KubePattern

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Roadmap ​

Welcome to our public roadmap! This is where you can see what I am working on, what I have planned for the near future, and what ideas I am considering.

WARNING

This roadmap represents my current vision and priorities. It is not a delivery promise, and features or timelines may change based on user feedback, market opportunities, or technical challenges.

🚀 Now (In Progress) ​

The features I am currently working on, which will be released in the next few weeks.

  • [ ] Refactoring and Performance Improvements: Continue optimizing the codebase for better performance and maintainability, ensuring that KubePattern runs efficiently even in large clusters.

🎯 Next (Planned) ​

The features I am planning to work on after the current ones are completed.

  • [ ] Relationships Label Selector: Enhance pattern definitions to allow specifying label selectors for relationships between resources, enabling more flexible and powerful pattern detection.

🤔 Later (Considering) ​

The features I am considering for future development, but have not yet committed to.

  • [ ] AI-Powered Pattern Creation: Explore the use of AI to assist users in creating new patterns based on common architectural issues and best practices.
  • [ ] AI-Powered Smell Resolution Suggestions: Provide AI-generated suggestions for resolving detected smells, helping users understand how to fix architectural issues in their clusters.
  • [ ] AI-Powered Step in the Loop: Integrate AI into the analysis process to provide real-time insights and recommendations as patterns are being evaluated.
  • [ ] CI/CD Integration: Develop integrations with popular CI/CD tools to allow automatic analysis of Kubernetes manifests during the deployment process, ensuring that architectural issues are caught early.
  • [ ] Make KubePattern a Kubernetes Operator: Transform KubePattern into a Kubernetes Operator to enable more seamless integration with Kubernetes clusters and provide a more native experience for users.

✅ Recently Launched ​

The big features recentely launched 🚀

  • [x] Initial Release: Launched the first version of KubePattern, introducing the core concept of Pattern-as-Code and providing a set of predefined patterns for detecting architectural issues in Kubernetes clusters.
  • [x] Moved from KubePattern Java to Go: Rewrote the entire codebase from Java to Go, improving performance, reducing resource consumption, and enhancing the overall user experience.
  • [x] Ownership Relationships support: Enhance pattern definitions to allow specifying ownership and label selector relationships between resources, enabling more complex and accurate pattern detection.
  • [x] Lazy Fetching: Optimize performance by only fetching resources that are relevant to the patterns being analyzed.
  • [x] Auto Remove Smells: Implement a mechanism to automatically remove smells that are no longer relevant, such as those that have been resolved or are outdated.