kubebuilder VS TheAlgorithms

The Spin operator uses the Kubebuilder framework and contains a Spin App Custom Resource Definition (CRD) and controller. It watches Spin App Custom Resources and realizes the desired state in the K8s cluster. Aside from the immediate benefits gained by running Wasm workloads in k8s, additional optimizations such as Horizontal Pod Scaling (HPA) and k8s Event-driven Autoscaling (KEDA) can be achieved in a pinch.

kubebuilder: brew install kubebuilder

The operator that I've been working on is designed to manage the full lifecycle of a QuestDB database instance, including version and hardware upgrades, config changes, backups, and (eventually) recovery from node failure. I used the Operator SDK and kubebuilder frameworks to provide scaffolding and API support.

Recently, I've been spending a lot of time writing a Kubernetes operator using the go operator-sdk, which is built on top of the Kubebuilder framework. This is a list of a few tips and tricks that I've compiled over the past few months working with these frameworks.

Since we built our operator using the Kubebuilder framework, most standard monitoring tasks were handled for us out-of-the-box. Our operator automatically exposes a rich set of Prometheus metrics that measure reconciliation performance, the number of k8s API calls, workqueue statistics, and memory-related metrics. We we were able to ingest these metrics into pre-built dashboards by leveraging the grafana/v1-alpha plugin, which scaffolds two Grafana dashboards to monitor Operator resource usage and performance. All we had to do was add these to our existing Grafana manifests and we were good to go!

I wrote a CSI driver and some operators. I admire K8s, because you can find solution to almost any problem in the source code - API versioning, load balancing, request throttling, optimistic concurrency, security, and much much more. I recommend https://book.kubebuilder.io/ It is similar to Operator SDK, but without Openshift-specific stuff. It gradually introduces you to many k8s concepts, and follows design patterns that k8s uses internally.

Do you mean this: https://book.kubebuilder.io/ ?

https://book.kubebuilder.io/ all you need to know

A better way to write an operator these days is to use kubebuilder [1].

My complaint is that I have seen orgs write operators for random stuff, often reinventing the wheel. Lot of operators in orgs are result of resume driven development. Having said that it often comes handy for complex orchestration.

[1]https://github.com/kubernetes-sigs/kubebuilder

Is it me or does it not seem very well thought out? Every example I've seen only has implementations in JavaScript and/or Python. I haven't seen any other languages nor a way to search by language. What a "string" means in one language can be completely different in another language. The primitive data types that the project assumes are not really supported across all programming languages.

Also if anyone hasn't already seen them, similar projects already exist and are more complete. E.g.

* https://rosettacode.org/

* https://programming-idioms.org/

* https://the-algorithms.com/

Not to mention LeetCode, CodeWars, Project Euler, Exercism can kinda serve the same role.

Hey Everyone, My name is Rachit Chawla and Its my first blog on dev.to. I am currently a student of Computer Programming and Analysis at Seneca College. Also I'm currently on my co-op term working as an Automation Developer at Ontario Public Service. In this role, I am currently working with PowerShell scripting and Microsoft Azure for automating every manual tasks to reduce workload and increase efficiency. This blog is a part of OSD600 course at Seneca College. I am taking this course as I am big fan of open source and always wanted to contribute in open source projects but I am unaware of proper documentation and standards used for open source contributions. I am hoping to learn all the required stuff by the end of this course and I aim to be one of the 15k contributors to Linux's repo by Linus Torvald. Open Source interests me because it gives developers the power to customise the application they want to use, also a chance to help others and improve their skills. I found https://github.com/TheAlgorithms/Python interesting from the Monthly trending feed on Github as it has all the algorithms which help us improve time complexity and write better codes. I has about 1000 contributors which helped to code all the algorithms in Python which may help others for working or learning purposes. I myself was a student of Data Structures and Algorithms in Python Winter 2023 and hoping to even able to contribute to this repo itself, once I learn more about documentation & proper standards to be followed.

The Algorithms Contains many open source implementations of algorithms. Check it out.

If you want to see implementations of all possible traversal algorithms you can find it here.

The mother load of all algorithms in python is here. dfs/bfs in particular are in the graph section.

There is no one way to learn all these but here are some resources: 1. Gooking algorithms [https://edu.anarcho-copy.org/Algorithm/grokking-algorithms-illustrated-programmers-curious.pdf\] 2. Algorithms in all languages [https://the-algorithms.com/] 3. Node js best practices. [https://github.com/goldbergyoni/nodebestpractices] 4. Refactoring [https://refactoring.guru/] 5. Learn about Clean Code and Clean Architecture from uncle bob. https://www.youtube.com/watch?v=NeXQEJNWO5w&ab_channel=StreamAConStreamingConferences

DSA basics

I would recommend The Algorithms, it comes with descriptions and examples in multiple programming languages.

There's also The Algorithms. Many implementations are unfortunately low quality. The Lua ones (disclaimer: I wrote them) should be fine however.

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