grpc-go VS kubernetes

The reflection service is open-sourced (at least for some sdks):

* https://github.com/grpc/grpc-go/blob/master/Documentation/se...

* https://chromium.googlesource.com/external/github.com/grpc/g...

We’re hoping to make this rate at least optional via this pull request but as the time of writing this blog, it’s nothing we can do to circle our way around it.

First, I started with gRPC’s recommended starter repository for learning gRPC, their **helloworld **example, which is a part of the official gRPC repository.

IDE: use whatever make you productive. I personally use vscode. VCS: git, as golang communities use github heavily as base for many libraries. AFAIK Linter: use staticcheck for linting as it looks like mostly used linting tool in go, supported by many also. In Vscode it will be recommended once you install go plugin. Libraries/Framework: actually the standard libraries already included many things you need, decent enough for your day-to-day development cycles(e.g. `net/http`). But here are things for extra: - Struct fields validator: validator - Http server lib: chi router , httprouter , fasthttp (for non standard http implementations, but fast) - Web Framework: echo , gin , fiber , beego , etc - Http client lib: most already covered by stdlib(net/http), so you rarely need extra lib for this, but if you really need some are: resty - CLI: cobra - Config: godotenv , viper - DB Drivers: sqlx , postgre , sqlite , mysql - nosql: redis , mongodb , elasticsearch - ORM: gorm , entgo , sqlc(codegen) - JS Transpiler: gopherjs - GUI: fyne - grpc: grpc - logging: zerolog - test: testify , gomock , dockertest - and many others you can find here

If you read the first comment, you’ll see the API was documented as being experimental.

https://github.com/grpc/grpc-go/issues/3798#issuecomment-670...

The lack of this analysis still results in bugs and CVEs. See how many races are found and fixed in gRPC releases: https://github.com/grpc/grpc-go/releases (search "race"). It's a shame Google does not publish these as CVEs, because many of them qualify.

I like to point people at this release to show that not even Google -- in its own language on its own library for its own RPC protocol -- can write thread-safe Go, so what chance does anyone else have. Maybe we have to stop thinking of Go as a language for mission critical parallel computing and think of it more like a Python 4 made for low-risk prototyping. Mature libraries help for that prototyping, you know how to put them together and get something working, that something just won't be scaleable, efficient, or thread-safe.

I first learned about this technique when gRPC generated code started using it. See the short readme and the long issue discussion. I think a lot more of the rationale from the discussion should have made it into the readme, since this is the only time most Go developers will ever see this technique used, especially since it can't be retrofitted to existing interfaces without breaking existing implementations.

I don't have any experience with goRPC (I'm assuming you're referring to https://github.com/valyala/gorpc), but just to note that that repo hasn't been updated in 7 years and has open issues that are that old, too. https://github.com/grpc/grpc-go has 17.5k stars and is actively maintained. That doesn't say anything about their relative performance - goRPC might be faster - but you probably won't have a fun time if you run into issues.

Found the root cause from https://github.com/grpc/grpc-go/commit/383b1143 (original issue: https://github.com/grpc/grpc-go/issues/75):

    // Note that ServeHTTP uses Go's HTTP/2 server implementation which is

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

If you do kubectl explain deployment than (surprise!) you'll get a description for extensions/v1beta1. Because kubectl explain works the same way, just like kubectl get:

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In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.