Cost Savings (KRR)¶

Robustas KRR 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.

By optionally integrating KRR with Robusta you can:

1. Get weekly KRR scan reports in Slack via Robusta OSS (disabled by default, see below to configure)

2. View KRR scans from all your clusters in the Robusta UI (enabled by default for UI users)

Sending Weekly KRR Scan Reports to Slack¶

With or without the UI, you can configure additional scans on a schedule. The results can be sent as a PDF to Slack. Follow the steps below to set it up

1. Install Robusta with Helm to your cluster and configure Slack sink.

2. Create your KRR slack playbook by adding the following to generated_values.yaml:

# Runs a weekly krr scan on the namespace devs-namespace and sends it to the configured slack channel
customPlaybooks:
- triggers:
  - on_schedule:
      fixed_delay_repeat:
        repeat: -1 # number of times to run or -1 to run forever
        seconds_delay: 604800 # 1 week
  actions:
  - krr_scan:
      args: "--namespace devs-namespace" ## KRR args here
  sinks:
      - "main_slack_sink" # slack sink you want to send the report to here

3. Do a Helm upgrade to apply the new values: helm upgrade robusta robusta/robusta --values=generated_values.yaml --set clusterName=<YOUR_CLUSTER_NAME>

Slack Robusta UI

Taints, Tolerations and NodeSelectors¶

To run KRR on an ARM cluster or on specific nodes you can set custom tolerations or a nodeSelector in your generated_values.yaml file as follows:

globalConfig:
  krr_job_spec:
    tolerations:
    - key: "key1"
      operator: "Exists"
      effect: "NoSchedule"
    nodeSelector:
      nodeName: "your-selector"

Customizing Efficiency Recommendations in the Robusta UI¶

You can tweak KRR's recommendation algorithm to suit your environment using the krr_args setting in Robusta's Helm chart.

Add the following config to the top of your generated_values.yaml with your custom values. KRR will use these values every time it sends data to the Robusta UI or other destinations.

If you are having performance issues, specifically with Prometheus using a lot of memory, reduce max_workers to reduce memory usage. KRR uses 3 workers by default.

globalConfig:
  krr_args: "--cpu-min 15 --mem-min 200 --cpu_percentile 90 --memory_buffer_percentage 25"
  max_workers: 2

Enabling HPA Recommendations in the Robusta UI¶

To enable Horizontal Pod Autoscaler (HPA) recommendations in the Robusta UI, add the following to your generated_values.yaml file:

globalConfig:
  krr_args: "--allow-hpa"

Common KRR Settings¶

Configuring KRR Job Memory Requests and Limits¶

To prevent the KRR job from OOMKill (Out of Memory), you can configure the memory requests and limits by adding the following environment variables to your generated_values.yaml file:

runner:
  additional_env_vars:
  - name: KRR_MEMORY_REQUEST
    value: "3Gi"
  - name: KRR_MEMORY_LIMIT
    value: "3Gi"

By default, the memory request and limit are set to 2Gi. Modify these values according to your requirements.

Enable KRR security context¶

To enable KRR security context, add the following to your generated_values.yaml file:

runner:
  setKRRSecurityContext: true

KRR API¶

You can retrieve KRR recommendations programmatically using the Robusta API. This allows you to integrate resource recommendations into your own tools and workflows.

Authentication¶

The KRR API requires authentication via API key.

To create your key, on the Robusta Platform, go to the settings page, and choose the API keys tab.

Click New API Key. Choose a name for your key, and check the KRR Read capability.

GET /api/krr/recommendations¶

Retrieves KRR resource recommendations for a specific cluster and namespace.

Query Parameters

Request Headers

Example Request

curl -X GET "https://api.robusta.dev/api/query/krr/recommendations?account_id=YOUR_ACCOUNT_ID&cluster_id=my-cluster&namespace=default" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json"

Success Response (200 OK)

{
  "cluster_id": "my-cluster",
  "scan_id": "12345-67890-abcde",
  "scan_date": "2024-01-07T12:00:00Z",
  "scan_state": "success",
  "results": [
    {
      "cluster_id": "my-cluster",
      "namespace": "default",
      "name": "nginx-deployment",
      "kind": "Deployment",
      "container": "nginx",
      "priority": "MEDIUM",
      "current_cpu_request": 100,
      "recommended_cpu_request": 50,
      "current_cpu_limit": 500,
      "recommended_cpu_limit": 200,
      "current_memory_request": 134217728,
      "recommended_memory_request": 67108864,
      "current_memory_limit": 536870912,
      "recommended_memory_limit": 268435456,
      "pods_count": 3
    }
  ]
}

Response Fields

Error Responses

401 Unauthorized

{
  "error": "Invalid or missing API key"
}

400 Bad Request

{
  "msg": "Bad query parameters [error details]",
  "error_code": "BAD_PARAMETER"
}

500 Internal Server Error

{
  "msg": "Failed to query KRR recommendations",
  "error_code": "UNEXPECTED_ERROR"
}

Reference¶

Krr scan¶

robusta playbooks trigger krr_scan

customPlaybooks:
- actions:
  - krr_scan:
      prometheus_additional_labels: 'cluster: ''cluster-2-test''env: ''prod'''
      prometheus_auth: Basic YWRtaW46cGFzc3dvcmQ=
      prometheus_url: http://prometheus-k8s.monitoring.svc.cluster.local:9090
      prometheus_url_query_string: demo-query=example-data
  triggers:
  - on_schedule: {}

robusta playbooks trigger krr_scan