist, DDSketch, HDRHistogram, ...). A good overview is this discussion here:
https://github.com/open-telemetry/opentelemetry-specificatio...
As for the circllhist: There are no knobs to turn. It uses base 10 and two decimal digits of precision. In the last 8 years I have not seen a single use-case in the operational domain where this was not appropriate.
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OpenTelemetry
A good place to look at is the milestones on GitHub: https://github.com/open-telemetry/opentelemetry-specificatio...
Logging is still experimental in the spec. Metrics API is feature freeze and the protocol is stable, so it's more on language SDKs to stabilize their implementations. This is a focus for several of them right now.
jvm-serializers
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Fury: 170x faster than JDK, fast serialization powered by JIT and Zero-copy
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
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The state of Java Object Serialization libraries in Q2 2023
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.
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Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19+
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.
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Go standard library: structured, leveled logging
> 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...
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Scala 3.0 serialization
You could use any of the JVM serialisers which should still work.
What are some alternatives?
skywalking - APM, Application Performance Monitoring System
fury-benchmarks - Serialization Benchmarks for fury with other libraries
SLF4J - Simple Logging Facade for Java
Apache Avro - Apache Avro is a data serialization system.
opentelemetry-specification - Specifications for OpenTelemetry
zio-json - Fast, secure JSON library with tight ZIO integration.
semantic-conventions - Defines standards for generating consistent, accessible telemetry across a variety of domains
janino - Janino is a super-small, super-fast Java™ compiler.
zipkin-api - Zipkin's language independent model and HTTP Api Definitions
grpc-dotnet - gRPC for .NET
terraform-aws-jaeger - Terraform module for Jeager
honeycomb-opentelemetry-go - Honeycomb's OpenTelemetry Go SDK distribution