Monitoring Custom Metrics Using Cloud Native Cluster Monitoring¶
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.
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.
Log in to a Linux VM that can access to the Internet and run Docker commands.
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.
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.Run the login command copied in the previous step on the node. If the login is successful, the message "Login Succeeded" is displayed.
Run the following command to build an image named nginx. The image version is exporter.
docker build -t nginx:exporter .
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.
Use nginx:exporter to create a workload.
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"}