1.1 Tasks: Setup

Executing oc commands

Task 1.1.1: Identify your monitoring repository

Before we get started, take the time to familiarize yourself with the your config repository. In the training you will not be working with the config repository of your team to prevent the training resources from getting in the way of the day to day business. You can find more information on how to deploy the Baloise monitoring stack for your team at Deploying the Baloise Monitoring Stack .

The working directory for this training is the folder in your config repository with the -monitoring suffix. If necessary, create the directory <team>-monitoring.

Task 1.1.2: Deploy example application

Create the following file training_python-deployment.yaml in your monitoring directory.

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: example-web-python
  name: example-web-python
spec:
  replicas: 1
  selector:
    matchLabels:
      app: example-web-python
  template:
    metadata:
      labels:
        app: example-web-python
    spec:
      containers:
      - image: quay.io/acend/example-web-python
        imagePullPolicy: Always
        name: example-web-python
      restartPolicy: Always

Use the following command to verify that the pod of the deployment example-web-python is ready and running (use CTRL+C to exit the command).

team=<team>
oc -n $team-monitoring get pod -w -l app=example-web-python

We also need to create a Service for the new application. Create a file with the name training_python-service.yaml with the following content:

apiVersion: v1
kind: Service
metadata:
  name: example-web-python
  labels:
    app: example-web-python
spec:
  ports:
    - name: http
      port: 5000
      protocol: TCP
      targetPort: 5000
  selector:
    app: example-web-python
  type: ClusterIP

This created a so-called Kubernetes Service

team=<team>
oc -n $team-monitoring get svc -l app=example-web-python

Which gives you an output similar to this:

NAME                 TYPE       CLUSTER-IP       EXTERNAL-IP   PORT(S)          AGE
example-web-python                  ClusterIP   172.24.195.25    <none>        5000/TCP                     24s

Our example application can now be reached on port 5000.

We can now make the application directly available on our machine using port-forward

team=<team>
oc -n $team-monitoring port-forward svc/example-web-python 5000

Use curl and verify the successful deployment of our example application in a separate terminal:

curl localhost:5000/metrics

Should result in something like:

# HELP python_gc_objects_collected_total Objects collected during gc
# TYPE python_gc_objects_collected_total counter
python_gc_objects_collected_total{generation="0"} 541.0
python_gc_objects_collected_total{generation="1"} 344.0
python_gc_objects_collected_total{generation="2"} 15.0
...

Since our newly deployed application now exposes metrics, the next thing we need to do, is to tell our Prometheus server to scrape metrics from the Kubernetes deployment. In a highly dynamic environment like Kubernetes this is done with so called Service Discovery.

Task 1.1.3: Create a ServiceMonitor

Task description:

Create a ServiceMonitor for the example application.

• Create a ServiceMonitor, which will configure Prometheus to scrape metrics from the example-web-python application every 30 seconds.

For this to work, you need to ensure:

• The example-web-python Service is labeled correctly and matches the labels you’ve defined in your ServiceMonitor.
• The port name in your ServiceMonitor configuration matches the port name in the Service definition.
  • hint: check with oc -n <team>-monitoring get service example-web-python -o yaml
• Verify the target in the Prometheus user interface.

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  labels:
    app.kubernetes.io/name: example-web-python
  name: example-web-python-monitor
spec:
  endpoints:
    - interval: 30s
      port: http
      scheme: http
      path: /metrics
  selector:
    matchLabels:
      app: example-web-python