operator for s3gw
an invention by gbaccini
Updated almost 3 years ago. No love. 2 followers.

Project Description

Create a K8s CRD for s3gw.
The operator will be written in Go.
The CRD should in the beginning allow an user to create a bucket.

Goal for this Hackweek

The CRD should in the beginning allow an user to create a bucket.

Project

https://github.com/giubacc/s3gw-operator

Looking for hackers with the skills:

golang kubernetes operator s3gw

This project is part of:

Hack Week 22

Activity

  • almost 3 years ago: gbaccini joined this project.
  • almost 3 years ago: gbaccini added keyword "golang" to this project.
  • almost 3 years ago: gbaccini added keyword "kubernetes" to this project.
  • almost 3 years ago: gbaccini added keyword "operator" to this project.
  • almost 3 years ago: gbaccini added keyword "s3gw" to this project.
  • almost 3 years ago: tdehler started this project.
  • almost 3 years ago: gbaccini originated this project.


    • Comments

    Be the first to comment!

    Similar Projects

    Create a go module to wrap happy-compta.fr by cbosdonnat

    Description

    https://happy-compta.fr is a tool for french work councils simple book keeping. While it does the job, it has no API to work with and it is tedious to enter loads of operations.

    Goals

    Write a go client module to be used as an API to programmatically manipulate the tool.

    Writing an example tool to load data from a CSV file would be good too.

    Updatecli Autodiscovery supporting WASM plugins by olblak

    Description

    Updatecli is a Golang Update policy engine that allow to write Update policies in YAML manifest. Updatecli already has a plugin ecosystem for common update strategies such as automating Dockerfile or Kubernetes manifest from Git repositories.

    This is what we call autodiscovery where Updatecli generate manifest and apply them dynamically based on some context.

    Obviously, the Updatecli project doesn't accept plugins specific to an organization.

    I saw project using different languages such as python, C#, or JS to generate those manifest.

    It would be great to be able to share and reuse those specific plugins

    During the HackWeek, I'll hang on the Updatecli matrix channel

    https://matrix.to/#/#Updatecli_community:gitter.im

    Goals

    Implement autodiscovery plugins using WASM. I am planning to experiment with https://github.com/extism/extism

    To build a simple WASM autodiscovery plugin and run it from Updatecli

    Resources

    • https://github.com/extism/extism
    • https://github.com/updatecli/updatecli
    • https://www.updatecli.io/docs/core/autodiscovery/
    • https://matrix.to/#/#Updatecli_community:gitter.im

    go-git: unlocking SHA256-based repository cloning ahead of git v3 by pgomes

    Description

    The go-git library implements the git internals in pure Go, so that any Go application can handle not only Git repositories, but also lower-level primitives (e.g. packfiles, idxfiles, etc) without needing to shell out to the git binary.

    The focus for this Hackweek is to fast track key improvements for the project ahead of the upstream release of Git V3, which may take place at some point next year.

    Goals

    Stretch goals

    Resources

    • https://github.com/go-git/go-git/
    • https://go-git.github.io/docs/

    terraform-provider-feilong by e_bischoff

    Project Description

    People need to test operating systems and applications on s390 platform. While this is straightforward with KVM, this is very difficult with z/VM.

    IBM Cloud Infrastructure Center (ICIC) harnesses the Feilong API, but you can use Feilong without installing ICIC(see this schema).

    What about writing a terraform Feilong provider, just like we have the terraform libvirt provider? That would allow to transparently call Feilong from your main.tf files to deploy and destroy resources on your z/VM system.

    Goal for Hackweek 23

    I would like to be able to easily deploy and provision VMs automatically on a z/VM system, in a way that people might enjoy even outside of SUSE.

    My technical preference is to write a terraform provider plugin, as it is the approach that involves the least software components for our deployments, while remaining clean, and compatible with our existing development infrastructure.

    Goals for Hackweek 24

    Feilong provider works and is used internally by SUSE Manager team. Let's push it forward!

    Let's add support for fiberchannel disks and multipath.

    Goals for Hackweek 25

    Modernization, maturity, and maintenance: support for SLES 16 and openTofu, new API calls, fixes...

    Resources

    Outcome

    SUSE Health Check Tools by roseswe

    SUSE HC Tools Overview

    A collection of tools written in Bash or Go 1.24++ to make life easier with handling of a bunch of tar.xz balls created by supportconfig.

    Background: For SUSE HC we receive a bunch of supportconfig tar balls to check them for misconfiguration, areas for improvement or future changes.

    Main focus on these HC are High Availability (pacemaker), SLES itself and SAP workloads, esp. around the SUSE best practices.

    Goals

    • Overall improvement of the tools
    • Adding new collectors
    • Add support for SLES16

    Resources

    csv2xls* example.sh go.mod listprodids.txt sumtext* trails.go README.md csv2xls.go exceltest.go go.sum m.sh* sumtext.go vercheck.py* config.ini csvfiles/ getrpm* listprodids* rpmdate.sh* sumxls* verdriver* credtest.go example.py getrpm.go listprodids.go sccfixer.sh* sumxls.go verdriver.go

    docollall.sh* extracthtml.go gethostnamectl* go.sum numastat.go cpuvul* extractcluster.go firmwarebug* gethostnamectl.go m.sh* numastattest.go cpuvul.go extracthtml* firmwarebug.go go.mod numastat* xtr_cib.sh*

    $ getrpm -r pacemaker >> Product ID: 2795 (SUSE Linux Enterprise Server for SAP Applications 15 SP7 x86_64), RPM Name: +--------------+----------------------------+--------+--------------+--------------------+ | Package Name | Version | Arch | Release | Repository | +--------------+----------------------------+--------+--------------+--------------------+ | pacemaker | 2.1.10+20250718.fdf796ebc8 | x86_64 | 150700.3.3.1 | sle-ha/15.7/x86_64 | | pacemaker | 2.1.9+20250410.471584e6a2 | x86_64 | 150700.1.9 | sle-ha/15.7/x86_64 | +--------------+----------------------------+--------+--------------+--------------------+ Total packages found: 2

    Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo

    Description

    Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.

    Goals

    Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:

    • Gain insight into the latest AI trends, tools, and architectural concepts.
    • Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).

    Resources

    • Red Hat AI Topic Articles

      • https://www.redhat.com/en/topics/ai

    • Kubeflow Documentation

      • https://www.kubeflow.org/docs/

    • Q4 2025 CNCF Technology Landscape Radar report:

      • https://www.cncf.io/announcements/2025/11/11/cncf-and-slashdata-report-finds-leading-ai-tools-gaining-adoption-in-cloud-native-ecosystems/
      • https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf

    • Agent-to-Agent (A2A) Protocol

      • https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/

    Kubernetes-Based ML Lifecycle Automation by lmiranda

    Description

    This project aims to build a complete end-to-end Machine Learning pipeline running entirely on Kubernetes, using Go, and containerized ML components.

    The pipeline will automate the lifecycle of a machine learning model, including:

    • Data ingestion/collection
    • Model training as a Kubernetes Job
    • Model artifact storage in an S3-compatible registry (e.g. Minio)
    • A Go-based deployment controller that automatically deploys new model versions to Kubernetes using Rancher
    • A lightweight inference service that loads and serves the latest model
    • Monitoring of model performance and service health through Prometheus/Grafana

    The outcome is a working prototype of an MLOps workflow that demonstrates how AI workloads can be trained, versioned, deployed, and monitored using the Kubernetes ecosystem.

    Goals

    By the end of Hack Week, the project should:

    1. Produce a fully functional ML pipeline running on Kubernetes with:

      • Data collection job
      • Training job container
      • Storage and versioning of trained models
      • Automated deployment of new model versions
      • Model inference API service
      • Basic monitoring dashboards

    2. Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.

    3. Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).

    4. Prepare a short demo explaining the end-to-end process and how new models flow through the system.

    Resources

    Project Repository

    Updates

    1. Training pipeline and datasets
    2. Inference Service py

    A CLI for Harvester by mohamed.belgaied

    Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI. Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.

    Project Description

    Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as: harvester vm create my-vm --count 5 to create 5 VMs named my-vm-01 to my-vm-05.

    asciicast

    Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.

    Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli

    Done in previous Hackweeks

    • Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
    • Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE

    Goal for this Hackweek

    The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.

    Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it

    Issue list is here: https://github.com/belgaied2/harvester-cli/issues

    Resources

    The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact). Welcome contributions are:

    • Testing it and creating issues
    • Documentation
    • Go code improvement

    What you might learn

    Harvester CLI might be interesting to you if you want to learn more about:

    • GitHub Actions
    • Harvester as a SUSE Product
    • Go programming language
    • Kubernetes API
    • Kubevirt API objects (Manipulating VMs and VM Configuration in Kubernetes using Kubevirt)

    Cluster API Provider for Harvester by rcase

    Project Description

    The Cluster API "infrastructure provider" for Harvester, also named CAPHV, makes it possible to use Harvester with Cluster API. This enables people and organisations to create Kubernetes clusters running on VMs created by Harvester using a declarative spec.

    The project has been bootstrapped in HackWeek 23, and its code is available here.

    Work done in HackWeek 2023

    • Have a early working version of the provider available on Rancher Sandbox : *DONE *
    • Demonstrated the created cluster can be imported using Rancher Turtles: DONE
    • Stretch goal - demonstrate using the new provider with CAPRKE2: DONE and the templates are available on the repo

    DONE in HackWeek 24:

    DONE in 2025 (out of Hackweek)

    • Support of ClusterClass
    • Add to clusterctl community providers, you can add it directly with clusterctl
    • Testing on newer versions of Harvester v1.4.X and v1.5.X
    • Support for clusterctl generate cluster ...
    • Improve Status Conditions to reflect current state of Infrastructure
    • Improve CI (some bugs for release creation)

    Goals for HackWeek 2025

    • FIRST and FOREMOST, any topic is important to you
    • Add e2e testing
    • Certify the provider for Rancher Turtles
    • Add Machine pool labeling
    • Add PCI-e passthrough capabilities.
    • Other improvement suggestions are welcome!

    Thanks to @isim and Dominic Giebert for their contributions!

    Resources

    Looking for help from anyone interested in Cluster API (CAPI) or who wants to learn more about Harvester.

    This will be an infrastructure provider for Cluster API. Some background reading for the CAPI aspect:

    Technical talks at universities by agamez

    Description

    This project aims to empower the next generation of tech professionals by offering hands-on workshops on containerization and Kubernetes, with a strong focus on open-source technologies. By providing practical experience with these cutting-edge tools and fostering a deep understanding of open-source principles, we aim to bridge the gap between academia and industry.

    For now, the scope is limited to Spanish universities, since we already have the contacts and have started some conversations.

    Goals

    • Technical Skill Development: equip students with the fundamental knowledge and skills to build, deploy, and manage containerized applications using open-source tools like Kubernetes.
    • Open-Source Mindset: foster a passion for open-source software, encouraging students to contribute to open-source projects and collaborate with the global developer community.
    • Career Readiness: prepare students for industry-relevant roles by exposing them to real-world use cases, best practices, and open-source in companies.

    Resources

    • Instructors: experienced open-source professionals with deep knowledge of containerization and Kubernetes.
    • SUSE Expertise: leverage SUSE's expertise in open-source technologies to provide insights into industry trends and best practices.