Training with sample data

To learn about cloud native analytics, you can install a sample data set. Learn how to install and load sample data, train the system, and see the results.

1. Run the loadSampleData.sh script from a job so that it has access to the secrets. Complete the following steps:
   1. Use the -j option with the script to generate a YAML file, such as loadSampleJob.yaml in the following example:

      kubectl delete pod ingesnoi3
      kubectl run ingesnoi3  --restart=Never --env=LICENSE=accept --image-pull-policy=Always  \
      --env=CONTAINER_IMAGE=image:image_tag  \
      -i --image=image:image_tag \
      loadSampleData.sh -- -r release_name -j  > loadSampleJob.yaml

      Where:
      • image is the location of the ea-events-tooling container, as described earlier.
      • CONTAINER_IMAGE is an environment variable, which is the same as the value you pass to the --image parameter in the Kubernetes command. This variable allows the container to populate the image details in the YAML file output that it creates.
      • image_tag is the image version tag, as described earlier.
      • release_name is the custom resource release name of your deployment.
      If you want to show the metric anomaly detection capability, you can add the following line to the previous command:

      -m "<metric API Key name>:<API Key password>"

      The complete command should be:

      kubectl delete pod ingesnoi3
      kubectl run ingesnoi3 -i --restart=Never --env=LICENSE=accept --image-pull-policy=Always  \
      --env=CONTAINER_IMAGE=image:image_tag  \
      - i --image=image:image  \
      loadSampleData.sh -- -r release_name -m "<metric API Key name>:<API Key password>" -j > loadSampleData.yaml

      Then run the command:

      run ingesnoi3 -i --restart=Never --env=LICENSE=accept --namespace netcool    
      --env=CONTAINER_IMAGE=image:image_tag
      --overrides='{ "apiVersion": "v1", "spec": { "imagePullSecrets": [{"name": "noi-registry-secret"}] } }'     
      --image=image:image_tag     
      loadSampleData.sh -- -r release_name -t cfd95b7e-3bc7-4006-a4a8-a73a79c71255     
      -a noi-service-account  -s noi-registry-secret     -j -m "<metric API Key name>:<API Key password>" > loadSampleData.yaml
      

   2. Create a job using the generated YAML file, such as loadSampleJob.yaml in the following example:

      kubectl create -f loadSampleJob.yaml -n <namespace>

      Where <namespace> is the namespace in which your deployment is installed.

   Note: If the default service account does not have access to the image repository, uncomment the image pull secrets section in the loadSampleData.yaml file and set the imagePullSecrets.name parameter to your Docker secret name before running the kubectl create command.
   A job called -loadSampleData is created. You can view the job output with the pod logs created by the job.
2. View how the sample data has been grouped.
   1. Select Alerts. The list of all alerts are displayed.
   2. Select the Example_IBM_CloudAnalytics view to see how the alerts from the sample data are grouped.
3. Turn off auto-deploy mode to view the suggested policies before they are deployed.
   The loadSampleData.sh script switches your configuration to auto-deploy mode. If you want to see the suggested policies for the sample data, and how you can manually enable them, then run the following steps to disable automatic policy deployment, and re-run a portion of the training.
   1. Get the start and end times for the sample data as in the following example.

      kubectl delete pod ingesnoi3;
      kubectl run ingesnoi3 -i --restart=Never --env=LICENSE=accept --image=image:image_tag getTimeRange.sh samples/demoTrainingData.json.gz
      pod "ingesnoi3" deleted
      {"minfirstoccurence":{"epoc":1552023064603,"formatted":"2019-03-08T05:31:04.603Z"},"maxlastoccurrence":{"epoc":1559729860924,"formatted":"2019-06-05T10:17:40.924Z"}}

   2. Note: If Review first mode is enabled by setting the -d parameter to false, Seasonality policies cannot be activated from the Suggested policies tab. Only Temporal Groupings policies can be activated from the Suggested policies tab.
      Run the script with the -d parameter set to false to disable auto-deployment.

      kubectl delete pod ingesnoi3;
      kubectl run ingesnoi3 -i --restart=Never --env=LICENSE=accept --image-pull-policy=Always --image=image:image_tag runTraining.sh -- -r test-install -a related_events -s 1552023064603 -e 1559729860924 -d false

      Where:

      • image is the location of the ea-events-tooling container, as described earlier.
      • image_tag is the image version tag, as described earlier.
      • -s and -e are the start and end times that are returned in step 3 a.
   Note: You may be required to override the registry secret by adding the following option to all of the kubectl commands that you issue:

   --overrides='{ "apiVersion": "v1", "spec": { "imagePullSecrets":
         [{"name": "evtmanager-registry-secret"}] } }'