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Robusta KRR (Kubernetes Resource Recommender) is a CLI tool for **optimizing resource allocation** in Kubernetes clusters. It gathers pod usage data from Prometheus and **recommends requests and limits** for CPU and memory. This **reduces costs and improves performance**.
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### Auto-Apply Mode
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**New:** Put right-sizing on auto-pilot by applying recommendations automatically. [Request beta access](https://robusta-dev.typeform.com/krr-auto-apply).
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### Data Integrations
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[](#data-source-integrations)
[](#integrations)
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_View instructions for: [Seeing recommendations in a UI](#free-ui-for-krr-recommendations), [Sending recommendations to Slack](#slack-notification), [Setting up KRR as a k9s plugin](#k9s-plugin)_
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_View instructions for: [Seeing recommendations in a UI](#free-ui-for-krr-recommendations), [Sending recommendations to Slack](#slack-notification), [Setting up KRR as a k9s plugin](#k9s-plugin), [Azure Blob Storage Export with Teams Notification](#azure-blob-teams-integration)_
Group jobs that have specific labels into GroupedJob objects for consolidated resource recommendations. This is useful for batch jobs, data processing pipelines, or any workload where you want to analyze resource usage across multiple related jobs.
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```sh
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krr simple --job-grouping-labels app,team
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```
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This will:
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- Group jobs that have either `app` or `team` labels (or both)
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- Create GroupedJob objects with names like `app=frontend`, `team=backend`, etc.
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- Provide resource recommendations for the entire group instead of individual jobs
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- Jobs with the specified labels will be excluded from regular Job listing
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You can specify multiple labels separated by commas:
* Each label group will include at most **N** jobs (e.g., the first 3 returned by the API).
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* Other matching jobs beyond the limit are ignored for that group.
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* If not specified, the default limit is **500** jobs per group.
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</details>
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<details>
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<summary>Override the kubectl context</summary>
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@@ -369,6 +410,7 @@ Currently KRR ships with a few formatters to represent the scan data:
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-`yaml`
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-`pprint` - data representation from python's pprint library
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-`csv` - export data to a csv file in the current directory
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-`csv-raw` - csv with raw data for calculation
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-`html`
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To run a strategy with a selected formatter, add a `-f` flag. Usually this should be combined with `--fileoutput <filename>` to write clean output to file without logs:
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