VictoriaMetrics VS go

VictoriaMetrics CTO here.

The referred library is the official OpenTelemetry package for reading metrics in Go language [1] - more details are available at [2].

Note that we at VictoriaMetrics like the idea of unified observability standard like OpenTelemetry. The issue is in the current otel implementation. It is too bloated and very inefficient. This contradicts to our experience with observability cases, which need very optimized format for metrics' transfer in order to reduce costs on CPU and network traffic needed to transfer and process these metrics.

VictoriaMetrics continues investing in OpenTelemetry by providing integration docs [3] and improving the existing functionality for otel metrics' ingestion [4].

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

[2] https://github.com/VictoriaMetrics/VictoriaMetrics/pull/2570...

[3] https://docs.victoriametrics.com/guides/getting-started-with...

[4] https://github.com/VictoriaMetrics/VictoriaMetrics/issues/60...

Victoria Metrics

VictoriaMetrics is primarily a time-series database designed for efficiently storing and querying time-series data. It is often used as a back-end data store for time-series data generated by monitoring systems like Prometheus. VictoriaMetrics excels at handling large volumes of time-series data, offering efficient storage and query capabilities.

Not sure I follow since there are very competitive tools written in Go such as https://victoriametrics.com for an example in this space.

Did you try VictoriaMetrics [1] and vmagent [2]? It is a single self-contained binary without external dependencies. It requires relatively low amounts of CPU, RAM, disk space and disk IO, and it runs on ARM.

[1] https://github.com/VictoriaMetrics/VictoriaMetrics/

[2] https://docs.victoriametrics.com/vmagent.html

https://github.com/VictoriaMetrics/VictoriaMetrics#cardinali...

If I understanding correctly, it deal with high cardinality by dropping data, the operators need to monitor for this and adjust their data to lower the cardinality.

VictoriaMetrics

You shouldn't unless you want to use the new open source standard for telemetry. You won't benefit from simplicity or performance improvements. It would be quite the opposite. You can check what is the actual cost of open telemetry adoption here [0]

But if you ever decide to go this path - VictoriaMetrics supports OpenTelemetry protocol for metrics [1]

[0] https://github.com/VictoriaMetrics/VictoriaMetrics/pull/2570

[1] https://docs.victoriametrics.com/Single-server-VictoriaMetri...

A Discussion about including this package in Go as encoding/json/v2 has been started on the Go Github project on 2023-10-05. Please provide your feedback there.

I like the Principles section. Very measured and practical approach to releasing new stdlib packages. https://go.dev/blog/randv2#principles

The end of the post they mention that an encoding/json/v2 package is in the works: https://github.com/golang/go/discussions/63397

There used to be the GO FIPS branch :

https://github.com/golang/go/tree/dev.boringcrypto/misc/bori...

But it looks dead.

And it looks like https://github.com/golang-fips/go as well.

I'm not sure what exactly you mean by acknowledgement, but here are some counterexamples:

- A proposal for sum types by a Go team member: https://github.com/golang/go/issues/57644

- The community proposal with some comments from the Go team: https://github.com/golang/go/issues/19412

Here are some excerpts from the latest Go survey [1]:

- "The top responses in the closed-form were learning how to write Go effectively (15%) and the verbosity of error handling (13%)."

- "The most common response mentioned Go’s type system, and often asked specifically for enums, option types, or sum types in Go."

I think the problem is not the lack of will on the part of the Go team, but rather that these issues are not easy to fix in a way that fits the language and doesn't cause too many issues with backwards compatibility.

[1]: https://go.dev/blog/survey2024-h1-results

Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.

Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!

net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements

Make sure you have Go installed https://go.dev/.