Uyuni Health-check Grafana 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

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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

• 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

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Move Uyuni Test Framework from Selenium to Playwright + AI by oscar-barrios

Description

This project aims to migrate the existing Uyuni Test Framework from Selenium to Playwright. The move will improve the stability, speed, and maintainability of our end-to-end tests by leveraging Playwright's modern features. We'll be rewriting the current Selenium code in Ruby to Playwright code in TypeScript, which includes updating the test framework runner, step definitions, and configurations. This is also necessary because we're moving from Cucumber Ruby to CucumberJS.

If you're still curious about the AI in the title, it was just a way to grab your attention. Thanks for your understanding.

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Goals

• Migrate Core tests including Onboarding of clients
• Improve test reliabillity: Measure and confirm a significant reduction of flakynes.
• Implement a robust framework: Establish a well-structured and reusable Playwright test framework using the CucumberJS

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Resources

• Existing Uyuni Test Framework (Cucumber Ruby + Capybara + Selenium)
• My Template for CucumberJS + Playwright in TypeScript
• Started Hackweek Project

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Casky – Lightweight C Key-Value Engine with Crash Recovery by pperego

Description

Casky is a lightweight, crash-safe key-value store written in C, designed for fast storage and retrieval of data with a minimal footprint. Built using Test-Driven Development (TDD), Casky ensures reliability while keeping the codebase clean and maintainable. It is inspired by Bitcask and aims to provide a simple, embeddable storage engine that can be integrated into microservices, IoT devices, and other C-based applications.

Objectives:

• Implement a minimal key-value store with append-only file storage.
• Support crash-safe persistence and recovery.
• Expose a simple public API: store(key, value), load(key), delete(key).
• Follow TDD methodology for robust and testable code.
• Provide a foundation for future extensions, such as in-memory caching, compaction, and eventual integration with vector-based databases like PixelDB.

Why This Project is Interesting:

Casky combines low-level C programming with modern database concepts, making it an ideal playground to explore storage engines, crash safety, and performance optimization. It’s small enough to complete during Hackweek, yet it provides a solid base for future experiments and more complex projects.

Goals

• Working prototype with append-only storage and memtable.
• TDD test suite covering core functionality and recovery.
• Demonstration of basic operations: insert, load, delete.
• Optional bonus: LRU caching, file compaction, performance benchmarks.

Future Directions:

After Hackweek, Casky can evolve into a backend engine for projects like PixelDB, supporting vector storage and approximate nearest neighbor search, combining low-level performance with cutting-edge AI retrieval applications.

Resources

The Bitcask paper: https://riak.com/assets/bitcask-intro.pdf The Casky repository: https://github.com/thesp0nge/casky

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Time-travelling topology on the Rocks by fvanlankvelt

Description

The current implementation of the Time-Travelling Topology database, StackGraph, has served SUSE Observability well over the years. But it is dependent on a number of complex components - Zookeeper, HDFS, HBase, Tephra. These lead to a large number of failure scenarios and parameters to tweak for optimal performance.

The goal of this project is to take the high-level requirements (time-travelling topology, querying over time, transactional changes to topology, scalability) and design/prototype key components, to see where they would lead us if we were to start from scratch today.

An example would be to use Kafka Streams to consolidate topology history (and its index) in sharded RocksDB key-value stores (native to stateful stream processors). A distributed transaction manager (DTM) should also be possible, by using a single Kafka partition for atomic writes.

Persistence with RocksDB would allow time travelling by using the merge operator.

Goals

Determine feasibility of implementing the model on a whole new architecture. E.g. a proof of concept for a DTM, find out how hard it is to do querying over time (merge operator?), howto route fetch requests to the correct instance, etcetera.

Resources

Backend developers, preferably experienced in distributed systems / stream processing. Programming language: scala 3 with some C++ for low-level.

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