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Announcing GoKubeDownscaler 1.3.0

· 3 min read
GoKubeDownscaler Maintainers
GoKubeDownscaler Maintainers
Development Team of the GoKubeDownscaler

We are pleased to announce the release of version 1.3.0 of GoKubeDownscaler. This release represents a significant milestone as it officially brings the project to GA and brings it into feature parity with the original, popular PyKubeDownscaler written in Python.

From now on, all development and new features will focus primarily on this project, with the goal of making it available within the CNCF landscape.

New Features​

Over the past few months, in addition to adapting PyKubeDownscaler features within this new project, we have been working hard to release as many new features and improvements as possible to make Kubernetes downscaling and cost optimization workflows as effective as possible. Among the new features are:

  • Introduction of Metrics: Starting today, GoKubeDownscaler exposes a series of metrics that can be used for production alerts, general statistics, and tracking of CPU and memory savings. Metrics can be exposed using the --metrics argument.
  • Performance Optimizations: GoKubeDownscaler introduces new quality-of-life performance improvements. API calls have been optimized and reduced, multithreaded execution has been introduced, leveraging Go's native parallelism. Resource usage has been minimized, especially during idle situations.
  • Webhook Introduction: An optional webhook has been introduced that can perform scaling during resource admission phases. This is very useful for ensuring cluster compliance and avoiding unnecessary scale-ups that could occur if a resource is deployed between two KubeDownscaler cycles. The webhook can be installed via Helm Chart.
  • Regulated API Server Access: GoKubeDownscaler now natively supports and customizes QPS and Burst values to avoid saturating the API Server during downscaling peaks. The user can adjust these values using the --qps and --burst arguments.
  • Introducing JSON Logs: GoKubeDownscaler can now print logs in JSON format using the --json-logs argument.

How To Migrate From PyKubeDownscaler​

To migrate from PyKubeDownscaler, you can follow the dedicated guide in our documentation at this link. We expect the migration to be quick and with only a few changes required. We recommend reading the guide carefully to avoid missing any details. The development team is available to answer any questions or address any issues within the "Issues" or "Discussions" sections of the GitHub repository.

PyKubeDownscaler Set In Maintenance Mode​

For anyone wondering what will happen from now on to the PyKubeDownscaler project, our fork of the original KubeDownscaler that we have maintained, evolved, and refined over the past two years, we assure you that we will continue to support the tool with bug fixes, security patches, helm chart changes, and the introduction of any mandatory features.

Adopters​

If your company actively uses the GoKubeDownscaler, you can contact us through our slack channel to add your logo to our showcase carousel in the homepage of our website.

Conclusions​

The development team sincerely thanks everyone who, over the past year and half, supported the development of this new tool by volunteering their time, offering ideas, suggestions, and pull requests. In the coming months we expect to add as many features as possible to make this project more and more attractive, stay tuned!