CCE provides the Cloud Native Cluster Monitoring add-on to monitor custom metrics using Prometheus.

The following procedure uses an Nginx application as an example to describe how to use Prometheus to monitor custom metrics:

Installing and Accessing Cloud Native Cluster Monitoring
CCE provides an add-on that integrates Prometheus functions. You can install it with several clicks.
Preparing an Application
Prepare an application image. The application must provide a metric monitoring API for Prometheus to collect data, and the monitoring data must comply with the Prometheus specifications.
Monitoring Custom Metrics
Use the application image to deploy a workload in a cluster. Custom metrics will be automatically reported to Prometheus.

Use one of the following methods to monitor custom metrics:

Constraints

To use Prometheus to monitor custom metrics, the application needs to provide a metric monitoring API. For details, see Prometheus Monitoring Data Collection.
Currently, metrics in the kube-system and monitoring namespaces cannot be collected when pod and service annotations are used. To collect metrics in the two namespaces, use PodMonitor and ServiceMonitor.
The nginx/nginx-prometheus-exporter:0.9.0 image is pulled for the Nginx application. You need to add an EIP for the node where the application is deployed or upload the image to SWR to prevent application deployment failures.

Prometheus Monitoring Data Collection

Prometheus periodically calls the metric monitoring API (/metrics by default) of an application to obtain monitoring data. The application needs to provide the metric monitoring API for Prometheus to call, and the monitoring data must meet the following specifications of Prometheus:

# TYPE nginx_connections_active gauge
nginx_connections_active 2
# TYPE nginx_connections_reading gauge
nginx_connections_reading 0

Prometheus provides clients in various languages. For details about the clients, see Prometheus CLIENT LIBRARIES. For details about how to develop an exporter, see WRITING EXPORTERS. The Prometheus community provides various third-party exporters that can be directly used. For details, see EXPORTERS AND INTEGRATIONS.

Installing and Accessing Cloud Native Cluster Monitoring

Log in to the CCE console and click the cluster name to access the cluster console.
In the navigation pane on the left, choose Add-ons. On the displayed page, locate the Cloud Native Cluster Monitoring add-on and click Install.
When installing this add-on, pay attention to the following configurations. Configure other parameters as required. For details, see Cloud Native Cluster Monitoring.
- For 3.8.0 or later, enable custom metric collection.
- For 3.8.0 or earlier, do not enable custom metric collection.

After this add-on is installed, deploy workloads and Services. The Prometheus server will be deployed as a StatefulSet in the monitoring namespace.

You can create a public network LoadBalancer Service so that Prometheus can be accessed from external networks.

Log in to the CCE console and click the name of the cluster with Prometheus installed to access the cluster console. In the navigation pane on the left, choose Services & Ingresses.

Click Create from YAML in the upper right corner to create a public network LoadBalancer Service.

apiVersion: v1
kind: Service
metadata:
  name: prom-lb     # Service name, which is customizable.
  namespace: monitoring
  labels:
    app: prometheus
    component: server
  annotations:
    kubernetes.io/elb.id: 038ff***     # Replace 038ff*** with the ID of the public network load balancer in the VPC that the cluster belongs to.
spec:
  ports:
    - name: cce-service-0
      protocol: TCP
      port: 88             # Service port, which is customizable.
      targetPort: 9090     # Default Prometheus port. Retain the default value.
  selector:                # The label selector can be adjusted based on the label of a Prometheus server instance.
    app.kubernetes.io/name: prometheus
    prometheus: server
  type: LoadBalancer

After the Service is created, enter Public IP address of the load balancer:Service port in the address box of the browser to access Prometheus.

Preparing an Application

User-developed applications must provide a metric monitoring API, and the monitoring data must comply with the Prometheus specifications. For details, see Prometheus Monitoring Data Collection.

This section uses Nginx as an example to describe how to collect monitoring data. There is a module named ngx_http_stub_status_module in Nginx, which provides basic monitoring functions. You can configure the nginx.conf file to provide an interface for external systems to access Nginx monitoring data.

Create an nginx.conf file. Add the server configuration under http to enable Nginx to provide an interface for the external systems to access the monitoring data.

user  nginx;
worker_processes  auto;

error_log  /var/log/nginx/error.log warn;
pid        /var/run/nginx.pid;

events {
    worker_connections  1024;
}

http {
    include       /etc/nginx/mime.types;
    default_type  application/octet-stream;
    log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
                      '$status $body_bytes_sent "$http_referer" '
                      '"$http_user_agent" "$http_x_forwarded_for"';

    access_log  /var/log/nginx/access.log  main;
    sendfile        on;
    #tcp_nopush     on;
    keepalive_timeout  65;
    #gzip  on;
    include /etc/nginx/conf.d/*.conf;

    server {
      listen 8080;
      server_name  localhost;
      location /stub_status {
         stub_status on;
         access_log off;
      }
    }
}

Use this configuration to create an image and a Dockerfile file.

vi Dockerfile

The content of Dockerfile is as follows:

FROM nginx:1.21.5-alpine
ADD nginx.conf /etc/nginx/nginx.conf
EXPOSE 80
CMD ["nginx", "-g", "daemon off;"]

Use this Dockerfile to build an image and upload it to SWR. The image name is nginx:exporter.
1. In the navigation pane, choose My Images. In the upper right corner, click Upload Through Client. On the displayed dialog box, click Generate a temporary login command and click to copy the command.
2. Run the login command copied in the previous step on the node. If the login is successful, the message "Login Succeeded" is displayed.
3. Run the following command to build an image named nginx. The image version is exporter.
```
docker build -t nginx:exporter .
```
4. Tag the image and upload it to the image repository. Change the image repository address and organization name based on your requirements.
```
docker tag nginx:exporter {swr-address}/{group}/nginx:exporter
docker push {swr-address}/{group}/nginx:exporter
```
View application metrics.
1. Use nginx:exporter to create a workload.
2. Access the container and use http://<ip_address>:8080/stub_status to obtain nginx monitoring data. <ip_address> indicates the IP address of the container. Information similar to the following is displayed.
```
# curl http://127.0.0.1:8080/stub_status
Active connections: 3 
server accepts handled requests
 146269 146269 212 
Reading: 0 Writing: 1 Waiting: 2
```

Method 1: Configuring Pod Annotations for Monitoring Custom Metrics

When the annotation settings of pods comply with the Prometheus data collection rules, Prometheus automatically collects the metrics exposed by the pods.

The format of the monitoring data provided by nginx:exporter does not meet the requirements of Prometheus. Convert the data format to the format required by Prometheus. To convert the format of Nginx metrics, use nginx-prometheus-exporter. Deploy nginx:exporter and nginx-prometheus-exporter in the same pod and add the following annotations during deployment. Then Prometheus can automatically collect metrics.

kind: Deployment
apiVersion: apps/v1
metadata:
  name: nginx-exporter
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx-exporter
  template:
    metadata:
      labels:
        app: nginx-exporter
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "9113"
        prometheus.io/path: "/metrics"
        prometheus.io/scheme: "http"
    spec:
      containers:
        - name: container-0
          image: 'nginx:exporter'      # Replace it with the address of the image you uploaded to SWR.
          resources:
            limits:
              cpu: 250m
              memory: 512Mi
            requests:
              cpu: 250m
              memory: 512Mi
        - name: container-1
          image: 'nginx/nginx-prometheus-exporter:0.9.0'
          command:
            - nginx-prometheus-exporter
          args:
            - '-nginx.scrape-uri=http://127.0.0.1:8080/stub_status'
      imagePullSecrets:
        - name: default-secret

Where,

prometheus.io/scrape indicates whether to enable Prometheus to collect pod monitoring data. The value is true.
prometheus.io/port indicates the port for collecting monitoring data, which varies depending on the application. In this example, the port is 9113.
prometheus.io/path indicates the URL of the API for collecting monitoring data. If this parameter is not set, the default value /metrics is used.
prometheus.io/scheme: protocol used for data collection. The value can be http or https.

After the application is successfully deployed, access Prometheus to query custom metrics by job name.

The custom metrics related to Nginx can be obtained. In the following, the job name indicates that the metrics are reported based on the pod configuration.

nginx_connections_accepted{cluster="2048c170-8359-11ee-9527-0255ac1000cf", cluster_category="CCE", cluster_name="cce-test", container="container-0", instance="10.0.0.46:9113", job="monitoring/kubernetes-pods", kubernetes_namespace="default", kubernetes_pod="nginx-exporter-77bf4d4948-zsb59", namespace="default", pod="nginx-exporter-77bf4d4948-zsb59", prometheus="monitoring/server"}

Method 2: Configuring Service Annotations for Monitoring Custom Metrics

When the annotation settings of Services comply with the Prometheus data collection rules, Prometheus automatically collects the metrics exposed by the Services.

You can use Service annotations in the same way as pod annotations. However, their application scenarios are different. Pod annotations focus on pod resource usage metrics while Service annotations focus on metrics such as requests for a Service.

The following is an example configuration:

kind: Deployment
apiVersion: apps/v1
metadata:
  name: nginx-test
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx-test
  template:
    metadata:
      labels:
        app: nginx-test
    spec:
      containers:
        - name: container-0
          image: 'nginx:exporter'      # Replace it with the address of the image you uploaded to SWR.
          resources:
            limits:
              cpu: 250m
              memory: 512Mi
            requests:
              cpu: 250m
              memory: 512Mi
        - name: container-1
          image: 'nginx/nginx-prometheus-exporter:0.9.0'
          command:
            - nginx-prometheus-exporter
          args:
            - '-nginx.scrape-uri=http://127.0.0.1:8080/stub_status'
      imagePullSecrets:
        - name: default-secret

The following is an example Service configuration:

apiVersion: v1
kind: Service
metadata:
  name: nginx-test
  labels:
    app: nginx-test
  namespace: default
  annotations: 
    prometheus.io/scrape: "true"  # Value true indicates that service discovery is enabled.
    prometheus.io/port: "9113"  # Set it to the port on which metrics are exposed.
    prometheus.io/path: "/metrics" # Enter the URI path under which metrics are exposed. Generally, the value is /metrics.
spec:
  selector:
    app: nginx-test
  externalTrafficPolicy: Cluster
  ports:
    - name: cce-service-0
      targetPort: 80
      nodePort: 0
      port: 8080
      protocol: TCP
    - name: cce-service-1
      protocol: TCP
      port: 9113
      targetPort: 9113
  type: NodePort

After the application is successfully deployed, access Prometheus to query custom metrics. In the following, the Service name indicates the metrics are reported based on the Service configuration.

nginx_connections_accepted{app="nginx-test", cluster="2048c170-8359-11ee-9527-0255ac1000cf", cluster_category="CCE", cluster_name="cce-test", instance="10.0.0.38:9113", job="nginx-test", kubernetes_namespace="default", kubernetes_service="nginx-test", namespace="default", pod="nginx-test-78cfb65889-gtv7z", prometheus="monitoring/server", service="nginx-test"}

Method 3: Configuring PodMonitor for Monitoring Custom Metrics

Cloud Native Cluster Monitoring allows you to configure metric collection tasks based on PodMonitor and ServiceMonitor. Prometheus Operator watches PodMonitor. The reload mechanism of Prometheus is used to trigger a hot update of the Prometheus collection tasks to the Prometheus instance.

To use CRDs defined by Prometheus Operator on GitHub, visit https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack/charts/crds/crds.

The following is an example configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-test2
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx-test2
  template:
    metadata:
      labels:
        app: nginx-test2
    spec:
      containers:
      - image: nginx:exporter     # Replace it with the address of the image you uploaded to SWR.
        name: container-0
        ports:
        - containerPort: 9113      # Port on which metrics are exposed.
          name: nginx-test2        # Application name used when PodMonitor is configured.
          protocol: TCP
        resources:
          limits:
            cpu: 250m
            memory: 300Mi
          requests:
            cpu: 100m
            memory: 100Mi
      - name: container-1
        image: 'nginx/nginx-prometheus-exporter:0.9.0'
        command:
          - nginx-prometheus-exporter
        args:
          - '-nginx.scrape-uri=http://127.0.0.1:8080/stub_status'
      imagePullSecrets:
        - name: default-secret

The following is an example PodMonitor configuration:

apiVersion: monitoring.coreos.com/v1
kind: PodMonitor
metadata:
  name: podmonitor-nginx   # PodMonitor name
  namespace: monitoring    # Namespace that PodMonitor belongs to. monitoring is recommended.
spec:
  namespaceSelector:       # An selector matching the namespace where the workload is located
    matchNames:
    - default              # Namespace that the workload belongs to
  jobLabel: podmonitor-nginx
  podMetricsEndpoints:
  - interval: 15s 
    path: /metrics            # Path under which metrics are exposed by the workload
    port: nginx-test2         # Port on which metrics are exposed by the workload
    tlsConfig:
      insecureSkipVerify: true
  selector:  
    matchLabels:
      app: nginx-test2   # Label carried by the pod, which can be selected by the selector

After the application is successfully deployed, access Prometheus to query custom metrics. In the following, the job name indicates the metrics are reported based on the PodMonitor configuration.

nginx_connections_accepted{cluster="2048c170-8359-11ee-9527-0255ac1000cf", cluster_category="CCE", cluster_name="cce-test", container="container-0", endpoint="nginx-test2", instance="10.0.0.44:9113", job="monitoring/podmonitor-nginx", namespace="default", pod="nginx-test2-746b7f8fdd-krzfp", prometheus="monitoring/server"}

Method 4: Configuring ServiceMonitor for Monitoring Custom Metrics

Cloud Native Cluster Monitoring allows you to configure metric collection tasks based on PodMonitor and ServiceMonitor. Prometheus Operator watches ServiceMonitor. The reload mechanism of Prometheus is used to trigger a hot update of the Prometheus collection tasks to the Prometheus instance.

To use CRDs defined by Prometheus Operator on GitHub, visit https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack/charts/crds/crds.

The following is an example configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-test3
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx-test3
  template:
    metadata:
      labels:
        app: nginx-test3
    spec:
      containers:
      - image: nginx:exporter        # Replace it with the address of the image you uploaded to SWR.
        name: container-0
        resources:
          limits:
            cpu: 250m
            memory: 300Mi
          requests:
            cpu: 100m
            memory: 100Mi
      - name: container-1
        image: 'nginx/nginx-prometheus-exporter:0.9.0'
        command:
          - nginx-prometheus-exporter
        args:
          - '-nginx.scrape-uri=http://127.0.0.1:8080/stub_status'
      imagePullSecrets:
        - name: default-secret

The following is an example Service configuration:

apiVersion: v1
kind: Service
metadata:
  name: nginx-test3
  labels:
    app: nginx-test3
  namespace: default
spec:
  selector:
    app: nginx-test3
  externalTrafficPolicy: Cluster
  ports:
    - name: cce-service-0
      targetPort: 80
      nodePort: 0
      port: 8080
      protocol: TCP
    - name: servicemonitor-ports
      protocol: TCP
      port: 9113
      targetPort: 9113
  type: NodePort

The following is an example ServiceMonitor configuration:

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: servicemonitor-nginx
  namespace: monitoring
spec:
  # Configure the name of the port on which metrics are exposed.
  endpoints:
  - path: /metrics
    port: servicemonitor-ports
  jobLabel: servicemonitor-nginx
  # Application scope of a collection task. If this parameter is not set, the default value default is used.
  namespaceSelector:
    matchNames:
    - default
  selector:
    matchLabels:
      app: nginx-test3

After the application is successfully deployed, access Prometheus to query custom metrics. In the following, the endpoint name indicates the metrics are reported based on the ServiceMonitor configuration.

nginx_connections_accepted{cluster="2048c170-8359-11ee-9527-0255ac1000cf", cluster_category="CCE", cluster_name="cce-test", endpoint="servicemonitor-ports", instance="10.0.0.47:9113", job="nginx-test3", namespace="default", pod="nginx-test3-6f8bccd9-f27hv", prometheus="monitoring/server", service="nginx-test3"}

Monitoring Custom Metrics Using Cloud Native Cluster Monitoring

Constraints

Prometheus Monitoring Data Collection

Installing and Accessing Cloud Native Cluster Monitoring

Preparing an Application

Method 1: Configuring Pod Annotations for Monitoring Custom Metrics

Method 2: Configuring Service Annotations for Monitoring Custom Metrics

Method 3: Configuring PodMonitor for Monitoring Custom Metrics

Method 4: Configuring ServiceMonitor for Monitoring Custom Metrics