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Kubernetes Secret

Jason Shaw edited this page Nov 11, 2023 · 15 revisions

Create a Kubernetes Secret (for all deployments)

Using a Kubernetes Secret to store your Turbonomic Server credentials is the recommended method, and a secret name of turbonomic-credentials is automatically used by default by the Kubeturbo deployment.

If a secret is created and used in your kubeturbo deployment it will take precedence over any plain text username and password defined in the configMap.

Once the secret is created it is available for use in any of the kubeturbo deployment methods. Remember to make note of the secret name if you are NOT using the recommended default name of turbonomic-credentials as it will be needed when configuring kubeturbo to use a non-default/customized secret name.

If running the OpenShift Container Platform and prefer to use the OCP console? Refer to this article here for option #2 OpenShift Console.

Before starting this section make sure you have followed all of the Username and Password Requirements section here to create the credentials in the Turbonomic Server first.

Create secret in YAML

  1. Create a namespace to deploy kubeturbo into which is where you need to create the Kubernetes secret (samples provided use turbo)

  2. Create a secret with type Opaque and data key/value pair username: and password:

  • Here is an example with a reference yaml to create a secret.

  • This yaml example assumes your secret will be using the recommended default name of turbonomic-credentials, preferred as kubeturbo will automatically check for and use this secret if it exists. Modify the namespace value if needed.

apiVersion: v1
kind: Secret
metadata:
  name: turbonomic-credentials
  namespace: turbo
type: Opaque
data:
  username: #####<replace with base64 encoded value>
  password: #####<replace with base64 encoded value>
  • The kubeturbo username and password need to be added in base64 encoded values, as in example shown below:
apiVersion: v1
kind: Secret
metadata:
  name: turbonomic-credentials
  namespace: turbo
type: Opaque
data:
  username: a3ViZXR1cmJvCg==
  password: S3ViZVR1cmJvIzEyMwo=

Create secret in OpenShift Console

  1. In the project where you have deployed the Kubeturbo Operator, go to Workload -> Secrets, click Create and select Key/value secret option.
  1. Add key / value pairs with the information below, in plain text, and then click Create
  • Secret name: turbonomic-credentials
  • Key: username
  • Value: TURBOADMINUSER
  • Key: password
  • Value: TURBOPASSWORD
  1. Once created the username and password values will automatically be encoded in base64 when you view the secret after it is created.
turbo secret

Kubeturbo

Introduction
  1. What's new
  2. Supported Platforms
Kubeturbo Use Cases
  1. Overview
  2. Getting Started
  3. Full Stack Management
  4. Optimized Vertical Scaling
  5. Effective Cluster Management
  6. Intelligent SLO Scaling
  7. Proactive Rescheduling
  8. Better Cost Management
  9. GitOps Integration
  10. Observability and Reporting
Kubeturbo Deployment
  1. Deployment Options Overview
  2. Prerequisites
  3. Turbonomic Server Credentials
  4. Deployment by Helm Chart
    a. Updating Kubeturbo image
  5. Deployment by Yaml
    a. Updating Kubeturbo image
  6. Deployment by Operator
    a. Updating Kubeturbo image
  7. Deployment by Red Hat OpenShift OperatorHub
    a. Updating Kubeturbo image
Kubeturbo Config Details and Custom Configurations
  1. Turbonomic Server Credentials
  2. Working with a Private Repo
  3. Node Roles: Control Suspend and HA Placement
  4. CPU Frequency Getter Job Details
  5. Logging
  6. Actions and Special Cases
Actions and how to leverage them
  1. Overview
  2. Resizing or Vertical Scaling of Containerized Workloads
    a. DeploymentConfigs with manual triggers in OpenShift Environments
  3. Node Provision and Suspend (Cluster Scaling)
  4. SLO Horizontal Scaling
  5. Turbonomic Pod Moves (continuous rescheduling)
  6. Pod move action technical details
    a. Red Hat Openshift Environments
    b. Pods with PVs
IBM Cloud Pak for Data & Kubeturbo:Evaluation Edition
Troubleshooting
  1. Startup and Connectivity Issues
  2. KubeTurbo Health Notification
  3. Logging: kubeturbo log collection and configuration options
  4. Startup or Validation Issues
  5. Stitching Issues
  6. Data Collection Issues
  7. Collect data for investigating Kubernetes deployment issue
  8. Changes to Cluster Role Names and Cluster Role Binding Names
Kubeturbo and Server version mapping
  1. Turbonomic - Kubeturbo version mappings
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