warrant VS go

Warrant — Hosted enterprise-grade authorization and access control service for your apps. The free tier includes 1 million monthly API requests and 1,000 authz rules.

The specific challenge with authz in the app layer is that different apps can have different access models with varying complexity, especially the more granular you get (e.g. implementing fine grained access to specific objects/resources - like Google Docs).

Personally, I think a rebac (relationship/graph based) approach works best for apps because permissions in applications are mostly relational and/or hierarchical (levels of groups). There are authz systems out there such as Warrant https://warrant.dev/ (I'm a founder) in which you can define a custom access model as a schema and enforce it in your app.

Let's use warrant.dev as an example. The system provides a set of REST APIs for you to define object types and access policies (called warrants). The general process is first to create object types using HTTP POST:

https://warrant.dev/ (Provider) Relatively new authZ provider, they have a dashboard where you can manage your rules in a central location and then use them from multiple languages via their SDKs, even on the client to perform UI checks. Rules can also be managed programmatically via SDK.

I would describe this debate more as Policy-as-Data (Zanzibar) vs Policy-as-Code (OPA et al).

In Zanzibar, all of the information required to make an authorization decision (namespaces, relationship tuples, etc.) is stored in Zanzibar, and the decision engine resolves access checks based on this data. This data can be scaled horizontally (and consistently) as needed for an application’s needs. This makes Zanzibar a centralized, unified solution for all of an application’s authorization needs. I’ve found this approach more purpose built / well suited for application authorization.

With OPA and other policy engines, the data required for performing access checks lives somewhere else (maybe the application’s database) and must be separately queried and included as part of the authorization check because OPA et al. are stateless decision engines. This makes it such that you need to piece together data from different sources in order to get your final decision, which IMO is something most developers don’t want to deal with.

On the flip side, Zanzibar’s “namespaces” are a very simple policy layer not well suited to querying against data outside of Zanzibar’s scope (e.g. geolocation, time, etc). For scenarios like this, a full fledged policy-as-code solution is great. However, it should be noted that some open source Zanzibar implementations like Warrant[1] and SpiceDB[2] (mentioned in the article) also offer a policy-as-code layer on top of Zanzibar’s graph-based/ReBAC approach to tackle these scenarios.

Disclaimer, I’m one of the founders of Warrant.

[1] https://github.com/warrant-dev/warrant

[2] https://github.com/authzed/spicedb

Hey HN, I recently shared my thoughts on why Google Zanzibar is a great solution for implementing authorization[1] and why we decided to build Warrant’s core authz service using key concepts from the Zanzibar paper. As I mentioned in the post, we recently open sourced the authz service powering our managed cloud service, Warrant Cloud[2], so I thought I’d share it with everyone here. Cheers!

[1] https://news.ycombinator.com/item?id=36470943

[2] https://warrant.dev/

More than two years after choosing to build Warrant atop Zanzibar’s core principles, we’re extremely happy with our decision. Doing so gave us a solid technical foundation on which to tackle the various complex authorization challenges companies face today. As we continue to encounter new scenarios and use cases, we’ll keep iterating on Warrant to ensure it’s the most capable authorization service. To share what we learn and what we build with the developer community, we recently open-sourced the core authorization engine that powers our fully managed authorization platform, Warrant Cloud. If you’re interested in authorization (or Zanzibar), check it out and give it a star!

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/.