Starting from prometheus ( and grafana if needed), learn how to monitor kubernetes and docker and do some valid alert/graph etc.
https://docs.docker.com/config/thirdparty/prometheus/
Looking for hackers with the skills:
This project is part of:
Hack Week 17
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terraform-provider-feilong by e_bischoff
Project Description
People need to test operating systems and applications on s390 platform.
Installation from scratch solutions include:
- just deploy and provision manually
(with the help of ftpbootscript, if you are at SUSE) - use
s3270terminal emulation (used byopenQApeople?) - use
LXCfrom IBM to start CP commands and analyze the results - use
zPXEto do some PXE-alike booting (used by theorthosteam?) - use
tessiato install from scratch using autoyast - use
libvirtfor s390 to do some nested virtualization on some already deployed z/VM system - directly install a Linux kernel on a LPAR and use
kvm+libvirtfrom there
Deployment from image solutions include:
- use
ICICweb interface (openstackin disguise, contributed by IBM) - use
ICICfrom theopenstackterraformprovider (used byRancherQA) - use
zvm_ansibleto controlSMAPI - connect directly to
SMAPIlow-level socket interface
IBM Cloud Infrastructure Center (ICIC) harnesses the Feilong API, but you can use Feilong without installing ICIC, provided you set up a "z/VM cloud connector" into one of your VMs following 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 system/z.
Other Feilong-based solutions include:
- make
libvirtFeilong-aware - simply call
Feilongfrom shell scripts withcurl - use
zvmconnectorclient python library from Feilong - use
zthinpart of Feilong to directly commandSMAPI.
Goal for Hackweek 23
My final goal is 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.
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
Create a Cloud-Native policy engine with notifying capabilities to optimize resource usage by gbazzotti
Description
The goal of this project is to begin the initial phase of development of an all-in-one Cloud-Native Policy Engine that notifies resource owners when their resources infringe predetermined policies. This was inspired by a current issue in the CES-SRE Team where other solutions seemed to not exactly correspond to the needs of the specific workloads running on the Public Cloud Team space.
The initial architecture can be checked out on the Repository listed under Resources.
Among the features that will differ this project from other monitoring/notification systems:
- Pre-defined sensible policies written at the software-level, avoiding a learning curve by requiring users to write their own policies
- All-in-one functionality: logging, mailing and all other actions are not required to install any additional plugins/packages
- Easy account management, being able to parse all required configuration by a single JSON file
- Eliminate integrations by not requiring metrics to go through a data-agreggator
Goals
- Create a minimal working prototype following the workflow specified on the documentation
- Provide instructions on installation/usage
- Work on email notifying capabilities
Resources
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
Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios
Description
Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.
This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.
The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.
Goals
By the end of Hack Week, we aim to have a single, working Python script that:
- Connects to Prometheus and executes a query to fetch detailed test failure history.
- Processes the raw data into a format suitable for the Gemini API.
- Successfully calls the Gemini API with the data and a clear prompt.
- Parses the AI's response to extract a simple list of flaky tests.
- Saves the list to a JSON file that can be displayed in Grafana.
- New panel in our Dashboard listing the Flaky tests
Resources
- Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
- Data Source: Our internal Prometheus server.
- Key Metric:
jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}. - Existing Query for Reference:
count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range])). - AI Model: The Google Gemini API.
- Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
- Visualization: Our internal Grafana Dashboard.
- Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring
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:
- Add more Unit Tests
- Improve Status Conditions for some phases
- Add cloud provider config generation
- Testing with Harvester v1.3.2
- Template improvements
- Issues creation
DONE in 2025 (out of Hackweek)
- Support of ClusterClass
- Add to
clusterctlcommunity providers, you can add it directly withclusterctl - 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:
The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio
Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it. 
The Plan
Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!
Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:
❥ The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.
❥ The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.
❥ Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.
If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.
Why?
We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.
The Outputs
❥ A "State of the Agentic Union" for SUSE engineers, detailing what works, what explodes, and how much coffee we can drink while the robots do the rebasing.
❥ Honest, Daily Updates With All the Gory Details
Mammuthus - The NFS-Ganesha inside Kubernetes controller by vcheng
Description
As the user-space NFS provider, the NFS-Ganesha is wieldy use with serval projects. e.g. Longhorn/Rook. We want to create the Kubernetes Controller to make configuring NFS-Ganesha easy. This controller will let users configure NFS-Ganesha through different backends like VFS/CephFS.
Goals
- Create NFS-Ganesha Package on OBS: nfs-ganesha5, nfs-ganesha6
- Create NFS-Ganesha Container Image on OBS: Image
- Create a Kubernetes controller for NFS-Ganesha and support the VFS configuration on demand. Mammuthus
Resources
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.
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.
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)
Uyuni Health-check Grafana AI Troubleshooter by ygutierrez
Description
This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.
Goals
- Investigate if and how the
grafana-llm-appplug-in can be used within the Uyuni Health-check tool. - Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
- Evaluate support for local LLMs and external APIs through the plugin.
- Evaluate if and how the Uyuni MCP server could be integrated as another source of information.
Resources
Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios
Description
Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.
This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.
The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.
Goals
By the end of Hack Week, we aim to have a single, working Python script that:
- Connects to Prometheus and executes a query to fetch detailed test failure history.
- Processes the raw data into a format suitable for the Gemini API.
- Successfully calls the Gemini API with the data and a clear prompt.
- Parses the AI's response to extract a simple list of flaky tests.
- Saves the list to a JSON file that can be displayed in Grafana.
- New panel in our Dashboard listing the Flaky tests
Resources
- Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
- Data Source: Our internal Prometheus server.
- Key Metric:
jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}. - Existing Query for Reference:
count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range])). - AI Model: The Google Gemini API.
- Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
- Visualization: Our internal Grafana Dashboard.
- Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring
