Empty objectives are fine for DPA manifest - #3400
Conversation
![chatgpt-codex-connector[bot]](https://avatars.githubusercontent.com/in/1144995?s=60&v=4){kind=small}
There was a problem hiding this comment.
💡 Codex Review
Here are some automated review suggestions for this pull request.
Reviewed commit: 710e2c58e4
ℹ️ About Codex in GitHub
Codex has been enabled to automatically review pull requests in this repo. Reviews are triggered when you
- Open a pull request for review
- Mark a draft as ready
- Comment "@codex review".
If Codex has suggestions, it will comment; otherwise it will react with 👍.
When you sign up for Codex through ChatGPT, Codex can also answer questions or update the PR, like "@codex address that feedback".
|
🎯 Code Coverage (details) 🔗 Commit SHA: 87f9335 | Docs | View more details | Give us feedback! |
|
I thought we already did it, make sure the Cluster Agent requires an objective for horizontal or multi-dim. Note: you need to use the proper versions of tools to generate (wipe |
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
c4fe0aa to
87f9335
Compare
|
@vboulineau for now, we agreed with @clamoriniere that we will rely on default of 75% on CPU set by backend - https://dd.slack.com/archives/C08FUDH30EA/p1787778072416079?thread_ts=1787755494.894189&cid=C08FUDH30EA |
gh-worker-dd-devflow-36fce6
Bot
added
mergequeue-status: queued
mergequeue-status: in_progress
and removed
mergequeue-status: queued
labels
3209b94
into
main
3 participants
Remove
MinItems=1validation from the Objectives field on both DatadogPodAutoscaler and DatadogPodAutoscalerClusterProfile. This allows users to define a DPA with an empty objectives array, which is needed when all scaling configuration comes from the custom-recommender annotation settings or vertical-only autoscalers.