monitor kubernetes and docker with prometheus
an invention by dmaiocchi
Updated over 7 years ago. No love.

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:

golang prometheus monitoring kubernetes docker grafana

This project is part of:

Hack Week 17

Activity

  • over 7 years ago: dmaiocchi added keyword "grafana" to this project.
  • over 7 years ago: dmaiocchi added keyword "golang" to this project.
  • over 7 years ago: dmaiocchi added keyword "prometheus" to this project.
  • over 7 years ago: dmaiocchi added keyword "monitoring" to this project.
  • over 7 years ago: dmaiocchi added keyword "kubernetes" to this project.
  • over 7 years ago: dmaiocchi added keyword "docker" to this project.
  • over 7 years ago: dmaiocchi started this project.
  • over 7 years ago: dmaiocchi originated this project.


    • Comments

    Be the first to comment!

    Similar Projects

    Contribute to terraform-provider-libvirt by pinvernizzi

    Description

    The SUSE Manager (SUMA) teams' main tool for infrastructure automation, Sumaform, largely relies on terraform-provider-libvirt. That provider is also widely used by other teams, both inside and outside SUSE.

    It would be good to help the maintainers of this project and give back to the community around it, after all the amazing work that has been already done.

    If you're interested in any of infrastructure automation, Terraform, virtualization, tooling development, Go (...) it is also a good chance to learn a bit about them all by putting your hands on an interesting, real-use-case and complex project.

    Goals

    • Get more familiar with Terraform provider development and libvirt bindings in Go
    • Solve some issues and/or implement some features
    • Get in touch with the community around the project

    Resources

    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

    Play with the userfaultfd(2) system call and download on demand using HTTP Range Requests with Golang by rbranco

    Description

    The userfaultfd(2) is a cool system call to handle page faults in user-space. This should allow me to list the contents of an ISO or similar archive without downloading the whole thing. The userfaultfd(2) part can also be done in theory with the PROT_NONE mprotect + SIGSEGV trick, for complete Unix portability, though reportedly being slower.

    Goals

    1. Create my own library for userfaultfd(2) in Golang.
    2. Create my own library for HTTP Range Requests.
    3. Complete portability with Unix.
    4. Benchmarks.
    5. Contribute some tests to LTP.

    Resources

    1. https://docs.kernel.org/admin-guide/mm/userfaultfd.html
    2. https://www.cons.org/cracauer/cracauer-userfaultfd.html

    Rewrite Distrobox in go (POC) by fabriziosestito

    Description

    Rewriting Distrobox in Go.

    Main benefits:

    • Easier to maintain and to test
    • Adapter pattern for different container backends (LXC, systemd-nspawn, etc.)

    Goals

    • Build a minimal starting point with core commands
    • Keep the CLI interface compatible: existing users shouldn't notice any difference
    • Use a clean Go architecture with adapters for different container backends
    • Keep dependencies minimal and binary size small
    • Benchmark against the original shell script

    Resources

    • Upstream project: https://github.com/89luca89/distrobox/
    • Distrobox site: https://distrobox.it/
    • ArchWiki: https://wiki.archlinux.org/title/Distrobox

    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:

    1. Connects to Prometheus and executes a query to fetch detailed test failure history.
    2. Processes the raw data into a format suitable for the Gemini API.
    3. Successfully calls the Gemini API with the data and a clear prompt.
    4. Parses the AI's response to extract a simple list of flaky tests.
    5. Saves the list to a JSON file that can be displayed in Grafana.
    6. New panel in our Dashboard listing the Flaky tests

    Resources

    Outcome

    OpenPlatform Self-Service Portal by tmuntan1

    Description

    In SUSE IT, we developed an internal developer platform for our engineers using SUSE technologies such as RKE2, SUSE Virtualization, and Rancher. While it works well for our existing users, the onboarding process could be better.

    To improve our customer experience, I would like to build a self-service portal to make it easy for people to accomplish common actions. To get started, I would have the portal create Jira SD tickets for our customers to have better information in our tickets, but eventually I want to add automation to reduce our workload.

    Goals

    • Build a frontend website (Angular) that helps customers create Jira SD tickets.
    • Build a backend (Rust with Axum) for the backend, which would do all the hard work for the frontend.

    Resources (SUSE VPN only)

    • development site: https://ui-dev.openplatform.suse.com/login?returnUrl=%2Fopenplatform%2Fforms
    • https://gitlab.suse.de/itpe/core/open-platform/op-portal/backend
    • https://gitlab.suse.de/itpe/core/open-platform/op-portal/frontend

    Preparing KubeVirtBMC for project transfer to the KubeVirt organization by zchang

    Description

    KubeVirtBMC is preparing to transfer the project to the KubeVirt organization. One requirement is to enhance the modeling design's security. The current v1alpha1 API (the VirtualMachineBMC CRD) was designed during the proof-of-concept stage. It's immature and inherently insecure due to its cross-namespace object references, exposing security concerns from an RBAC perspective.

    The other long-awaited feature is the ability to mount virtual media so that virtual machines can boot from remote ISO images.

    Goals

    1. Deliver the v1beta1 API and its corresponding controller implementation
    2. Enable the Redfish virtual media mount function for KubeVirt virtual machines

    Resources

    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)

    Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

    Self-Scaling LLM Infrastructure Powered by Rancher

    logo

    Description

    The Problem

    Running LLMs can get expensive and complex pretty quickly.

    Today there are typically two choices:

    1. Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
    2. Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.

    What if there was a middle ground?

    What if infrastructure scaled itself instead of making you scale it?

    Can we use existing Rancher capabilities like CAPI, autoscaling, and GitOps to make this simpler instead of building everything from scratch?

    Project Repository: github.com/alexander-demicev/llmserverless

    What This Project Does

    A key feature is hybrid deployment: requests can be routed based on complexity or privacy needs. Simple or low-sensitivity queries can use public APIs (like OpenAI), while complex or private requests are handled in-house on local infrastructure. This flexibility allows balancing cost, privacy, and performance - using cloud for routine tasks and on-premises resources for sensitive or demanding workloads.

    A complete, self-scaling LLM infrastructure that:

    • Scales to zero when idle (no idle costs)
    • Scales up automatically when requests come in
    • Adds more nodes when needed, removes them when demand drops
    • Runs on any infrastructure - laptop, bare metal, or cloud

    Think of it as "serverless for LLMs" - focus on building, the infrastructure handles itself.

    How It Works

    A combination of open source tools working together:

    Flow:

    • Users interact with OpenWebUI (chat interface)
    • Requests go to LiteLLM Gateway
    • LiteLLM routes requests to:
      • Ollama (Knative) for local model inference (auto-scales pods)
      • Or cloud APIs for fallback

    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. A GitHub robot mascot trying to lasso a blue bull with a Kubernetes logo tatooed on 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 CONCLUSION!!!

    A add-emoji State of the Union add-emoji document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below! add-emoji

    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:

    1. Connects to Prometheus and executes a query to fetch detailed test failure history.
    2. Processes the raw data into a format suitable for the Gemini API.
    3. Successfully calls the Gemini API with the data and a clear prompt.
    4. Parses the AI's response to extract a simple list of flaky tests.
    5. Saves the list to a JSON file that can be displayed in Grafana.
    6. New panel in our Dashboard listing the Flaky tests

    Resources

    Outcome

    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-app plug-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

    Grafana LMM plug-in

    Uyuni Health-check