rke2 VS go

In this essay, we showed how to use Rancher rke2 to deploy a Kubernetes cluster with 6 Debian nodes with firewall enabled. We've also covered deploying Cilium as a CNI for our cluster and have it completely replace kube-proxy so as to increase speed and gain more observability via Cilium tools. This article also showed how to deploy Metallb to manage IP pools and load balance traffic for those IP pools. Throughout this guide, we assumed that we have an external load balancer that will distribute traffic to our workload and control plane nodes. For further information please visit rke2 official documents: "https://docs.rke2.io/".

In this blog post, we will demonstrate how easy and fast it is to deploy Sveltos on an RKE2 cluster with the help of ArgoCD, register two RKE2 Cluster API (CAPI) clusters and create a ClusterProfile to deploy Prometheus and Grafana Helm charts down the managed CAPI clusters.

Did something happen to the Apache 2 rancher? https://github.com/rancher/rancher/blob/v2.7.5/LICENSE RKE2 is similarly Apache 2: https://github.com/rancher/rke2/blob/v1.26.7%2Brke2r1/LICENS...

To provision all of my clusters, I use Rancher with RKE2. The primary Rancher server is hosted on a bootstrapped RKE2 cluster running on a VPS.

Golang has burned me more than once with bizarre design decisions that break things in a user hostile way.

The last one we ran into was a change in Go 1.15 where servers that presented a TLS certificate with the hostname encoded into the CN field instead of the more appropriate SAN field always fail validation.

The behavior could be disabled however that functionality was removed in 1.18 with no way to opt back into the old behavior. I understand why SAN is the right way to do it but in this case I didn’t control the server.

Developers at Google probably never have to deal with 3rd parties with shitty infrastructure but a lot of us do.

Here’s a bug in rke that’s related https://github.com/rancher/rke2/issues/775

just looking at this myself. I think k3s has more support for arm, but looking through the github repo there are a lot of bugs indicating its a mess. RKE2 seems to be their big push, they also have a github issue open that has been open for the last 2 releases that they are going to add a update path from k3s to rke2. https://github.com/rancher/rke2/issues/881

RKE (https://rancher.com/docs/rke) and RKE2 (https://docs.rke2.io/) from Rancher folks

The description indicates it is not production ready, and is archived at the same time.

If you pull all stops in each respective language, C# will always end up winning at parsing text as it offers C structs, pointers, zero-cost interop, Rust-style struct generics, cross-platform SIMD API and simply has better compiler. You can win back some performance in Go by writing hot parts in Go's ASM dialect at much greater effort for a specific platform.

For example, Go has to resort to this https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f... in order to efficiently scan memory, while in C# you write the following once and it compiles to all supported ISAs with their respective SIMD instructions for a given vector width: https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (there is a lot of code because C# covers much wider range of scenarios and does not accept sacrificing performance in odd lengths and edge cases, which Go does).

Another example is computing CRC32: you have to write ASM for Go https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f..., in C# you simply write standard vectorized routine once https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (its codegen is competitive with hand-intrinsified C++ code).

There is a lot more of this. Performance and low-level primitives to achieve it have been an area of focus of .NET for a long time, so it is disheartening to see one tenth of effort in Go to receive so much spotlight.

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/.